glmnet/0000755000176200001440000000000013574220223011540 5ustar liggesusersglmnet/NAMESPACE0000644000176200001440000000421713573535152012774 0ustar liggesusers# Generated by roxygen2: do not edit by hand S3method(buildPredmat,array) S3method(buildPredmat,coxnetlist) S3method(buildPredmat,default) S3method(buildPredmat,lognetlist) S3method(buildPredmat,mrelnetlist) S3method(buildPredmat,multnetlist) S3method(coef,cv.glmnet) S3method(coef,cv.relaxed) S3method(coef,glmnet) S3method(coef,relaxed) S3method(deviance,glmnet) S3method(family,cv.glmnet) S3method(family,glmnet) S3method(family,relaxed) S3method(plot,cv.glmnet) S3method(plot,cv.relaxed) S3method(plot,glmnet) S3method(plot,mrelnet) S3method(plot,multnet) S3method(plot,relaxed) S3method(predict,coxnet) S3method(predict,cv.glmnet) S3method(predict,cv.relaxed) S3method(predict,elnet) S3method(predict,fishnet) S3method(predict,glmnet) S3method(predict,lognet) S3method(predict,mrelnet) S3method(predict,multnet) S3method(predict,relaxed) S3method(print,bigGlm) S3method(print,confusion.table) S3method(print,cv.glmnet) S3method(print,cv.relaxed) S3method(print,glmnet) S3method(print,relaxed) export(Cindex) export(assess.glmnet) export(bigGlm) export(buildPredmat) export(coef.glmnet) export(coef.relaxed) export(confusion.glmnet) export(coxgrad) export(coxnet.deviance) export(cv.glmnet) export(glmnet) export(glmnet.control) export(glmnet.measures) export(makeX) export(na.replace) export(na_sparse_fix) export(predict.glmnet) export(predict.relaxed) export(prepareX) export(print.cv.glmnet) export(relax.glmnet) export(rmult) export(roc.glmnet) import(Matrix) import(foreach) import(methods) importFrom(Matrix,sparse.model.matrix) importFrom(grDevices,rainbow) importFrom(graphics,abline) importFrom(graphics,axis) importFrom(graphics,matplot) importFrom(graphics,par) importFrom(graphics,plot) importFrom(graphics,points) importFrom(graphics,segments) importFrom(graphics,text) importFrom(shape,colorlegend) importFrom(stats,approx) importFrom(stats,coef) importFrom(stats,contrasts) importFrom(stats,family) importFrom(stats,median) importFrom(stats,model.matrix) importFrom(stats,na.pass) importFrom(stats,predict) importFrom(stats,rmultinom) importFrom(stats,rnorm) importFrom(stats,runif) importFrom(stats,weighted.mean) importFrom(utils,packageDescription) useDynLib(glmnet) glmnet/README.md0000644000176200001440000000625713555633674013052 0ustar liggesusers # Lasso and Elastic-Net Regularized Generalized Linear Models [![CRAN\_Status\_Badge](https://www.r-pkg.org/badges/version/glmnet)](https://cran.r-project.org/package=glmnet)[![](https://cranlogs.r-pkg.org/badges/glmnet)](https://CRAN.R-project.org/package=glmnet) We provide extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression (gaussian), multi-task gaussian, logistic and multinomial regression models (grouped or not), Poisson regression and the Cox model. The algorithm uses cyclical coordinate descent in a path-wise fashion. Details may be found in Friedman, Hastie, and Tibshirani ([2010](#ref-glmnet)), Simon et al. ([2011](#ref-coxnet)), Tibshirani et al. ([2012](#ref-strongrules)), Simon, Friedman, and Hastie ([2013](#ref-block)). Version 3.0 is a major release with several new features, including: - Relaxed fitting to allow models in the path to be refit without regularization. CV will select from these, or from specified mixtures of the relaxed fit and the regular fit; - Progress bar to monitor computation; - Assessment functions for displaying performance of models on test data. These include all the measures available via `cv.glmnet`, as well as confusion matrices and ROC plots for classification models; - print methods for CV output; - Functions for building the `x` input matrix for `glmnet` that allow for *one-hot-encoding* of factor variables, appropriate treatment of missing values, and an option to create a sparse matrix if appropriate. - A function for fitting unpenalized a single version of any of the GLMs of `glmnet`. ## References
Friedman, Jerome, Trevor Hastie, and Rob Tibshirani. 2010. “Regularization Paths for Generalized Linear Models via Coordinate Descent.” *Journal of Statistical Software, Articles* 33 (1): 1–22. .
Simon, Noah, Jerome Friedman, and Trevor Hastie. 2013. “A Blockwise Descent Algorithm for Group-Penalized Multiresponse and Multinomial Regression.”
Simon, Noah, Jerome Friedman, Trevor Hastie, and Rob Tibshirani. 2011. “Regularization Paths for Cox’s Proportional Hazards Model via Coordinate Descent.” *Journal of Statistical Software, Articles* 39 (5): 1–13. .
Tibshirani, Robert, Jacob Bien, Jerome Friedman, Trevor Hastie, Noah Simon, Jonathan Taylor, and Ryan J. Tibshirani. 2012. “Strong Rules for Discarding Predictors in Lasso-Type Problems.” *Journal of the Royal Statistical Society: Series B (Statistical Methodology)* 74 (2): 245–66. .
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A,aIrqWh,% z߃ /zXEpagz8 X%u&7Ztzs[ ]1[co:9R,fU\ay|(t =`ujB_@x+8 ~Vܔ1﹞~!VӰz@EIwh1{4YJ9UA`y7DW+Ԅxp\rl4)J&^[j)bWDOĝ^ݽvD7ًj qZfec˜1D%#LD%[?0TMqcf#ngu!0$&jj(W\BQڰ7dXks{) Lo=W}rǠT1aPQ&'u)/]V®#R2qϟ@"s'eoy awàWm qۋ"KΥ +m7$l]޴"p:%9 i:;C4&oZDfKnOI([j7H9a.w$9"!H偙hRjޗq W۟K|tM]n=]YD4#?4GS>e|b@yzZ\iǾMUf+zpwҿ&ؤ=f Ȫu5KY*_#YR/9y.H歔.uܜk}??Iؑ—t[JFB.fׇ mno{4<ۂ}qqʉ.DЗf^zdojm*kސD~{w޶D}EDfPśݴ%߉;0,family="binomial") print(fit2) fit2p=bigGlm(x,y>0,family="binomial",path=TRUE) print(fit2p) } \seealso{ \code{print}, \code{predict}, and \code{coef} methods. } \author{ Trevor Hastie\cr Maintainer: Trevor Hastie \email{hastie@stanford.edu} } \keyword{models} \keyword{regression} glmnet/man/plot.glmnet.Rd0000644000176200001440000000425013553366407015062 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot.glmnet.R, R/plot.mrelnet.R, % R/plot.multnet.R, R/plot.relaxed.R \name{plot.glmnet} \alias{plot.glmnet} \alias{plot.multnet} \alias{plot.mrelnet} \alias{plot.relaxed} \title{plot coefficients from a "glmnet" object} \usage{ \method{plot}{glmnet}(x, xvar = c("norm", "lambda", "dev"), label = FALSE, ...) \method{plot}{mrelnet}(x, xvar = c("norm", "lambda", "dev"), label = FALSE, type.coef = c("coef", "2norm"), ...) \method{plot}{multnet}(x, xvar = c("norm", "lambda", "dev"), label = FALSE, type.coef = c("coef", "2norm"), ...) \method{plot}{relaxed}(x, xvar = c("lambda", "dev"), label = FALSE, gamma = 1, ...) } \arguments{ \item{x}{fitted \code{"glmnet"} model} \item{xvar}{What is on the X-axis. \code{"norm"} plots against the L1-norm of the coefficients, \code{"lambda"} against the log-lambda sequence, and \code{"dev"} against the percent deviance explained.} \item{label}{If \code{TRUE}, label the curves with variable sequence numbers.} \item{\dots}{Other graphical parameters to plot} \item{type.coef}{If \code{type.coef="2norm"} then a single curve per variable, else if \code{type.coef="coef"}, a coefficient plot per response} \item{gamma}{Value of the mixing parameter for a "relaxed" fit} } \description{ Produces a coefficient profile plot of the coefficient paths for a fitted \code{"glmnet"} object. } \details{ A coefficient profile plot is produced. If \code{x} is a multinomial model, a coefficient plot is produced for each class. } \examples{ x=matrix(rnorm(100*20),100,20) y=rnorm(100) g2=sample(1:2,100,replace=TRUE) g4=sample(1:4,100,replace=TRUE) fit1=glmnet(x,y) plot(fit1) plot(fit1,xvar="lambda",label=TRUE) fit3=glmnet(x,g4,family="multinomial") plot(fit3,pch=19) } \references{ Friedman, J., Hastie, T. and Tibshirani, R. (2008) \emph{Regularization Paths for Generalized Linear Models via Coordinate Descent} } \seealso{ \code{glmnet}, and \code{print}, \code{predict} and \code{coef} methods. } \author{ Jerome Friedman, Trevor Hastie and Rob Tibshirani\cr Maintainer: Trevor Hastie \href{mailto:hastie@stanford.edu}{hastie@stanford.edu} } \keyword{models} \keyword{regression} glmnet/man/print.glmnet.Rd0000644000176200001440000000263613553366407015246 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/print.glmnet.R \name{print.glmnet} \alias{print.glmnet} \alias{print.relaxed} \alias{print.bigGlm} \title{print a glmnet object} \usage{ \method{print}{glmnet}(x, digits = max(3, getOption("digits") - 3), ...) } \arguments{ \item{x}{fitted glmnet object} \item{digits}{significant digits in printout} \item{\dots}{additional print arguments} } \value{ The matrix above is silently returned } \description{ Print a summary of the glmnet path at each step along the path. } \details{ The call that produced the object \code{x} is printed, followed by a three-column matrix with columns \code{Df}, \code{\%Dev} and \code{Lambda}. The \code{Df} column is the number of nonzero coefficients (Df is a reasonable name only for lasso fits). \code{\%Dev} is the percent deviance explained (relative to the null deviance). In the case of a 'relaxed' fit, an additional column is inserted, \code{\%Dev R} which gives the percent deviance explained by the relaxed model. For a "bigGlm" model, a simpler summary is printed. } \examples{ x = matrix(rnorm(100 * 20), 100, 20) y = rnorm(100) fit1 = glmnet(x, y) print(fit1) } \references{ Friedman, J., Hastie, T. and Tibshirani, R. (2008). Regularization Paths for Generalized Linear Models via Coordinate Descent } \seealso{ \code{glmnet}, \code{predict} and \code{coef} methods. } \keyword{models} \keyword{regression} glmnet/man/glmnet.control.Rd0000644000176200001440000000434013553366407015564 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/glmnet.control.R \name{glmnet.control} \alias{glmnet.control} \title{internal glmnet parameters} \usage{ glmnet.control(fdev = 1e-05, devmax = 0.999, eps = 1e-06, big = 9.9e+35, mnlam = 5, pmin = 1e-09, exmx = 250, prec = 1e-10, mxit = 100, itrace = 0, factory = FALSE) } \arguments{ \item{fdev}{minimum fractional change in deviance for stopping path; factory default = 1.0e-5} \item{devmax}{maximum fraction of explained deviance for stopping path; factory default = 0.999} \item{eps}{minimum value of lambda.min.ratio (see glmnet); factory default= 1.0e-6} \item{big}{large floating point number; factory default = 9.9e35. Inf in definition of upper.limit is set to big} \item{mnlam}{minimum number of path points (lambda values) allowed; factory default = 5} \item{pmin}{minimum probability for any class. factory default = 1.0e-9. Note that this implies a pmax of 1-pmin.} \item{exmx}{maximum allowed exponent. factory default = 250.0} \item{prec}{convergence threshold for multi response bounds adjustment solution. factory default = 1.0e-10} \item{mxit}{maximum iterations for multiresponse bounds adjustment solution. factory default = 100} \item{itrace}{If 1 then progress bar is displayed when running \code{glmnet} and \code{cv.glmnet}. factory default = 0} \item{factory}{If \code{TRUE}, reset all the parameters to the factory default; default is \code{FALSE}} } \value{ A list with named elements as in the argument list } \description{ View and/or change the factory default parameters in glmnet } \details{ If called with no arguments, \code{glmnet.control()} returns a list with the current settings of these parameters. Any arguments included in the call sets those parameters to the new values, and then silently returns. The values set are persistent for the duration of the R session. } \examples{ glmnet.control(fdev = 0) #continue along path even though not much changes glmnet.control() # view current settings glmnet.control(factory = TRUE) # reset all the parameters to their default } \seealso{ \code{glmnet} } \author{ Jerome Friedman, Trevor Hastie\cr Maintainer: Trevor Hastie \email{hastie@stanford.edu} } \keyword{models} \keyword{regression} glmnet/man/predict.glmnet.Rd0000644000176200001440000001267113557672052015544 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/coef.glmnet.R, R/predict.glmnet.R, % R/predict.relaxed.R \name{coef.glmnet} \alias{coef.glmnet} \alias{predict.glmnet} \alias{coef.relaxed} \alias{predict.relaxed} \alias{predict.elnet} \alias{predict.lognet} \alias{predict.multnet} \alias{predict.mrelnet} \alias{predict.fishnet} \alias{predict.coxnet} \title{Extract coefficients from a glmnet object} \usage{ \method{coef}{glmnet}(object, s = NULL, exact = FALSE, ...) \method{predict}{glmnet}(object, newx, s = NULL, type = c("link", "response", "coefficients", "nonzero", "class"), exact = FALSE, newoffset, ...) \method{predict}{relaxed}(object, newx, s = NULL, gamma = 1, type = c("link", "response", "coefficients", "nonzero", "class"), exact = FALSE, newoffset, ...) } \arguments{ \item{object}{Fitted \code{"glmnet"} model object or a \code{"relaxed"} model (which inherits from class "glmnet").} \item{s}{Value(s) of the penalty parameter \code{lambda} at which predictions are required. Default is the entire sequence used to create the model.} \item{exact}{This argument is relevant only when predictions are made at values of \code{s} (lambda) \emph{different} from those used in the fitting of the original model. Not available for \code{"relaxed"} objects. If \code{exact=FALSE} (default), then the predict function uses linear interpolation to make predictions for values of \code{s} (lambda) that do not coincide with those used in the fitting algorithm. While this is often a good approximation, it can sometimes be a bit coarse. With \code{exact=TRUE}, these different values of \code{s} are merged (and sorted) with \code{object$lambda}, and the model is refit before predictions are made. In this case, it is required to supply the original data \code{x=} and \code{y=} as additional named arguments to \code{predict()} or \code{coef()}. The workhorse \code{predict.glmnet()} needs to \code{update} the model, and so needs the data used to create it. The same is true of \code{weights}, \code{offset}, \code{penalty.factor}, \code{lower.limits}, \code{upper.limits} if these were used in the original call. Failure to do so will result in an error.} \item{\dots}{This is the mechanism for passing arguments like \code{x=} when \code{exact=TRUE}; see\code{exact} argument.} \item{newx}{Matrix of new values for \code{x} at which predictions are to be made. Must be a matrix; can be sparse as in \code{Matrix} package. This argument is not used for \code{type=c("coefficients","nonzero")}} \item{type}{Type of prediction required. Type \code{"link"} gives the linear predictors for \code{"binomial"}, \code{"multinomial"}, \code{"poisson"} or \code{"cox"} models; for \code{"gaussian"} models it gives the fitted values. Type \code{"response"} gives the fitted probabilities for \code{"binomial"} or \code{"multinomial"}, fitted mean for \code{"poisson"} and the fitted relative-risk for \code{"cox"}; for \code{"gaussian"} type \code{"response"} is equivalent to type \code{"link"}. Type \code{"coefficients"} computes the coefficients at the requested values for \code{s}. Note that for \code{"binomial"} models, results are returned only for the class corresponding to the second level of the factor response. Type \code{"class"} applies only to \code{"binomial"} or \code{"multinomial"} models, and produces the class label corresponding to the maximum probability. Type \code{"nonzero"} returns a list of the indices of the nonzero coefficients for each value of \code{s}.} \item{newoffset}{If an offset is used in the fit, then one must be supplied for making predictions (except for \code{type="coefficients"} or \code{type="nonzero"})} \item{gamma}{Single value of \code{gamma} at which predictions are required, for "relaxed" objects.} } \value{ The object returned depends on type. } \description{ Similar to other predict methods, this functions predicts fitted values, logits, coefficients and more from a fitted \code{"glmnet"} object. } \details{ The shape of the objects returned are different for \code{"multinomial"} objects. This function actually calls \code{NextMethod()}, and the appropriate predict method is invoked for each of the three model types. \code{coef(...)} is equivalent to \code{predict(type="coefficients",...)} } \examples{ x=matrix(rnorm(100*20),100,20) y=rnorm(100) g2=sample(1:2,100,replace=TRUE) g4=sample(1:4,100,replace=TRUE) fit1=glmnet(x,y) predict(fit1,newx=x[1:5,],s=c(0.01,0.005)) predict(fit1,type="coef") fit2=glmnet(x,g2,family="binomial") predict(fit2,type="response",newx=x[2:5,]) predict(fit2,type="nonzero") fit3=glmnet(x,g4,family="multinomial") predict(fit3,newx=x[1:3,],type="response",s=0.01) } \references{ Friedman, J., Hastie, T. and Tibshirani, R. (2008) \emph{Regularization Paths for Generalized Linear Models via Coordinate Descent}, \url{https://web.stanford.edu/~hastie/Papers/glmnet.pdf}\cr \emph{Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010}\cr \url{https://www.jstatsoft.org/v33/i01/}\cr Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2011) \emph{Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5) 1-13}\cr \url{https://www.jstatsoft.org/v39/i05/} } \seealso{ \code{glmnet}, and \code{print}, and \code{coef} methods, and \code{cv.glmnet}. } \author{ Jerome Friedman, Trevor Hastie and Rob Tibshirani\cr Maintainer: Trevor Hastie \href{mailto:hastie@stanford.edu}{hastie@stanford.edu} } \keyword{models} \keyword{regression} glmnet/man/predict.cv.glmnet.Rd0000644000176200001440000000556313555633674016162 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/predict.cv.glmnet.R, R/predict.cv.relaxed.R \name{predict.cv.glmnet} \alias{predict.cv.glmnet} \alias{coef.cv.glmnet} \alias{coef.cv.relaxed} \alias{predict.cv.relaxed} \title{make predictions from a "cv.glmnet" object.} \usage{ \method{predict}{cv.glmnet}(object, newx, s = c("lambda.1se", "lambda.min"), ...) \method{predict}{cv.relaxed}(object, newx, s = c("lambda.1se", "lambda.min"), gamma = c("gamma.1se", "gamma.min"), ...) } \arguments{ \item{object}{Fitted \code{"cv.glmnet"} or \code{"cv.relaxed"} object.} \item{newx}{Matrix of new values for \code{x} at which predictions are to be made. Must be a matrix; can be sparse as in \code{Matrix} package. See documentation for \code{predict.glmnet}.} \item{s}{Value(s) of the penalty parameter \code{lambda} at which predictions are required. Default is the value \code{s="lambda.1se"} stored on the CV \code{object}. Alternatively \code{s="lambda.min"} can be used. If \code{s} is numeric, it is taken as the value(s) of \code{lambda} to be used. (For historical reasons we use the symbol 's' rather than 'lambda' to reference this parameter)} \item{\dots}{Not used. Other arguments to predict.} \item{gamma}{Value (single) of 'gamma' at which predictions are to be made} } \value{ The object returned depends on the \dots{} argument which is passed on to the \code{predict} method for \code{glmnet} objects. } \description{ This function makes predictions from a cross-validated glmnet model, using the stored \code{"glmnet.fit"} object, and the optimal value chosen for \code{lambda} (and \code{gamma} for a 'relaxed' fit. } \details{ This function makes it easier to use the results of cross-validation to make a prediction. } \examples{ x = matrix(rnorm(100 * 20), 100, 20) y = rnorm(100) cv.fit = cv.glmnet(x, y) predict(cv.fit, newx = x[1:5, ]) coef(cv.fit) coef(cv.fit, s = "lambda.min") predict(cv.fit, newx = x[1:5, ], s = c(0.001, 0.002)) cv.fitr = cv.glmnet(x, y, relax = TRUE) predict(cv.fit, newx = x[1:5, ]) coef(cv.fit) coef(cv.fit, s = "lambda.min", gamma = "gamma.min") predict(cv.fit, newx = x[1:5, ], s = c(0.001, 0.002), gamma = "gamma.min") } \references{ Friedman, J., Hastie, T. and Tibshirani, R. (2008) \emph{Regularization Paths for Generalized Linear Models via Coordinate Descent, Journal of Statistical Software, Vol. 33, Issue 1, Feb 2010}\cr \url{https://www.jstatsoft.org/v33/i01/} \url{https://arxiv.org/abs/1707.08692}\cr Hastie, T., Tibshirani, Robert, Tibshirani, Ryan (2019) \emph{Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso} } \seealso{ \code{glmnet}, and \code{print}, and \code{coef} methods, and \code{cv.glmnet}. } \author{ Jerome Friedman, Trevor Hastie and Rob Tibshirani\cr Maintainer: Trevor Hastie \href{mailto:hastie@stanford.edu}{hastie@stanford.edu} } \keyword{models} \keyword{regression} glmnet/man/glmnet.measures.Rd0000644000176200001440000000151613553366407015732 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/glmnet.measures.R \name{glmnet.measures} \alias{glmnet.measures} \title{Display the names of the measures used in CV for different "glmnet" families} \usage{ glmnet.measures(family = c("all", "gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian")) } \arguments{ \item{family}{If a "glmnet" family is supplied, a list of the names of measures available for that family are produced. Default is "all", in which case the names of measures for all families are produced.} } \description{ Produces a list of names of measures } \details{ Try it and see. A very simple function to provide information } \seealso{ \code{cv.glmnet} and \code{assess.glmnet}. } \author{ Trevor Hastie\cr Maintainer: Trevor Hastie \email{hastie@stanford.edu} } \keyword{models} glmnet/man/glmnet.Rd0000644000176200001440000004371313560704061014102 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/glmnet.R, R/relax.glmnet.R \name{glmnet} \alias{glmnet} \alias{relax.glmnet} \title{fit a GLM with lasso or elasticnet regularization} \usage{ glmnet(x, y, family = c("gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian"), weights, offset = NULL, alpha = 1, nlambda = 100, lambda.min.ratio = ifelse(nobs < nvars, 0.01, 1e-04), lambda = NULL, standardize = TRUE, intercept = TRUE, thresh = 1e-07, dfmax = nvars + 1, pmax = min(dfmax * 2 + 20, nvars), exclude, penalty.factor = rep(1, nvars), lower.limits = -Inf, upper.limits = Inf, maxit = 1e+05, type.gaussian = ifelse(nvars < 500, "covariance", "naive"), type.logistic = c("Newton", "modified.Newton"), standardize.response = FALSE, type.multinomial = c("ungrouped", "grouped"), relax = FALSE, trace.it = 0, ...) relax.glmnet(fit, x, ..., maxp = n - 3, path = FALSE, check.args = TRUE) } \arguments{ \item{x}{input matrix, of dimension nobs x nvars; each row is an observation vector. Can be in sparse matrix format (inherit from class \code{"sparseMatrix"} as in package \code{Matrix}; not yet available for \code{family="cox"})} \item{y}{response variable. Quantitative for \code{family="gaussian"}, or \code{family="poisson"} (non-negative counts). For \code{family="binomial"} should be either a factor with two levels, or a two-column matrix of counts or proportions (the second column is treated as the target class; for a factor, the last level in alphabetical order is the target class). For \code{family="multinomial"}, can be a \code{nc>=2} level factor, or a matrix with \code{nc} columns of counts or proportions. For either \code{"binomial"} or \code{"multinomial"}, if \code{y} is presented as a vector, it will be coerced into a factor. For \code{family="cox"}, \code{y} should be a two-column matrix with columns named 'time' and 'status'. The latter is a binary variable, with '1' indicating death, and '0' indicating right censored. The function \code{Surv()} in package \pkg{survival} produces such a matrix. For \code{family="mgaussian"}, \code{y} is a matrix of quantitative responses.} \item{family}{Response type (see above)} \item{weights}{observation weights. Can be total counts if responses are proportion matrices. Default is 1 for each observation} \item{offset}{A vector of length \code{nobs} that is included in the linear predictor (a \code{nobs x nc} matrix for the \code{"multinomial"} family). Useful for the \code{"poisson"} family (e.g. log of exposure time), or for refining a model by starting at a current fit. Default is \code{NULL}. If supplied, then values must also be supplied to the \code{predict} function.} \item{alpha}{The elasticnet mixing parameter, with \eqn{0\le\alpha\le 1}. The penalty is defined as \deqn{(1-\alpha)/2||\beta||_2^2+\alpha||\beta||_1.} \code{alpha=1} is the lasso penalty, and \code{alpha=0} the ridge penalty.} \item{nlambda}{The number of \code{lambda} values - default is 100.} \item{lambda.min.ratio}{Smallest value for \code{lambda}, as a fraction of \code{lambda.max}, the (data derived) entry value (i.e. the smallest value for which all coefficients are zero). The default depends on the sample size \code{nobs} relative to the number of variables \code{nvars}. If \code{nobs > nvars}, the default is \code{0.0001}, close to zero. If \code{nobs < nvars}, the default is \code{0.01}. A very small value of \code{lambda.min.ratio} will lead to a saturated fit in the \code{nobs < nvars} case. This is undefined for \code{"binomial"} and \code{"multinomial"} models, and \code{glmnet} will exit gracefully when the percentage deviance explained is almost 1.} \item{lambda}{A user supplied \code{lambda} sequence. Typical usage is to have the program compute its own \code{lambda} sequence based on \code{nlambda} and \code{lambda.min.ratio}. Supplying a value of \code{lambda} overrides this. WARNING: use with care. Avoid supplying a single value for \code{lambda} (for predictions after CV use \code{predict()} instead). Supply instead a decreasing sequence of \code{lambda} values. \code{glmnet} relies on its warms starts for speed, and its often faster to fit a whole path than compute a single fit.} \item{standardize}{Logical flag for x variable standardization, prior to fitting the model sequence. The coefficients are always returned on the original scale. Default is \code{standardize=TRUE}. If variables are in the same units already, you might not wish to standardize. See details below for y standardization with \code{family="gaussian"}.} \item{intercept}{Should intercept(s) be fitted (default=TRUE) or set to zero (FALSE)} \item{thresh}{Convergence threshold for coordinate descent. Each inner coordinate-descent loop continues until the maximum change in the objective after any coefficient update is less than \code{thresh} times the null deviance. Defaults value is \code{1E-7}.} \item{dfmax}{Limit the maximum number of variables in the model. Useful for very large \code{nvars}, if a partial path is desired.} \item{pmax}{Limit the maximum number of variables ever to be nonzero} \item{exclude}{Indices of variables to be excluded from the model. Default is none. Equivalent to an infinite penalty factor (next item).} \item{penalty.factor}{Separate penalty factors can be applied to each coefficient. This is a number that multiplies \code{lambda} to allow differential shrinkage. Can be 0 for some variables, which implies no shrinkage, and that variable is always included in the model. Default is 1 for all variables (and implicitly infinity for variables listed in \code{exclude}). Note: the penalty factors are internally rescaled to sum to nvars, and the lambda sequence will reflect this change.} \item{lower.limits}{Vector of lower limits for each coefficient; default \code{-Inf}. Each of these must be non-positive. Can be presented as a single value (which will then be replicated), else a vector of length \code{nvars}} \item{upper.limits}{Vector of upper limits for each coefficient; default \code{Inf}. See \code{lower.limits}} \item{maxit}{Maximum number of passes over the data for all lambda values; default is 10^5.} \item{type.gaussian}{Two algorithm types are supported for (only) \code{family="gaussian"}. The default when \code{nvar<500} is \code{type.gaussian="covariance"}, and saves all inner-products ever computed. This can be much faster than \code{type.gaussian="naive"}, which loops through \code{nobs} every time an inner-product is computed. The latter can be far more efficient for \code{nvar >> nobs} situations, or when \code{nvar > 500}.} \item{type.logistic}{If \code{"Newton"} then the exact hessian is used (default), while \code{"modified.Newton"} uses an upper-bound on the hessian, and can be faster.} \item{standardize.response}{This is for the \code{family="mgaussian"} family, and allows the user to standardize the response variables} \item{type.multinomial}{If \code{"grouped"} then a grouped lasso penalty is used on the multinomial coefficients for a variable. This ensures they are all in our out together. The default is \code{"ungrouped"}} \item{relax}{If \code{TRUE} then for each \emph{active set} in the path of solutions, the model is refit without any regularization. See \code{details} for more information. This argument is new, and users may experience convergence issues with small datasets, especially with non-gaussian families. Limiting the value of 'maxp' can alleviate these issues in some cases.} \item{trace.it}{If \code{trace.it=1}, then a progress bar is displayed; useful for big models that take a long time to fit.} \item{...}{Additional argument used in \code{relax.glmnet}. These include some of the original arguments to 'glmnet', and each must be named if used.} \item{fit}{For \code{relax.glmnet} a fitted 'glmnet' object} \item{maxp}{a limit on how many relaxed coefficients are allowed. Default is 'n-3', where 'n' is the sample size. This may not be sufficient for non-gaussian familes, in which case users should supply a smaller value. This argument can be supplied directly to 'glmnet'.} \item{path}{Since \code{glmnet} does not do stepsize optimization, the Newton algorithm can get stuck and not converge, especially with relaxed fits. With \code{path=TRUE}, each relaxed fit on a particular set of variables is computed pathwise using the original sequence of lambda values (with a zero attached to the end). Not needed for Gaussian models, and should not be used unless needed, since will lead to longer compute times. Default is \code{path=FALSE}. appropriate subset of variables} \item{check.args}{Should \code{relax.glmnet} make sure that all the data dependent arguments used in creating 'fit' have been resupplied. Default is 'TRUE'.} } \value{ An object with S3 class \code{"glmnet","*" }, where \code{"*"} is \code{"elnet"}, \code{"lognet"}, \code{"multnet"}, \code{"fishnet"} (poisson), \code{"coxnet"} or \code{"mrelnet"} for the various types of models. If the model was created with \code{relax=TRUE} then this class has a prefix class of \code{"relaxed"}. \item{call}{the call that produced this object} \item{a0}{Intercept sequence of length \code{length(lambda)}} \item{beta}{For \code{"elnet"}, \code{"lognet"}, \code{"fishnet"} and \code{"coxnet"} models, a \code{nvars x length(lambda)} matrix of coefficients, stored in sparse column format (\code{"CsparseMatrix"}). For \code{"multnet"} and \code{"mgaussian"}, a list of \code{nc} such matrices, one for each class.} \item{lambda}{The actual sequence of \code{lambda} values used. When \code{alpha=0}, the largest lambda reported does not quite give the zero coefficients reported (\code{lambda=inf} would in principle). Instead, the largest \code{lambda} for \code{alpha=0.001} is used, and the sequence of \code{lambda} values is derived from this.} \item{dev.ratio}{The fraction of (null) deviance explained (for \code{"elnet"}, this is the R-square). The deviance calculations incorporate weights if present in the model. The deviance is defined to be 2*(loglike_sat - loglike), where loglike_sat is the log-likelihood for the saturated model (a model with a free parameter per observation). Hence dev.ratio=1-dev/nulldev.} \item{nulldev}{Null deviance (per observation). This is defined to be 2*(loglike_sat -loglike(Null)); The NULL model refers to the intercept model, except for the Cox, where it is the 0 model.} \item{df}{The number of nonzero coefficients for each value of \code{lambda}. For \code{"multnet"}, this is the number of variables with a nonzero coefficient for \emph{any} class.} \item{dfmat}{For \code{"multnet"} and \code{"mrelnet"} only. A matrix consisting of the number of nonzero coefficients per class} \item{dim}{dimension of coefficient matrix (ices)} \item{nobs}{number of observations} \item{npasses}{total passes over the data summed over all lambda values} \item{offset}{a logical variable indicating whether an offset was included in the model} \item{jerr}{error flag, for warnings and errors (largely for internal debugging).} \item{relaxed}{If \code{relax=TRUE}, this additional item is another glmnet object with different values for \code{beta} and \code{dev.ratio}} } \description{ Fit a generalized linear model via penalized maximum likelihood. The regularization path is computed for the lasso or elasticnet penalty at a grid of values for the regularization parameter lambda. Can deal with all shapes of data, including very large sparse data matrices. Fits linear, logistic and multinomial, poisson, and Cox regression models. } \details{ The sequence of models implied by \code{lambda} is fit by coordinate descent. For \code{family="gaussian"} this is the lasso sequence if \code{alpha=1}, else it is the elasticnet sequence. For the other families, this is a lasso or elasticnet regularization path for fitting the generalized linear regression paths, by maximizing the appropriate penalized log-likelihood (partial likelihood for the "cox" model). Sometimes the sequence is truncated before \code{nlambda} values of \code{lambda} have been used, because of instabilities in the inverse link functions near a saturated fit. \code{glmnet(...,family="binomial")} fits a traditional logistic regression model for the log-odds. \code{glmnet(...,family="multinomial")} fits a symmetric multinomial model, where each class is represented by a linear model (on the log-scale). The penalties take care of redundancies. A two-class \code{"multinomial"} model will produce the same fit as the corresponding \code{"binomial"} model, except the pair of coefficient matrices will be equal in magnitude and opposite in sign, and half the \code{"binomial"} values. Note that the objective function for \code{"gaussian"} is \deqn{1/2 RSS/nobs + \lambda*penalty,} and for the other models it is \deqn{-loglik/nobs + \lambda*penalty.} Note also that for \code{"gaussian"}, \code{glmnet} standardizes y to have unit variance (using 1/n rather than 1/(n-1) formula) before computing its lambda sequence (and then unstandardizes the resulting coefficients); if you wish to reproduce/compare results with other software, best to supply a standardized y. The coefficients for any predictor variables with zero variance are set to zero for all values of lambda. The latest two features in glmnet are the \code{family="mgaussian"} family and the \code{type.multinomial="grouped"} option for multinomial fitting. The former allows a multi-response gaussian model to be fit, using a "group -lasso" penalty on the coefficients for each variable. Tying the responses together like this is called "multi-task" learning in some domains. The grouped multinomial allows the same penalty for the \code{family="multinomial"} model, which is also multi-responsed. For both of these the penalty on the coefficient vector for variable j is \deqn{(1-\alpha)/2||\beta_j||_2^2+\alpha||\beta_j||_2.} When \code{alpha=1} this is a group-lasso penalty, and otherwise it mixes with quadratic just like elasticnet. A small detail in the Cox model: if death times are tied with censored times, we assume the censored times occurred just \emph{before} the death times in computing the Breslow approximation; if users prefer the usual convention of \emph{after}, they can add a small number to all censoring times to achieve this effect. If \code{relax=TRUE} a duplicate sequence of models is produced, where each active set in the elastic-net path is refit without regularization. The result of this is a matching \code{"glmnet"} object which is stored on the original object in a component named \code{"relaxed"}, and is part of the glmnet output. Generally users will not call \code{relax.glmnet} directly, unless the original 'glmnet' object took a long time to fit. But if they do, they must supply the fit, and all the original arguments used to create that fit. They can limit the length of the relaxed path via 'maxp'. } \examples{ # Gaussian x = matrix(rnorm(100 * 20), 100, 20) y = rnorm(100) fit1 = glmnet(x, y) print(fit1) coef(fit1, s = 0.01) # extract coefficients at a single value of lambda predict(fit1, newx = x[1:10, ], s = c(0.01, 0.005)) # make predictions # Relaxed fit1r = glmnet(x, y, relax = TRUE) # can be used with any model # multivariate gaussian y = matrix(rnorm(100 * 3), 100, 3) fit1m = glmnet(x, y, family = "mgaussian") plot(fit1m, type.coef = "2norm") # binomial g2 = sample(1:2, 100, replace = TRUE) fit2 = glmnet(x, g2, family = "binomial") fit2r = glmnet(x,g2, family = "binomial", relax=TRUE) fit2rp = glmnet(x,g2, family = "binomial", relax=TRUE, path=TRUE) # multinomial g4 = sample(1:4, 100, replace = TRUE) fit3 = glmnet(x, g4, family = "multinomial") fit3a = glmnet(x, g4, family = "multinomial", type.multinomial = "grouped") # poisson N = 500 p = 20 nzc = 5 x = matrix(rnorm(N * p), N, p) beta = rnorm(nzc) f = x[, seq(nzc)] \%*\% beta mu = exp(f) y = rpois(N, mu) fit = glmnet(x, y, family = "poisson") plot(fit) pfit = predict(fit, x, s = 0.001, type = "response") plot(pfit, y) # Cox set.seed(10101) N = 1000 p = 30 nzc = p/3 x = matrix(rnorm(N * p), N, p) beta = rnorm(nzc) fx = x[, seq(nzc)] \%*\% beta/3 hx = exp(fx) ty = rexp(N, hx) tcens = rbinom(n = N, prob = 0.3, size = 1) # censoring indicator y = cbind(time = ty, status = 1 - tcens) # y=Surv(ty,1-tcens) with library(survival) fit = glmnet(x, y, family = "cox") plot(fit) # Sparse n = 10000 p = 200 nzc = trunc(p/10) x = matrix(rnorm(n * p), n, p) iz = sample(1:(n * p), size = n * p * 0.85, replace = FALSE) x[iz] = 0 sx = Matrix(x, sparse = TRUE) inherits(sx, "sparseMatrix") #confirm that it is sparse beta = rnorm(nzc) fx = x[, seq(nzc)] \%*\% beta eps = rnorm(n) y = fx + eps px = exp(fx) px = px/(1 + px) ly = rbinom(n = length(px), prob = px, size = 1) system.time(fit1 <- glmnet(sx, y)) system.time(fit2n <- glmnet(x, y)) } \references{ Friedman, J., Hastie, T. and Tibshirani, R. (2008) \emph{Regularization Paths for Generalized Linear Models via Coordinate Descent}, \url{https://web.stanford.edu/~hastie/Papers/glmnet.pdf}\cr \emph{Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010}\cr \url{https://www.jstatsoft.org/v33/i01/}\cr Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2011) \emph{Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5) 1-13}\cr \url{https://www.jstatsoft.org/v39/i05/}\cr Tibshirani, Robert, Bien, J., Friedman, J., Hastie, T.,Simon, N.,Taylor, J. and Tibshirani, Ryan. (2012) \emph{Strong Rules for Discarding Predictors in Lasso-type Problems, JRSSB vol 74},\cr \url{https://statweb.stanford.edu/~tibs/ftp/strong.pdf}\cr \emph{Stanford Statistics Technical Report}\cr \url{https://arxiv.org/abs/1707.08692}\cr Hastie, T., Tibshirani, Robert, Tibshirani, Ryan (2019) \emph{Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso}\cr \emph{Glmnet Vignette} \url{https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html} } \seealso{ \code{print}, \code{predict}, \code{coef} and \code{plot} methods, and the \code{cv.glmnet} function. } \author{ Jerome Friedman, Trevor Hastie, Balasubramanian Narasimhan, Noah Simon and Rob Tibshirani\cr Maintainer: Trevor Hastie \email{hastie@stanford.edu} } \keyword{models} \keyword{regression} glmnet/man/cv.glmnet.Rd0000644000176200001440000002520513555633674014524 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/cv.glmnet.R \name{cv.glmnet} \alias{cv.glmnet} \title{Cross-validation for glmnet} \usage{ cv.glmnet(x, y, weights = NULL, offset = NULL, lambda = NULL, type.measure = c("default", "mse", "deviance", "class", "auc", "mae", "C"), nfolds = 10, foldid = NULL, alignment = c("lambda", "fraction"), grouped = TRUE, keep = FALSE, parallel = FALSE, gamma = c(0, 0.25, 0.5, 0.75, 1), relax = FALSE, trace.it = 0, ...) } \arguments{ \item{x}{\code{x} matrix as in \code{glmnet}.} \item{y}{response \code{y} as in \code{glmnet}.} \item{weights}{Observation weights; defaults to 1 per observation} \item{offset}{Offset vector (matrix) as in \code{glmnet}} \item{lambda}{Optional user-supplied lambda sequence; default is \code{NULL}, and \code{glmnet} chooses its own sequence} \item{type.measure}{loss to use for cross-validation. Currently five options, not all available for all models. The default is \code{type.measure="deviance"}, which uses squared-error for gaussian models (a.k.a \code{type.measure="mse"} there), deviance for logistic and poisson regression, and partial-likelihood for the Cox model. \code{type.measure="class"} applies to binomial and multinomial logistic regression only, and gives misclassification error. \code{type.measure="auc"} is for two-class logistic regression only, and gives area under the ROC curve. \code{type.measure="mse"} or \code{type.measure="mae"} (mean absolute error) can be used by all models except the \code{"cox"}; they measure the deviation from the fitted mean to the response. \code{type.measure="C"} is Harrel's concordance measure, only available for \code{cox} models.} \item{nfolds}{number of folds - default is 10. Although \code{nfolds} can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is \code{nfolds=3}} \item{foldid}{an optional vector of values between 1 and \code{nfold} identifying what fold each observation is in. If supplied, \code{nfold} can be missing.} \item{alignment}{This is an experimental argument, designed to fix the problems users were having with CV, with possible values \code{"lambda"} (the default) else \code{"fraction"}. With \code{"lambda"} the \code{lambda} values from the master fit (on all the data) are used to line up the predictions from each of the folds. In some cases this can give strange values, since the effective \code{lambda} values in each fold could be quite different. With \code{"fraction"} we line up the predictions in each fold according to the fraction of progress along the regularization. If in the call a \code{lambda} argument is also provided, \code{alignment="fraction"} is ignored (with a warning).} \item{grouped}{This is an experimental argument, with default \code{TRUE}, and can be ignored by most users. For all models except the \code{"cox"}, this refers to computing \code{nfolds} separate statistics, and then using their mean and estimated standard error to describe the CV curve. If \code{grouped=FALSE}, an error matrix is built up at the observation level from the predictions from the \code{nfold} fits, and then summarized (does not apply to \code{type.measure="auc"}). For the \code{"cox"} family, \code{grouped=TRUE} obtains the CV partial likelihood for the Kth fold by \emph{subtraction}; by subtracting the log partial likelihood evaluated on the full dataset from that evaluated on the on the (K-1)/K dataset. This makes more efficient use of risk sets. With \code{grouped=FALSE} the log partial likelihood is computed only on the Kth fold} \item{keep}{If \code{keep=TRUE}, a \emph{prevalidated} array is returned containing fitted values for each observation and each value of \code{lambda}. This means these fits are computed with this observation and the rest of its fold omitted. The \code{folid} vector is also returned. Default is keep=FALSE. If \code{relax=TRUE}, then a list of such arrays is returned, one for each value of 'gamma'. Note: if the value 'gamma=1' is omitted, this case is included in the list since it corresponds to the original 'glmnet' fit.} \item{parallel}{If \code{TRUE}, use parallel \code{foreach} to fit each fold. Must register parallel before hand, such as \code{doMC} or others. See the example below.} \item{gamma}{The values of the parameter for mixing the relaxed fit with the regularized fit, between 0 and 1; default is \code{gamma = c(0, 0.25, 0.5, 0.75, 1)}} \item{relax}{If \code{TRUE}, then CV is done with respect to the mixing parameter \code{gamma} as well as \code{lambda}. Default is \code{relax=FALSE}} \item{trace.it}{If \code{trace.it=1}, then progress bars are displayed; useful for big models that take a long time to fit. Limited tracing if \code{parallel=TRUE}} \item{\dots}{Other arguments that can be passed to \code{glmnet}} } \value{ an object of class \code{"cv.glmnet"} is returned, which is a list with the ingredients of the cross-validation fit. If the object was created with \code{relax=TRUE} then this class has a prefix class of \code{"cv.relaxed"}. \item{lambda}{the values of \code{lambda} used in the fits.} \item{cvm}{The mean cross-validated error - a vector of length \code{length(lambda)}.} \item{cvsd}{estimate of standard error of \code{cvm}.} \item{cvup}{upper curve = \code{cvm+cvsd}.} \item{cvlo}{lower curve = \code{cvm-cvsd}.} \item{nzero}{number of non-zero coefficients at each \code{lambda}.} \item{name}{a text string indicating type of measure (for plotting purposes).} \item{glmnet.fit}{a fitted glmnet object for the full data.} \item{lambda.min}{value of \code{lambda} that gives minimum \code{cvm}.} \item{lambda.1se}{largest value of \code{lambda} such that error is within 1 standard error of the minimum.} \item{fit.preval}{if \code{keep=TRUE}, this is the array of prevalidated fits. Some entries can be \code{NA}, if that and subsequent values of \code{lambda} are not reached for that fold} \item{foldid}{if \code{keep=TRUE}, the fold assignments used} \item{relaxed}{if \code{relax=TRUE}, this additional item has the CV info for each of the mixed fits. In particular it also selects \code{lambda, gamma} pairs corresponding to the 1SE rule, as well as the minimum error.} } \description{ Does k-fold cross-validation for glmnet, produces a plot, and returns a value for \code{lambda} (and \code{gamma} if \code{relax=TRUE}) } \details{ The function runs \code{glmnet} \code{nfolds}+1 times; the first to get the \code{lambda} sequence, and then the remainder to compute the fit with each of the folds omitted. The error is accumulated, and the average error and standard deviation over the folds is computed. Note that \code{cv.glmnet} does NOT search for values for \code{alpha}. A specific value should be supplied, else \code{alpha=1} is assumed by default. If users would like to cross-validate \code{alpha} as well, they should call \code{cv.glmnet} with a pre-computed vector \code{foldid}, and then use this same fold vector in separate calls to \code{cv.glmnet} with different values of \code{alpha}. Note also that the results of \code{cv.glmnet} are random, since the folds are selected at random. Users can reduce this randomness by running \code{cv.glmnet} many times, and averaging the error curves. If \code{relax=TRUE} then the values of \code{gamma} are used to mix the fits. If \eqn{\eta} is the fit for lasso/elastic net, and \eqn{\eta_R} is the relaxed fit (with unpenalized coefficients), then a relaxed fit mixed by \eqn{\gamma} is \deqn{\eta(\gamma)=(1-\gamma)\eta_R+\gamma\eta}. There is practically no extra cost for having a lot of values for \code{gamma}. However, 5 seems sufficient for most purposes. CV then selects both \code{gamma} and \code{lambda}. } \examples{ set.seed(1010) n = 1000 p = 100 nzc = trunc(p/10) x = matrix(rnorm(n * p), n, p) beta = rnorm(nzc) fx = x[, seq(nzc)] \%*\% beta eps = rnorm(n) * 5 y = drop(fx + eps) px = exp(fx) px = px/(1 + px) ly = rbinom(n = length(px), prob = px, size = 1) set.seed(1011) cvob1 = cv.glmnet(x, y) plot(cvob1) coef(cvob1) predict(cvob1, newx = x[1:5, ], s = "lambda.min") title("Gaussian Family", line = 2.5) set.seed(1011) cvob1a = cv.glmnet(x, y, type.measure = "mae") plot(cvob1a) title("Gaussian Family", line = 2.5) set.seed(1011) par(mfrow = c(2, 2), mar = c(4.5, 4.5, 4, 1)) cvob2 = cv.glmnet(x, ly, family = "binomial") plot(cvob2) title("Binomial Family", line = 2.5) frame() set.seed(1011) cvob3 = cv.glmnet(x, ly, family = "binomial", type.measure = "class") plot(cvob3) title("Binomial Family", line = 2.5) \dontrun{ cvob1r = cv.glmnet(x, y, relax = TRUE) plot(cvob1r) predict(cvob1r, newx = x[, 1:5]) set.seed(1011) cvob3a = cv.glmnet(x, ly, family = "binomial", type.measure = "auc") plot(cvob3a) title("Binomial Family", line = 2.5) set.seed(1011) mu = exp(fx/10) y = rpois(n, mu) cvob4 = cv.glmnet(x, y, family = "poisson") plot(cvob4) title("Poisson Family", line = 2.5) # Multinomial n = 500 p = 30 nzc = trunc(p/10) x = matrix(rnorm(n * p), n, p) beta3 = matrix(rnorm(30), 10, 3) beta3 = rbind(beta3, matrix(0, p - 10, 3)) f3 = x \%*\% beta3 p3 = exp(f3) p3 = p3/apply(p3, 1, sum) g3 = glmnet:::rmult(p3) set.seed(10101) cvfit = cv.glmnet(x, g3, family = "multinomial") plot(cvfit) title("Multinomial Family", line = 2.5) # Cox beta = rnorm(nzc) fx = x[, seq(nzc)] \%*\% beta/3 hx = exp(fx) ty = rexp(n, hx) tcens = rbinom(n = n, prob = 0.3, size = 1) # censoring indicator y = cbind(time = ty, status = 1 - tcens) # y=Surv(ty,1-tcens) with library(survival) foldid = sample(rep(seq(10), length = n)) fit1_cv = cv.glmnet(x, y, family = "cox", foldid = foldid) plot(fit1_cv) title("Cox Family", line = 2.5) # Parallel require(doMC) registerDoMC(cores = 4) x = matrix(rnorm(1e+05 * 100), 1e+05, 100) y = rnorm(1e+05) system.time(cv.glmnet(x, y)) system.time(cv.glmnet(x, y, parallel = TRUE)) } } \references{ Friedman, J., Hastie, T. and Tibshirani, R. (2008) \emph{Regularization Paths for Generalized Linear Models via Coordinate Descent}, \url{https://web.stanford.edu/~hastie/Papers/glmnet.pdf}\cr \emph{Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010}\cr \url{https://www.jstatsoft.org/v33/i01/}\cr Simon, N., Friedman, J., Hastie, T., Tibshirani, R. (2011) \emph{Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5) 1-13}\cr \url{https://www.jstatsoft.org/v39/i05/} } \seealso{ \code{glmnet} and \code{plot}, \code{predict}, and \code{coef} methods for \code{"cv.glmnet"} and \code{"cv.relaxed"} objects. } \author{ Jerome Friedman, Trevor Hastie and Rob Tibshirani\cr Noah Simon helped develop the 'coxnet' function.\cr Jeffrey Wong and B. Narasimhan helped with the parallel option\cr Maintainer: Trevor Hastie \email{hastie@stanford.edu} } \keyword{models} \keyword{regression} glmnet/man/plot.cv.glmnet.Rd0000644000176200001440000000402013556622666015471 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot.cv.glmnet.R, R/plot.cv.relaxed.R \name{plot.cv.glmnet} \alias{plot.cv.glmnet} \alias{plot.cv.relaxed} \title{plot the cross-validation curve produced by cv.glmnet} \usage{ \method{plot}{cv.glmnet}(x, sign.lambda = 1, ...) \method{plot}{cv.relaxed}(x, se.bands = TRUE, ...) } \arguments{ \item{x}{fitted \code{"cv.glmnet"} object} \item{sign.lambda}{Either plot against \code{log(lambda)} (default) or its negative if \code{sign.lambda=-1}.} \item{\dots}{Other graphical parameters to plot} \item{se.bands}{Should shading be produced to show standard-error bands; default is \code{TRUE}} } \description{ Plots the cross-validation curve, and upper and lower standard deviation curves, as a function of the \code{lambda} values used. If the object has class \code{"cv.relaxed"} a different plot is produced, showing both \code{lambda} and \code{gamma} } \details{ A plot is produced, and nothing is returned. } \examples{ set.seed(1010) n = 1000 p = 100 nzc = trunc(p/10) x = matrix(rnorm(n * p), n, p) beta = rnorm(nzc) fx = (x[, seq(nzc)] \%*\% beta) eps = rnorm(n) * 5 y = drop(fx + eps) px = exp(fx) px = px/(1 + px) ly = rbinom(n = length(px), prob = px, size = 1) cvob1 = cv.glmnet(x, y) plot(cvob1) title("Gaussian Family", line = 2.5) cvob1r = cv.glmnet(x, y, relax = TRUE) plot(cvob1r) frame() set.seed(1011) par(mfrow = c(2, 2), mar = c(4.5, 4.5, 4, 1)) cvob2 = cv.glmnet(x, ly, family = "binomial") plot(cvob2) title("Binomial Family", line = 2.5) ## set.seed(1011) ## cvob3 = cv.glmnet(x, ly, family = "binomial", type = "class") ## plot(cvob3) ## title("Binomial Family", line = 2.5) } \references{ Friedman, J., Hastie, T. and Tibshirani, R. (2008) \emph{Regularization Paths for Generalized Linear Models via Coordinate Descent} } \seealso{ \code{glmnet} and \code{cv.glmnet}. } \author{ Jerome Friedman, Trevor Hastie and Rob Tibshirani\cr Maintainer: Trevor Hastie \href{mailto:hastie@stanford.edu}{hastie@stanford.edu} } \keyword{models} \keyword{regression} glmnet/man/deviance.glmnet.Rd0000644000176200001440000000305513553366407015664 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/deviance.glmnet.R \name{deviance.glmnet} \alias{deviance.glmnet} \title{Extract the deviance from a glmnet object} \usage{ \method{deviance}{glmnet}(object, ...) } \arguments{ \item{object}{fitted glmnet object} \item{\dots}{additional print arguments} } \value{ (1-dev.ratio)*nulldev } \description{ Compute the deviance sequence from the glmnet object } \details{ A glmnet object has components \code{dev.ratio} and \code{nulldev}. The former is the fraction of (null) deviance explained. The deviance calculations incorporate weights if present in the model. The deviance is defined to be 2*(loglike_sat - loglike), where loglike_sat is the log-likelihood for the saturated model (a model with a free parameter per observation). Null deviance is defined to be 2*(loglike_sat -loglike(Null)); The NULL model refers to the intercept model, except for the Cox, where it is the 0 model. Hence dev.ratio=1-deviance/nulldev, and this \code{deviance} method returns (1-dev.ratio)*nulldev. } \examples{ x = matrix(rnorm(100 * 20), 100, 20) y = rnorm(100) fit1 = glmnet(x, y) deviance(fit1) } \references{ Friedman, J., Hastie, T. and Tibshirani, R. (2008) \emph{Regularization Paths for Generalized Linear Models via Coordinate Descent} } \seealso{ \code{glmnet}, \code{predict}, \code{print}, and \code{coef} methods. } \author{ Jerome Friedman, Trevor Hastie and Rob Tibshirani\cr Maintainer: Trevor Hastie \href{mailto:hastie@stanford.edu}{hastie@stanford.edu} } \keyword{models} \keyword{regression} glmnet/man/rmult.Rd0000644000176200001440000000117613554367224013765 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rmult.R \name{rmult} \alias{rmult} \title{Generate multinomial samples from a probability matrix} \usage{ rmult(p) } \arguments{ \item{p}{matrix of probabilities, with number of columns the number of classes} } \value{ a vector of class memberships } \description{ Generate multinomial samples } \details{ Simple function that calls the \code{rmultinom} function. It generates a class label for each row of its input matrix of class probabilities. } \author{ Trevor Hastie \cr Maintainer: Trevor Hastie \href{mailto:hastie@stanford.edu}{hastie@stanford.edu} } glmnet/man/print.cv.glmnet.Rd0000644000176200001440000000315713553366407015654 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/print.cv.glmnet.R \name{print.cv.glmnet} \alias{print.cv.glmnet} \alias{print.cv.relaxed} \title{print a cross-validated glmnet object} \usage{ \method{print}{cv.glmnet}(x, digits = max(3, getOption("digits") - 3), ...) } \arguments{ \item{x}{fitted 'cv.glmnet' object} \item{digits}{significant digits in printout} \item{\dots}{additional print arguments} } \description{ Print a summary of the results of cross-validation for a glmnet model. } \details{ A summary of the cross-validated fit is produced, slightly different for a 'cv.relaxed' object than for a 'cv.glmnet' object. Note that a 'cv.relaxed' object inherits from class 'cv.glmnet', so by directly invoking \code{print.cv.glmnet(object)} will print the summary as if \code{relax=TRUE} had not been used. } \examples{ x = matrix(rnorm(100 * 20), 100, 20) y = rnorm(100) fit1 = cv.glmnet(x, y) print(fit1) fit1r = cv.glmnet(x, y, relax = TRUE) print(fit1r) ## print.cv.glmnet(fit1r) ## CHECK WITH TREVOR } \references{ Friedman, J., Hastie, T. and Tibshirani, R. (2008) \emph{Regularization Paths for Generalized Linear Models via Coordinate Descent}\cr \url{https://arxiv.org/abs/1707.08692}\cr Hastie, T., Tibshirani, Robert, Tibshirani, Ryan (2019) \emph{Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso} } \seealso{ \code{glmnet}, \code{predict} and \code{coef} methods. } \author{ Jerome Friedman, Trevor Hastie and Rob Tibshirani\cr Maintainer: Trevor Hastie \href{mailto:hastie@stanford.edu}{hastie@stanford.edu} } \keyword{models} \keyword{regression} glmnet/man/coxgrad.Rd0000644000176200001440000000207113553705340014236 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/coxgrad.R \name{coxgrad} \alias{coxgrad} \title{compute gradient for cox model} \usage{ coxgrad(f, time, d, w, eps = 1e-05) } \arguments{ \item{f}{fit vector} \item{time}{time vector (can have ties)} \item{d}{death/censoring indicator 1/0} \item{w}{observation weights (default equal)} \item{eps}{(default 0.00001) Breaks ties between death and censoring by making death times \code{eps} earlier} } \value{ a single gradient vector the same length as \code{f} } \description{ Compute the gradient of the partial likelihood at a particular fit } \details{ Compute a gradient vector at the fitted vector for the log partial likelihood. This is like a residual vector, and useful for manual screening of predictors for \code{glmnet} in applications where \code{p} is very large (as in GWAS). Uses the Breslow approach to ties } \seealso{ \code{coxnet.deviance} } \author{ Trevor Hastie\cr Maintainer: Trevor Hastie \href{mailto:hastie@stanford.edu}{hastie@stanford.edu} } \keyword{Cox} \keyword{model} glmnet/man/assess.glmnet.Rd0000644000176200001440000001023013573535152015375 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/assess.glmnet.R, R/confusion.glmnet.R, % R/roc.glmnet.R \name{assess.glmnet} \alias{assess.glmnet} \alias{confusion.glmnet} \alias{roc.glmnet} \title{assess performace of a 'glmnet' object using test data.} \usage{ assess.glmnet(object, newx = NULL, newy, weights = NULL, family = c("gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian"), ...) confusion.glmnet(object, newx = NULL, newy, family = c("binomial", "multinomial"), ...) roc.glmnet(object, newx = NULL, newy, ...) } \arguments{ \item{object}{Fitted \code{"glmnet"} or \code{"cv.glmnet"}, \code{"relaxed"} or \code{"cv.relaxed"} object, or a matrix of predictions (for \code{roc.glmnet} or \code{assess.glmnet}). For \code{roc.glmnet} the model must be a 'binomial', and for \code{confusion.glmnet} must be either 'binomial' or 'multinomial'} \item{newx}{If predictions are to made, these are the 'x' values. Required for \code{confusion.glmnet}} \item{newy}{required argument for all functions; the new response values} \item{weights}{For observation weights for the test observations} \item{family}{The family of the model, in case predictions are passed in as 'object'} \item{...}{additional arguments to \code{predict.glmnet} when "object" is a "glmnet" fit, and predictions must be made to produce the statistics.} } \value{ \code{assess.glmnet} produces a list of vectors of measures. \code{roc.glmnet} a list of 'roc' two-column matrices, and \code{confusion.glmnet} a list of tables. If a single prediction is provided, or predictions are made from a CV object, the latter two drop the list status and produce a single matrix or table. } \description{ Given a test set, produce summary performance measures for the glmnet model(s) } \details{ \code{assess.glmnet} produces all the different performance measures provided by \code{cv.glmnet} for each of the families. A single vector, or a matrix of predictions can be provided, or fitted model objects or CV objects. In the case when the predictions are still to be made, the \code{...} arguments allow, for example, 'offsets' and other prediction parameters such as values for 'gamma' for 'relaxed' fits. \code{roc.glmnet} produces for a single vector a two column matrix with columns TPR and FPR (true positive rate and false positive rate). This object can be plotted to produce an ROC curve. If more than one predictions are called for, then a list of such matrices is produced. \code{confusion.glmnet} produces a confusion matrix tabulating the classification results. Again, a single table or a list, with a print method. } \examples{ data(QuickStartExample) set.seed(11) train = sample(seq(length(y)),70,replace=FALSE) fit1 = glmnet(x[train,], y[train]) assess.glmnet(fit1, newx = x[-train,], newy = y[-train]) preds = predict(fit1, newx = x[-train, ], s = c(1, 0.25)) assess.glmnet(preds, newy = y[-train], family = "gaussian") fit1c = cv.glmnet(x, y, keep = TRUE) fit1a = assess.glmnet(fit1c$fit.preval, newy=y,family="gaussian") plot(fit1c$lambda, log="x",fit1a$mae,xlab="Log Lambda",ylab="Mean Absolute Error") abline(v=fit1c$lambda.min, lty=2, col="red") data(BinomialExample) fit2 = glmnet(x[train,], y[train], family = "binomial") assess.glmnet(fit2,newx = x[-train,], newy=y[-train], s=0.1) plot(roc.glmnet(fit2, newx = x[-train,], newy=y[-train])[[10]]) fit2c = cv.glmnet(x, y, family = "binomial", keep=TRUE) idmin = match(fit2c$lambda.min, fit2c$lambda) plot(roc.glmnet(fit2c$fit.preval, newy = y)[[idmin]]) data(MultinomialExample) set.seed(103) train = sample(seq(length(y)),100,replace=FALSE) fit3 = glmnet(x[train,], y[train], family = "multinomial") confusion.glmnet(fit3, newx = x[-train, ], newy = y[-train], s = 0.01) fit3c = cv.glmnet(x, y, family = "multinomial", type.measure="class", keep=TRUE) idmin = match(fit3c$lambda.min, fit3c$lambda) confusion.glmnet(fit3c$fit.preval, newy = y, family="multinomial")[[idmin]] } \seealso{ \code{cv.glmnet}, \code{glmnet.measures} and \code{vignette("relax",package="glmnet")} } \author{ Trevor Hastie and Rob Tibshirani\cr Maintainer: Trevor Hastie \href{mailto:hastie@stanford.edu}{hastie@stanford.edu} } \keyword{classification} \keyword{models} glmnet/man/glmnet-internal.Rd0000644000176200001440000000356313554366360015724 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/glmnet-package.R \name{glmnet-internal} \alias{glmnet-internal} \alias{auc} \alias{assess.coxnet} \alias{auc.mat} \alias{cvtype} \alias{cvstats} \alias{cvcompute} \alias{getcoef} \alias{getcoef.multinomial} \alias{response.coxnet} \alias{fix.lam} \alias{error.bars} \alias{getmin} \alias{elnet} \alias{mrelnet} \alias{lognet} \alias{fishnet} \alias{coefnorm} \alias{coxnet} \alias{cv.lognet} \alias{cv.elnet} \alias{cv.multnet} \alias{cv.mrelnet} \alias{cv.coxnet} \alias{cv.fishnet} \alias{cv.glmnet.raw} \alias{cv.relaxed.raw} \alias{blend.relaxed} \alias{checkgamma.relax} \alias{buildPredmat} \alias{buildPredmat.mrelnetlist} \alias{buildPredmat.multnetlist} \alias{buildPredmat.lognetlist} \alias{buildPredmat.array} \alias{buildPredmat.coxnetlist} \alias{buildPredmat.default} \alias{lambda.interp} \alias{nonzeroCoef} \alias{glmnet_softmax} \alias{getOptcv.glmnet} \alias{getOptcv.relaxed} \alias{jerr} \alias{jerr.elnet} \alias{jerr.lognet} \alias{jerr.fishnet} \alias{jerr.coxnet} \alias{jerr.mrelnet} \alias{plotCoef} \alias{zeromat} \alias{na.mean} \alias{check_dots} \alias{na_sparse_fix} \alias{prepareX} \title{Internal glmnet functions} \description{ These are not intended for use by users. \code{lambda.interp} does linear interpolation of the lambdas to obtain a prediction at a new point s. \code{glmnet_softmax} does the classification for multinomial models. \code{nonzeroCoef} determines in an efficient manner which variables are nonzero in each fit. \code{jerr} prints out error messages from the fortran. \code{plotCoef} is called by the \code{plot} method for \code{glmnet} objects. \code{check_dots} is used in \code{coef} and \code{predict} with argument \code{exact=TRUE}, to make sure user supplies original data used to fit the \code{"glmnet"} object. } \author{ Trevor Hastie } \keyword{internal} glmnet/man/na.replace.Rd0000644000176200001440000000304013553366407014623 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/makeX.R \name{na.replace} \alias{na.replace} \title{Replace the missing entries in a matrix columnwise with the entries in a supplied vector} \usage{ na.replace(x, m = rowSums(x, na.rm = TRUE)) } \arguments{ \item{x}{A matrix with potentially missing values, and also potentially in sparse matrix format (i.e. inherits from "sparseMatrix")} \item{m}{Optional argument. A vector of values used to replace the missing entries, columnwise. If missing, the column means of 'x' are used} } \value{ A version of 'x' is returned with the missing values replaced. } \description{ Missing entries in any given column of the matrix are replaced by the column means or the values in a supplied vector. } \details{ This is a simple imputation scheme. This function is called by \code{makeX} if the \code{na.impute=TRUE} option is used, but of course can be used on its own. If 'x' is sparse, the result is sparse, and the replacements are done so as to maintain sparsity. } \examples{ set.seed(101) ### Single data frame X = matrix(rnorm(20), 10, 2) X[3, 1] = NA X[5, 2] = NA X3 = sample(letters[1:3], 10, replace = TRUE) X3[6] = NA X4 = sample(LETTERS[1:3], 10, replace = TRUE) X4[9] = NA dfn = data.frame(X, X3, X4) x = makeX(dfn) m = rowSums(x, na.rm = TRUE) na.replace(x, m) x = makeX(dfn, sparse = TRUE) na.replace(x, m) } \seealso{ \code{makeX} and \code{glmnet} } \author{ Trevor Hastie\cr Maintainer: Trevor Hastie \href{mailto:hastie@stanford.edu}{hastie@stanford.edu} } \keyword{models} glmnet/man/Cindex.Rd0000644000176200001440000000300213553366407014023 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Cindex.R \name{Cindex} \alias{Cindex} \title{compute C index for a Cox model} \usage{ Cindex(pred, y, weights = rep(1, nrow(y))) } \arguments{ \item{pred}{Predictions from a \code{"coxnet"} object} \item{y}{a survival response object - a matrix with two columns "time" and "status"; see documentation for "glmnet"} \item{weights}{optional observation weights} } \description{ Computes Harrel's C index for predictions from a \code{"coxnet"} object. } \details{ Computes the concordance index, taking into account censoring. } \examples{ set.seed(10101) N = 1000 p = 30 nzc = p/3 x = matrix(rnorm(N * p), N, p) beta = rnorm(nzc) fx = x[, seq(nzc)] \%*\% beta/3 hx = exp(fx) ty = rexp(N, hx) tcens = rbinom(n = N, prob = 0.3, size = 1) # censoring indicator y = cbind(time = ty, status = 1 - tcens) # y=Surv(ty,1-tcens) with library(survival) fit = glmnet(x, y, family = "cox") pred = predict(fit, newx = x) Cindex(pred, y) cv.glmnet(x, y, family = "cox", type.measure = "C") } \references{ Harrel Jr, F. E. and Lee, K. L. and Mark, D. B. (1996) \emph{Tutorial in biostatistics: multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing error}, Statistics in Medicine, 15, pages 361--387. } \seealso{ \code{cv.glmnet} } \author{ Rob Tibshirani\cr Maintainer: Trevor Hastie \href{mailto:hastie@stanford.edu}{hastie@stanford.edu} } \keyword{Cox} \keyword{cross-validation} \keyword{models} glmnet/man/coxnet.deviance.Rd0000644000176200001440000000205313553705340015664 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/coxnet.deviance.R \name{coxnet.deviance} \alias{coxnet.deviance} \title{compute deviance for cox model output} \usage{ coxnet.deviance(pred = NULL, y, x = 0, offset = NULL, weights = NULL, beta = NULL) } \arguments{ \item{pred}{matrix of predictions} \item{y}{a survival response matrix, as produced by \code{Surv}} \item{x}{optional \code{x} matrix, if \code{pred} is \code{NULL}} \item{offset}{optional offset} \item{weights}{optional observation weights} \item{beta}{optional coefficient vector/matrix, supplied if \code{pred=NULL}} } \value{ a single or vector of deviances } \description{ Given a fit or coefficients, compute the deciance (-2 log partial likelihood) for right-censored survival data } \details{ \code{coxnet.deviance} computes the deviance for a single prediction, or a matrix of predictions } \seealso{ \code{coxgrad} } \author{ Trevor Hastie\cr Maintainer: Trevor Hastie \href{mailto:hastie@stanford.edu}{hastie@stanford.edu} } \keyword{Cox} \keyword{model} glmnet/DESCRIPTION0000644000176200001440000000303313574220222013244 0ustar liggesusersPackage: glmnet Type: Package Title: Lasso and Elastic-Net Regularized Generalized Linear Models Version: 3.0-2 Date: 2019-12-09 Authors@R: c(person("Jerome", "Friedman", role=c("aut")), person("Trevor", "Hastie", role=c("aut", "cre"), email = "hastie@stanford.edu"), person("Rob", "Tibshirani", role=c("aut")), person("Balasubramanian", "Narasimhan", role=c("aut")), person("Noah", "Simon", role=c("aut")), person("Junyang", "Qian", role=c("ctb"))) Depends: R (>= 3.6.0), Matrix (>= 1.0-6) Imports: methods, utils, foreach, shape Suggests: survival, knitr, lars Description: Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial regression. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers listed in the URL below. License: GPL-2 VignetteBuilder: knitr Encoding: UTF-8 URL: https://glmnet.stanford.edu, https://dx.doi.org/10.18637/jss.v033.i01, https://dx.doi.org/10.18637/jss.v039.i05 RoxygenNote: 6.1.1 NeedsCompilation: yes Packaged: 2019-12-11 01:55:13 UTC; hastie Author: Jerome Friedman [aut], Trevor Hastie [aut, cre], Rob Tibshirani [aut], Balasubramanian Narasimhan [aut], Noah Simon [aut], Junyang Qian [ctb] Maintainer: Trevor Hastie Repository: CRAN Date/Publication: 2019-12-11 17:00:02 UTC glmnet/build/0000755000176200001440000000000013574046177012655 5ustar liggesusersglmnet/build/vignette.rds0000644000176200001440000000041613574046177015215 0ustar liggesusersQMK1~v'o?`7ċ,fIɦz:f*[2/VeE lsYzn:IFwStHLŋQ|p$G.`}?ܭ=[>Ή WMV[ٞya@L3Z0e\Q6/<Őg/)"jxQ*`dOmo}MîÏ`ZYIE:#Al{3glmnet/src/0000755000176200001440000000000013574046201012331 5ustar liggesusersglmnet/src/glmnet_init.c0000644000176200001440000002624713573755070015033 0ustar liggesusers// Automatically generated, editing not advised. #ifndef R_GLMNET_H #define R_GLMNET_H #include #include #include #ifdef ENABLE_NLS #include #define _(String) dgettext ("glmnet", String) #else #define _(String) (String) #endif /* New addition */ extern SEXP storePB(SEXP tpb); static const R_CallMethodDef CallEntries[] = { {"storePB", (DL_FUNC) &storePB, 1}, {NULL, NULL, 0} }; /* End of new addition */ #define FDEF(name) {#name, (DL_FUNC) &F77_SUB(name), sizeof(name ## _t)/sizeof(name ## _t[0]), name ##_t} void F77_SUB(coxnet)( double *parm, int *no, int *ni, double *x, double *y, double *d, double *o, double *w, int *jd, double *vp, double *cl, int *ne, int *nx, int *nlam, double *flmin, double *ulam, double *thr, int *maxit, int *isd, int *lmu, double *ca, int *ia, int *nin, double *dev0, double *fdev, double *alm, int *nlp, int *jerrc ); static R_NativePrimitiveArgType coxnet_t[] = { REALSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, REALSXP, REALSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP, INTSXP, INTSXP, REALSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP, INTSXP }; void F77_SUB(spelnet)( int *ka, double *parm, int *no, int *ni, double *x, int *ix, int *jx, double *y, double *w, int *jd, double *vp, double *cl, int *ne, int *nx, int *nlam, double *flmin, double *ulam, double *thr, int *isd, int *intr, int *maxit, int *lmu, double *a0, double *ca, int *ia, int *nin, double *rsq, double *alm, int *nlp, int *jerr ); static R_NativePrimitiveArgType spelnet_t[] = { INTSXP, REALSXP, INTSXP, INTSXP, REALSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP }; void F77_SUB(elnet)( int *ka, double *parm, int *no, int *ni, double *x, double *y, double *w, int *jd, double *vp, double *cl, int *ne, int *nx, int *nlam, double *flmin, double *ulam, double *thr, int *isd, int *intr, int *maxit, int *lmu, double *a0, double *ca, int *ia, int *nin, double *rsq, double *alm, int *nlp, int *jerr ); static R_NativePrimitiveArgType elnet_t[] = { INTSXP, REALSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP }; void F77_SUB(fishnet)( double *parm, int *no, int *ni, double *x, double *y, double *g, double *w, int *jd, double *vp, double *cl, int *ne, int *nx, int *nlam, double *flmin, double *ulam, double *thr, int *isd, int *intr, int *maxit, int *lmu, double *a0, double *ca, int *ia, int *nin, double *dev0, double *dev, double *alm, int *nlp, int *jerr ); static R_NativePrimitiveArgType fishnet_t[] = { REALSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, REALSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP, INTSXP }; void F77_SUB(spfishnet)( double *parm, int *no, int *ni, double *x, int *ix, int *jx, double *y, double *g, double *w, int *jd, double *vp, double *cl, int *ne, int *nx, int *nlam, double *flmin, double *ulam, double *thr, int *isd, int *intr, int *maxit, int *lmu, double *a0, double *ca, int *ia, int *nin, double *dev0, double *dev, double *alm, int *nlp, int *jerr ); static R_NativePrimitiveArgType spfishnet_t[] = { REALSXP, INTSXP, INTSXP, REALSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP, INTSXP }; void F77_SUB(splognet)( double *parm, int *no, int *ni, int *nc, double *x, int *ix, int *jx, double *y, double *g, int *jd, double *vp, double *cl, int *ne, int *nx, int *nlam, double *flmin, double *ulam, double *thr, int *isd, int *intr, int *maxit, int *kopt, int *lmu, double *a0, double *ca, int *ia, int *nin, double *dev0, double *dev, double *alm, int *nlp, int *jerr ); static R_NativePrimitiveArgType splognet_t[] = { REALSXP, INTSXP, INTSXP, INTSXP, REALSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP, INTSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP, INTSXP }; void F77_SUB(lognet)( double *parm, int *no, int *ni, int *nc, double *x, double *y, double *g, int *jd, double *vp, double *cl, int *ne, int *nx, int *nlam, double *flmin, double *ulam, double *thr, int *isd, int *intr, int *maxit, int *kopt, int *lmu, double *a0, double *ca, int *ia, int *nin, double *dev0, double *dev, double *alm, int *nlp, int *jerr ); static R_NativePrimitiveArgType lognet_t[] = { REALSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP, INTSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP, INTSXP }; void F77_SUB(multspelnet)( double *parm, int *no, int *ni, int *nr, double *x, int *ix, int *jx, double *y, double *w, int *jd, double *vp, double *cl, int *ne, int *nx, int *nlam, double *flmin, double *ulam, double *thr, int *isd, int *jsd, int *intr, int *maxit, int *lmu, double *a0, double *ca, int *ia, int *nin, double *rsq, double *alm, int *nlp, int *jerr ); static R_NativePrimitiveArgType multspelnet_t[] = { REALSXP, INTSXP, INTSXP, INTSXP, REALSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP, INTSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP }; void F77_SUB(multelnet)( double *parm, int *no, int *ni, int *nr, double *x, double *y, double *w, int *jd, double *vp, double *cl, int *ne, int *nx, int *nlam, double *flmin, double *ulam, double *thr, int *isd, int *jsd, int *intr, int *maxit, int *lmu, double *a0, double *ca, int *ia, int *nin, double *rsq, double *alm, int *nlp, int *jerr ); static R_NativePrimitiveArgType multelnet_t[] = { REALSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP, INTSXP, INTSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP, REALSXP, REALSXP, INTSXP, INTSXP }; void F77_SUB(loglike)( int *no, int *ni, double *x, double *y, double *d, double *g, double *w, int *nlam, double *a, double *flog, int *jerr ); static R_NativePrimitiveArgType loglike_t[] = { INTSXP, INTSXP, REALSXP, REALSXP, REALSXP, REALSXP, REALSXP, INTSXP, REALSXP, REALSXP, INTSXP }; void F77_SUB(get_int_parms)( double *sml, double *eps, double *big, int *mnlam, double *rsqmax, double *pmin, double *exmx, int *itrace ); static R_NativePrimitiveArgType get_int_parms_t[] = { REALSXP, REALSXP, REALSXP, INTSXP, REALSXP, REALSXP, REALSXP, INTSXP }; void F77_SUB(chg_fract_dev)( double *fdev ); static R_NativePrimitiveArgType chg_fract_dev_t[] = { REALSXP }; void F77_SUB(chg_dev_max)( double *devmax ); static R_NativePrimitiveArgType chg_dev_max_t[] = { REALSXP }; void F77_SUB(chg_min_flmin)( double *eps ); static R_NativePrimitiveArgType chg_min_flmin_t[] = { REALSXP }; void F77_SUB(chg_big)( double *big ); static R_NativePrimitiveArgType chg_big_t[] = { REALSXP }; void F77_SUB(chg_min_lambdas)( int *mnlam ); static R_NativePrimitiveArgType chg_min_lambdas_t[] = { INTSXP }; void F77_SUB(chg_min_null_prob)( double *pmin ); static R_NativePrimitiveArgType chg_min_null_prob_t[] = { REALSXP }; void F77_SUB(chg_max_exp)( double *exmx ); static R_NativePrimitiveArgType chg_max_exp_t[] = { REALSXP }; void F77_SUB(chg_itrace)( int *itrace ); static R_NativePrimitiveArgType chg_itrace_t[] = { INTSXP }; void F77_SUB(chg_bnorm)( double *prec, int *mxit ); static R_NativePrimitiveArgType chg_bnorm_t[] = { REALSXP, INTSXP }; void F77_SUB(get_bnorm)( double *arg, int *irg ); static R_NativePrimitiveArgType get_bnorm_t[] = { REALSXP, INTSXP }; static R_FortranMethodDef fMethods[] = { FDEF(coxnet) , FDEF(spelnet) , FDEF(elnet) , FDEF(fishnet) , FDEF(spfishnet) , FDEF(splognet) , FDEF(lognet) , FDEF(multspelnet) , FDEF(multelnet) , FDEF(loglike) , FDEF(get_int_parms) , FDEF(chg_fract_dev) , FDEF(chg_dev_max) , FDEF(chg_min_flmin) , FDEF(chg_big) , FDEF(chg_min_lambdas) , FDEF(chg_min_null_prob) , FDEF(chg_max_exp) , FDEF(chg_itrace) , FDEF(chg_bnorm) , FDEF(get_bnorm) , {NULL, NULL, 0} }; void R_init_glmnet(DllInfo *dll){ R_registerRoutines(dll, NULL, CallEntries, fMethods, NULL); R_useDynamicSymbols(dll, FALSE); } #endif glmnet/src/pb.c0000644000176200001440000000120313573515432013100 0ustar liggesusers#include #include /* The progress bar */ static SEXP pb; /* Save the progress bar */ SEXP storePB(SEXP tpb) { pb = tpb; return(R_NilValue); } /* Set Progress bar to value */ void F77_SUB(setpb)(int *val) { SEXP s, t; /* printf("%d\n", *val); */ t = s = PROTECT(allocList(3)); SET_TYPEOF(s, LANGSXP); SETCAR(t, install("setTxtProgressBar")); t = CDR(t); SETCAR(t, pb); SET_TAG(t, install("pb")); t = CDR(t); SETCAR(t, ScalarInteger(*val)); SET_TAG(t, install("value")); eval(s, R_GetCurrentEnv()); UNPROTECT(1); } /* Nullify progress bar, not really used */ void nullifyPB() { pb = NULL; } glmnet/src/glmnet5dpclean.f0000644000176200001440000173035413542237467015432 0ustar liggesusersc mortran 2.0 (version of 7/04/75 mod 7/4/87 (ajc)) subroutine get_int_parms(sml,eps,big,mnlam,rsqmax,pmin,exmx,itrace *) implicit double precision(a-h,o-z) data sml0,eps0,big0,mnlam0,rsqmax0,pmin0,exmx0,itrace0 /1.0d-5,1. *0d-6,9.9d35,5,0.999,1.0d-9,250.0,0/ sml=sml0 eps=eps0 big=big0 mnlam=mnlam0 rsqmax=rsqmax0 pmin=pmin0 exmx=exmx0 itrace=itrace0 return entry chg_fract_dev(arg) sml0=arg return entry chg_dev_max(arg) rsqmax0=arg return entry chg_min_flmin(arg) eps0=arg return entry chg_big(arg) big0=arg return entry chg_min_lambdas(irg) mnlam0=irg return entry chg_min_null_prob(arg) pmin0=arg return entry chg_max_exp(arg) exmx0=arg return entry chg_itrace(irg) itrace0=irg return end subroutine elnet(ka,parm,no,ni,x,y,w,jd,vp,cl,ne,nx,nlam, flmin,u *lam,thr,isd,intr,maxit, lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr) implicit double precision(a-h,o-z) double precision x(no,ni),y(no),w(no),vp(ni),ca(nx,nlam),cl(2,ni) double precision ulam(nlam),a0(nlam),rsq(nlam),alm(nlam) integer jd(*),ia(nx),nin(nlam) double precision, dimension (:), allocatable :: vq; if(maxval(vp) .gt. 0.0)goto 10021 jerr=10000 return 10021 continue allocate(vq(1:ni),stat=jerr) if(jerr.ne.0) return vq=max(0d0,vp) vq=vq*ni/sum(vq) if(ka .ne. 1)goto 10041 call elnetu (parm,no,ni,x,y,w,jd,vq,cl,ne,nx,nlam,flmin,ulam,thr, *isd,intr,maxit, lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr) goto 10051 10041 continue call elnetn (parm,no,ni,x,y,w,jd,vq,cl,ne,nx,nlam,flmin,ulam,thr,i *sd,intr,maxit, lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr) 10051 continue continue deallocate(vq) return end subroutine elnetu(parm,no,ni,x,y,w,jd,vp,cl,ne,nx,nlam, flmin,ula *m,thr,isd,intr,maxit, lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr) implicit double precision(a-h,o-z) double precision x(no,ni),y(no),w(no),vp(ni),ulam(nlam),cl(2,ni) double precision ca(nx,nlam),a0(nlam),rsq(nlam),alm(nlam) integer jd(*),ia(nx),nin(nlam) double precision, dimension (:), allocatable :: xm,xs,g,xv,vlam integer, dimension (:), allocatable :: ju allocate(g(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xs(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ju(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xv(1:ni),stat=jerr) if(jerr.ne.0) return allocate(vlam(1:nlam),stat=jerr) if(jerr.ne.0) return call chkvars(no,ni,x,ju) if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0 if(maxval(ju) .gt. 0)goto 10071 jerr=7777 return 10071 continue call standard(no,ni,x,y,w,isd,intr,ju,g,xm,xs,ym,ys,xv,jerr) if(jerr.ne.0) return cl=cl/ys if(isd .le. 0)goto 10091 do 10101 j=1,ni cl(:,j)=cl(:,j)*xs(j) 10101 continue continue 10091 continue if(flmin.ge.1.0) vlam=ulam/ys call elnet1(parm,ni,ju,vp,cl,g,no,ne,nx,x,nlam,flmin,vlam,thr,maxi *t,xv, lmu,ca,ia,nin,rsq,alm,nlp,jerr) if(jerr.gt.0) return do 10111 k=1,lmu alm(k)=ys*alm(k) nk=nin(k) do 10121 l=1,nk ca(l,k)=ys*ca(l,k)/xs(ia(l)) 10121 continue continue a0(k)=0.0 if(intr.ne.0) a0(k)=ym-dot_product(ca(1:nk,k),xm(ia(1:nk))) 10111 continue continue deallocate(xm,xs,g,ju,xv,vlam) return end subroutine standard(no,ni,x,y,w,isd,intr,ju,g,xm,xs,ym,ys,xv,jerr) implicit double precision(a-h,o-z) double precision x(no,ni),y(no),w(no),g(ni),xm(ni),xs(ni),xv(ni) integer ju(ni) double precision, dimension (:), allocatable :: v allocate(v(1:no),stat=jerr) if(jerr.ne.0) return w=w/sum(w) v=sqrt(w) if(intr .ne. 0)goto 10141 ym=0.0 y=v*y ys=sqrt(dot_product(y,y)-dot_product(v,y)**2) y=y/ys do 10151 j=1,ni if(ju(j).eq.0)goto 10151 xm(j)=0.0 x(:,j)=v*x(:,j) xv(j)=dot_product(x(:,j),x(:,j)) if(isd .eq. 0)goto 10171 xbq=dot_product(v,x(:,j))**2 vc=xv(j)-xbq xs(j)=sqrt(vc) x(:,j)=x(:,j)/xs(j) xv(j)=1.0+xbq/vc goto 10181 10171 continue xs(j)=1.0 10181 continue continue 10151 continue continue goto 10191 10141 continue do 10201 j=1,ni if(ju(j).eq.0)goto 10201 xm(j)=dot_product(w,x(:,j)) x(:,j)=v*(x(:,j)-xm(j)) xv(j)=dot_product(x(:,j),x(:,j)) if(isd.gt.0) xs(j)=sqrt(xv(j)) 10201 continue continue if(isd .ne. 0)goto 10221 xs=1.0 goto 10231 10221 continue do 10241 j=1,ni if(ju(j).eq.0)goto 10241 x(:,j)=x(:,j)/xs(j) 10241 continue continue xv=1.0 10231 continue continue ym=dot_product(w,y) y=v*(y-ym) ys=sqrt(dot_product(y,y)) y=y/ys 10191 continue continue g=0.0 do 10251 j=1,ni if(ju(j).ne.0) g(j)=dot_product(y,x(:,j)) 10251 continue continue deallocate(v) return end subroutine elnet1(beta,ni,ju,vp,cl,g,no,ne,nx,x,nlam,flmin,ulam,th *r,maxit,xv, lmu,ao,ia,kin,rsqo,almo,nlp,jerr) implicit double precision(a-h,o-z) double precision vp(ni),g(ni),x(no,ni),ulam(nlam),ao(nx,nlam) double precision rsqo(nlam),almo(nlam),xv(ni) double precision cl(2,ni) integer ju(ni),ia(nx),kin(nlam) double precision, dimension (:), allocatable :: a,da integer, dimension (:), allocatable :: mm double precision, dimension (:,:), allocatable :: c allocate(c(1:ni,1:nx),stat=jerr) if(jerr.ne.0) return; call get_int_parms(sml,eps,big,mnlam,rsqmax,pmin,exmx,itrace) allocate(a(1:ni),stat=jerr) if(jerr.ne.0) return allocate(mm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(da(1:ni),stat=jerr) if(jerr.ne.0) return bta=beta omb=1.0-bta alm=0.0 alf=1.0 if(flmin .ge. 1.0)goto 10271 eqs=max(eps,flmin) alf=eqs**(1.0/(nlam-1)) 10271 continue rsq=0.0 a=0.0 mm=0 nlp=0 nin=nlp iz=0 mnl=min(mnlam,nlam) do 10281 m=1,nlam if(itrace.ne.0) call setpb(m-1) if(flmin .lt. 1.0)goto 10301 alm=ulam(m) goto 10291 10301 if(m .le. 2)goto 10311 alm=alm*alf goto 10291 10311 if(m .ne. 1)goto 10321 alm=big goto 10331 10321 continue alm=0.0 do 10341 j=1,ni if(ju(j).eq.0)goto 10341 if(vp(j).le.0.0)goto 10341 alm=max(alm,abs(g(j))/vp(j)) 10341 continue continue alm=alf*alm/max(bta,1.0d-3) 10331 continue 10291 continue dem=alm*omb ab=alm*bta rsq0=rsq jz=1 continue 10351 continue if(iz*jz.ne.0) go to 10360 nlp=nlp+1 dlx=0.0 do 10371 k=1,ni if(ju(k).eq.0)goto 10371 ak=a(k) u=g(k)+ak*xv(k) v=abs(u)-vp(k)*ab a(k)=0.0 if(v.gt.0.0) a(k)=max(cl(1,k),min(cl(2,k),sign(v,u)/(xv(k)+vp(k)*d *em))) if(a(k).eq.ak)goto 10371 if(mm(k) .ne. 0)goto 10391 nin=nin+1 if(nin.gt.nx)goto 10372 do 10401 j=1,ni if(ju(j).eq.0)goto 10401 if(mm(j) .eq. 0)goto 10421 c(j,nin)=c(k,mm(j)) goto 10401 10421 continue if(j .ne. k)goto 10441 c(j,nin)=xv(j) goto 10401 10441 continue c(j,nin)=dot_product(x(:,j),x(:,k)) 10401 continue continue mm(k)=nin ia(nin)=k 10391 continue del=a(k)-ak rsq=rsq+del*(2.0*g(k)-del*xv(k)) dlx=max(xv(k)*del**2,dlx) do 10451 j=1,ni if(ju(j).ne.0) g(j)=g(j)-c(j,mm(k))*del 10451 continue continue 10371 continue 10372 continue if(dlx.lt.thr)goto 10352 if(nin.gt.nx)goto 10352 if(nlp .le. maxit)goto 10471 jerr=-m return 10471 continue 10360 continue iz=1 da(1:nin)=a(ia(1:nin)) continue 10481 continue nlp=nlp+1 dlx=0.0 do 10491 l=1,nin k=ia(l) ak=a(k) u=g(k)+ak*xv(k) v=abs(u)-vp(k)*ab a(k)=0.0 if(v.gt.0.0) a(k)=max(cl(1,k),min(cl(2,k),sign(v,u)/(xv(k)+vp(k)*d *em))) if(a(k).eq.ak)goto 10491 del=a(k)-ak rsq=rsq+del*(2.0*g(k)-del*xv(k)) dlx=max(xv(k)*del**2,dlx) do 10501 j=1,nin g(ia(j))=g(ia(j))-c(ia(j),mm(k))*del 10501 continue continue 10491 continue continue if(dlx.lt.thr)goto 10482 if(nlp .le. maxit)goto 10521 jerr=-m return 10521 continue goto 10481 10482 continue da(1:nin)=a(ia(1:nin))-da(1:nin) do 10531 j=1,ni if(mm(j).ne.0)goto 10531 if(ju(j).ne.0) g(j)=g(j)-dot_product(da(1:nin),c(j,1:nin)) 10531 continue continue jz=0 goto 10351 10352 continue if(nin .le. nx)goto 10551 jerr=-10000-m goto 10282 10551 continue if(nin.gt.0) ao(1:nin,m)=a(ia(1:nin)) kin(m)=nin rsqo(m)=rsq almo(m)=alm lmu=m if(m.lt.mnl)goto 10281 if(flmin.ge.1.0)goto 10281 me=0 do 10561 j=1,nin if(ao(j,m).ne.0.0) me=me+1 10561 continue continue if(me.gt.ne)goto 10282 if(rsq-rsq0.lt.sml*rsq)goto 10282 if(rsq.gt.rsqmax)goto 10282 10281 continue 10282 continue deallocate(a,mm,c,da) return end subroutine elnetn(parm,no,ni,x,y,w,jd,vp,cl,ne,nx,nlam,flmin,ulam, *thr,isd, intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr) implicit double precision(a-h,o-z) double precision vp(ni),x(no,ni),y(no),w(no),ulam(nlam),cl(2,ni) double precision ca(nx,nlam),a0(nlam),rsq(nlam),alm(nlam) integer jd(*),ia(nx),nin(nlam) double precision, dimension (:), allocatable :: xm,xs,xv,vlam integer, dimension (:), allocatable :: ju allocate(xm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xs(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ju(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xv(1:ni),stat=jerr) if(jerr.ne.0) return allocate(vlam(1:nlam),stat=jerr) if(jerr.ne.0) return call chkvars(no,ni,x,ju) if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0 if(maxval(ju) .gt. 0)goto 10581 jerr=7777 return 10581 continue call standard1(no,ni,x,y,w,isd,intr,ju,xm,xs,ym,ys,xv,jerr) if(jerr.ne.0) return cl=cl/ys if(isd .le. 0)goto 10601 do 10611 j=1,ni cl(:,j)=cl(:,j)*xs(j) 10611 continue continue 10601 continue if(flmin.ge.1.0) vlam=ulam/ys call elnet2(parm,ni,ju,vp,cl,y,no,ne,nx,x,nlam,flmin,vlam,thr,maxi *t,xv, lmu,ca,ia,nin,rsq,alm,nlp,jerr) if(jerr.gt.0) return do 10621 k=1,lmu alm(k)=ys*alm(k) nk=nin(k) do 10631 l=1,nk ca(l,k)=ys*ca(l,k)/xs(ia(l)) 10631 continue continue a0(k)=0.0 if(intr.ne.0) a0(k)=ym-dot_product(ca(1:nk,k),xm(ia(1:nk))) 10621 continue continue deallocate(xm,xs,ju,xv,vlam) return end subroutine standard1(no,ni,x,y,w,isd,intr,ju,xm,xs,ym,ys,xv,jerr) implicit double precision(a-h,o-z) double precision x(no,ni),y(no),w(no),xm(ni),xs(ni),xv(ni) integer ju(ni) double precision, dimension (:), allocatable :: v allocate(v(1:no),stat=jerr) if(jerr.ne.0) return w=w/sum(w) v=sqrt(w) if(intr .ne. 0)goto 10651 ym=0.0 y=v*y ys=sqrt(dot_product(y,y)-dot_product(v,y)**2) y=y/ys do 10661 j=1,ni if(ju(j).eq.0)goto 10661 xm(j)=0.0 x(:,j)=v*x(:,j) xv(j)=dot_product(x(:,j),x(:,j)) if(isd .eq. 0)goto 10681 xbq=dot_product(v,x(:,j))**2 vc=xv(j)-xbq xs(j)=sqrt(vc) x(:,j)=x(:,j)/xs(j) xv(j)=1.0+xbq/vc goto 10691 10681 continue xs(j)=1.0 10691 continue continue 10661 continue continue go to 10700 10651 continue do 10711 j=1,ni if(ju(j).eq.0)goto 10711 xm(j)=dot_product(w,x(:,j)) x(:,j)=v*(x(:,j)-xm(j)) xv(j)=dot_product(x(:,j),x(:,j)) if(isd.gt.0) xs(j)=sqrt(xv(j)) 10711 continue continue if(isd .ne. 0)goto 10731 xs=1.0 goto 10741 10731 continue do 10751 j=1,ni if(ju(j).eq.0)goto 10751 x(:,j)=x(:,j)/xs(j) 10751 continue continue xv=1.0 10741 continue continue ym=dot_product(w,y) y=v*(y-ym) ys=sqrt(dot_product(y,y)) y=y/ys 10700 continue deallocate(v) return end subroutine elnet2(beta,ni,ju,vp,cl,y,no,ne,nx,x,nlam,flmin,ulam,th *r,maxit,xv, lmu,ao,ia,kin,rsqo,almo,nlp,jerr) implicit double precision(a-h,o-z) double precision vp(ni),y(no),x(no,ni),ulam(nlam),ao(nx,nlam) double precision rsqo(nlam),almo(nlam),xv(ni) double precision cl(2,ni) integer ju(ni),ia(nx),kin(nlam) double precision, dimension (:), allocatable :: a,g integer, dimension (:), allocatable :: mm,ix call get_int_parms(sml,eps,big,mnlam,rsqmax,pmin,exmx,itrace) allocate(a(1:ni),stat=jerr) if(jerr.ne.0) return allocate(mm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(g(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ix(1:ni),stat=jerr) if(jerr.ne.0) return bta=beta omb=1.0-bta ix=0 alf=1.0 if(flmin .ge. 1.0)goto 10771 eqs=max(eps,flmin) alf=eqs**(1.0/(nlam-1)) 10771 continue rsq=0.0 a=0.0 mm=0 nlp=0 nin=nlp iz=0 mnl=min(mnlam,nlam) alm=0.0 do 10781 j=1,ni if(ju(j).eq.0)goto 10781 g(j)=abs(dot_product(y,x(:,j))) 10781 continue continue do 10791 m=1,nlam if(itrace.ne.0) call setpb(m-1) alm0=alm if(flmin .lt. 1.0)goto 10811 alm=ulam(m) goto 10801 10811 if(m .le. 2)goto 10821 alm=alm*alf goto 10801 10821 if(m .ne. 1)goto 10831 alm=big goto 10841 10831 continue alm0=0.0 do 10851 j=1,ni if(ju(j).eq.0)goto 10851 if(vp(j).gt.0.0) alm0=max(alm0,g(j)/vp(j)) 10851 continue continue alm0=alm0/max(bta,1.0d-3) alm=alf*alm0 10841 continue 10801 continue dem=alm*omb ab=alm*bta rsq0=rsq jz=1 tlam=bta*(2.0*alm-alm0) do 10861 k=1,ni if(ix(k).eq.1)goto 10861 if(ju(k).eq.0)goto 10861 if(g(k).gt.tlam*vp(k)) ix(k)=1 10861 continue continue continue 10871 continue if(iz*jz.ne.0) go to 10360 10880 continue nlp=nlp+1 dlx=0.0 do 10891 k=1,ni if(ix(k).eq.0)goto 10891 gk=dot_product(y,x(:,k)) ak=a(k) u=gk+ak*xv(k) v=abs(u)-vp(k)*ab a(k)=0.0 if(v.gt.0.0) a(k)=max(cl(1,k),min(cl(2,k),sign(v,u)/(xv(k)+vp(k)*d *em))) if(a(k).eq.ak)goto 10891 if(mm(k) .ne. 0)goto 10911 nin=nin+1 if(nin.gt.nx)goto 10892 mm(k)=nin ia(nin)=k 10911 continue del=a(k)-ak rsq=rsq+del*(2.0*gk-del*xv(k)) y=y-del*x(:,k) dlx=max(xv(k)*del**2,dlx) 10891 continue 10892 continue if(nin.gt.nx)goto 10872 if(dlx .ge. thr)goto 10931 ixx=0 do 10941 k=1,ni if(ix(k).eq.1)goto 10941 if(ju(k).eq.0)goto 10941 g(k)=abs(dot_product(y,x(:,k))) if(g(k) .le. ab*vp(k))goto 10961 ix(k)=1 ixx=1 10961 continue 10941 continue continue if(ixx.eq.1) go to 10880 goto 10872 10931 continue if(nlp .le. maxit)goto 10981 jerr=-m return 10981 continue 10360 continue iz=1 continue 10991 continue nlp=nlp+1 dlx=0.0 do 11001 l=1,nin k=ia(l) gk=dot_product(y,x(:,k)) ak=a(k) u=gk+ak*xv(k) v=abs(u)-vp(k)*ab a(k)=0.0 if(v.gt.0.0) a(k)=max(cl(1,k),min(cl(2,k),sign(v,u)/(xv(k)+vp(k)*d *em))) if(a(k).eq.ak)goto 11001 del=a(k)-ak rsq=rsq+del*(2.0*gk-del*xv(k)) y=y-del*x(:,k) dlx=max(xv(k)*del**2,dlx) 11001 continue continue if(dlx.lt.thr)goto 10992 if(nlp .le. maxit)goto 11021 jerr=-m return 11021 continue goto 10991 10992 continue jz=0 goto 10871 10872 continue if(nin .le. nx)goto 11041 jerr=-10000-m goto 10792 11041 continue if(nin.gt.0) ao(1:nin,m)=a(ia(1:nin)) kin(m)=nin rsqo(m)=rsq almo(m)=alm lmu=m if(m.lt.mnl)goto 10791 if(flmin.ge.1.0)goto 10791 me=0 do 11051 j=1,nin if(ao(j,m).ne.0.0) me=me+1 11051 continue continue if(me.gt.ne)goto 10792 if(rsq-rsq0.lt.sml*rsq)goto 10792 if(rsq.gt.rsqmax)goto 10792 10791 continue 10792 continue deallocate(a,mm,g,ix) return end subroutine chkvars(no,ni,x,ju) implicit double precision(a-h,o-z) double precision x(no,ni) integer ju(ni) do 11061 j=1,ni ju(j)=0 t=x(1,j) do 11071 i=2,no if(x(i,j).eq.t)goto 11071 ju(j)=1 goto 11072 11071 continue 11072 continue 11061 continue continue return end subroutine uncomp(ni,ca,ia,nin,a) implicit double precision(a-h,o-z) double precision ca(*),a(ni) integer ia(*) a=0.0 if(nin.gt.0) a(ia(1:nin))=ca(1:nin) return end subroutine modval(a0,ca,ia,nin,n,x,f) implicit double precision(a-h,o-z) double precision ca(nin),x(n,*),f(n) integer ia(nin) f=a0 if(nin.le.0) return do 11081 i=1,n f(i)=f(i)+dot_product(ca(1:nin),x(i,ia(1:nin))) 11081 continue continue return end subroutine spelnet(ka,parm,no,ni,x,ix,jx,y,w,jd,vp,cl,ne,nx,nlam, * flmin,ulam,thr,isd,intr, maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr *) implicit double precision(a-h,o-z) double precision x(*),y(no),w(no),vp(ni),ulam(nlam),cl(2,ni) double precision ca(nx,nlam),a0(nlam),rsq(nlam),alm(nlam) integer ix(*),jx(*),jd(*),ia(nx),nin(nlam) double precision, dimension (:), allocatable :: vq; if(maxval(vp) .gt. 0.0)goto 11101 jerr=10000 return 11101 continue allocate(vq(1:ni),stat=jerr) if(jerr.ne.0) return vq=max(0d0,vp) vq=vq*ni/sum(vq) if(ka .ne. 1)goto 11121 call spelnetu (parm,no,ni,x,ix,jx,y,w,jd,vq,cl,ne,nx,nlam,flmin,u *lam,thr,isd, intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr) goto 11131 11121 continue call spelnetn (parm,no,ni,x,ix,jx,y,w,jd,vq,cl,ne,nx,nlam,flmin,ul *am,thr,isd,intr, maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr) 11131 continue continue deallocate(vq) return end subroutine spelnetu(parm,no,ni,x,ix,jx,y,w,jd,vp,cl,ne,nx,nlam, f *lmin,ulam,thr,isd,intr, maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr) implicit double precision(a-h,o-z) double precision x(*),y(no),w(no),vp(ni),ulam(nlam),cl(2,ni) double precision ca(nx,nlam),a0(nlam),rsq(nlam),alm(nlam) integer ix(*),jx(*),jd(*),ia(nx),nin(nlam) double precision, dimension (:), allocatable :: xm,xs,g,xv,vlam integer, dimension (:), allocatable :: ju allocate(g(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xs(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ju(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xv(1:ni),stat=jerr) if(jerr.ne.0) return allocate(vlam(1:nlam),stat=jerr) if(jerr.ne.0) return call spchkvars(no,ni,x,ix,ju) if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0 if(maxval(ju) .gt. 0)goto 11151 jerr=7777 return 11151 continue call spstandard(no,ni,x,ix,jx,y,w,ju,isd,intr,g,xm,xs,ym,ys,xv,jer *r) if(jerr.ne.0) return cl=cl/ys if(isd .le. 0)goto 11171 do 11181 j=1,ni cl(:,j)=cl(:,j)*xs(j) 11181 continue continue 11171 continue if(flmin.ge.1.0) vlam=ulam/ys call spelnet1(parm,ni,g,no,w,ne,nx,x,ix,jx,ju,vp,cl,nlam,flmin,vla *m,thr,maxit, xm,xs,xv,lmu,ca,ia,nin,rsq,alm,nlp,jerr) if(jerr.gt.0) return do 11191 k=1,lmu alm(k)=ys*alm(k) nk=nin(k) do 11201 l=1,nk ca(l,k)=ys*ca(l,k)/xs(ia(l)) 11201 continue continue a0(k)=0.0 if(intr.ne.0) a0(k)=ym-dot_product(ca(1:nk,k),xm(ia(1:nk))) 11191 continue continue deallocate(xm,xs,g,ju,xv,vlam) return end subroutine spstandard(no,ni,x,ix,jx,y,w,ju,isd,intr,g,xm,xs,ym,ys, *xv,jerr) implicit double precision(a-h,o-z) double precision x(*),y(no),w(no),g(ni),xm(ni),xs(ni),xv(ni) integer ix(*),jx(*),ju(ni) jerr = jerr*1 w=w/sum(w) if(intr .ne. 0)goto 11221 ym=0.0 ys=sqrt(dot_product(w,y**2)-dot_product(w,y)**2) y=y/ys do 11231 j=1,ni if(ju(j).eq.0)goto 11231 xm(j)=0.0 jb=ix(j) je=ix(j+1)-1 xv(j)=dot_product(w(jx(jb:je)),x(jb:je)**2) if(isd .eq. 0)goto 11251 xbq=dot_product(w(jx(jb:je)),x(jb:je))**2 vc=xv(j)-xbq xs(j)=sqrt(vc) xv(j)=1.0+xbq/vc goto 11261 11251 continue xs(j)=1.0 11261 continue continue 11231 continue continue goto 11271 11221 continue do 11281 j=1,ni if(ju(j).eq.0)goto 11281 jb=ix(j) je=ix(j+1)-1 xm(j)=dot_product(w(jx(jb:je)),x(jb:je)) xv(j)=dot_product(w(jx(jb:je)),x(jb:je)**2)-xm(j)**2 if(isd.gt.0) xs(j)=sqrt(xv(j)) 11281 continue continue if(isd .ne. 0)goto 11301 xs=1.0 goto 11311 11301 continue xv=1.0 11311 continue continue ym=dot_product(w,y) y=y-ym ys=sqrt(dot_product(w,y**2)) y=y/ys 11271 continue continue g=0.0 do 11321 j=1,ni if(ju(j).eq.0)goto 11321 jb=ix(j) je=ix(j+1)-1 g(j)=dot_product(w(jx(jb:je))*y(jx(jb:je)),x(jb:je))/xs(j) 11321 continue continue return end subroutine spelnet1(beta,ni,g,no,w,ne,nx,x,ix,jx,ju,vp,cl,nlam,flm *in,ulam, thr,maxit,xm,xs,xv,lmu,ao,ia,kin,rsqo,almo,nlp,jerr) implicit double precision(a-h,o-z) double precision g(ni),vp(ni),x(*),ulam(nlam),w(no) double precision ao(nx,nlam),rsqo(nlam),almo(nlam) double precision xm(ni),xs(ni),xv(ni),cl(2,ni) integer ix(*),jx(*),ju(ni),ia(nx),kin(nlam) double precision, dimension (:), allocatable :: a,da integer, dimension (:), allocatable :: mm double precision, dimension (:,:), allocatable :: c allocate(c(1:ni,1:nx),stat=jerr) if(jerr.ne.0) return; call get_int_parms(sml,eps,big,mnlam,rsqmax,pmin,exmx,itrace) allocate(a(1:ni),stat=jerr) if(jerr.ne.0) return allocate(mm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(da(1:ni),stat=jerr) if(jerr.ne.0) return bta=beta omb=1.0-bta alm=0.0 alf=1.0 if(flmin .ge. 1.0)goto 11341 eqs=max(eps,flmin) alf=eqs**(1.0/(nlam-1)) 11341 continue rsq=0.0 a=0.0 mm=0 nlp=0 nin=nlp iz=0 mnl=min(mnlam,nlam) do 11351 m=1,nlam if(itrace.ne.0) call setpb(m-1) if(flmin .lt. 1.0)goto 11371 alm=ulam(m) goto 11361 11371 if(m .le. 2)goto 11381 alm=alm*alf goto 11361 11381 if(m .ne. 1)goto 11391 alm=big goto 11401 11391 continue alm=0.0 do 11411 j=1,ni if(ju(j).eq.0)goto 11411 if(vp(j).le.0.0)goto 11411 alm=max(alm,abs(g(j))/vp(j)) 11411 continue continue alm=alf*alm/max(bta,1.0d-3) 11401 continue 11361 continue dem=alm*omb ab=alm*bta rsq0=rsq jz=1 continue 11421 continue if(iz*jz.ne.0) go to 10360 nlp=nlp+1 dlx=0.0 do 11431 k=1,ni if(ju(k).eq.0)goto 11431 ak=a(k) u=g(k)+ak*xv(k) v=abs(u)-vp(k)*ab a(k)=0.0 if(v.gt.0.0) a(k)=max(cl(1,k),min(cl(2,k),sign(v,u)/(xv(k)+vp(k)*d *em))) if(a(k).eq.ak)goto 11431 if(mm(k) .ne. 0)goto 11451 nin=nin+1 if(nin.gt.nx)goto 11432 do 11461 j=1,ni if(ju(j).eq.0)goto 11461 if(mm(j) .eq. 0)goto 11481 c(j,nin)=c(k,mm(j)) goto 11461 11481 continue if(j .ne. k)goto 11501 c(j,nin)=xv(j) goto 11461 11501 continue c(j,nin)= (row_prod(j,k,ix,jx,x,w)-xm(j)*xm(k))/(xs(j)*xs(k)) 11461 continue continue mm(k)=nin ia(nin)=k 11451 continue del=a(k)-ak rsq=rsq+del*(2.0*g(k)-del*xv(k)) dlx=max(xv(k)*del**2,dlx) do 11511 j=1,ni if(ju(j).ne.0) g(j)=g(j)-c(j,mm(k))*del 11511 continue continue 11431 continue 11432 continue if(dlx.lt.thr)goto 11422 if(nin.gt.nx)goto 11422 if(nlp .le. maxit)goto 11531 jerr=-m return 11531 continue 10360 continue iz=1 da(1:nin)=a(ia(1:nin)) continue 11541 continue nlp=nlp+1 dlx=0.0 do 11551 l=1,nin k=ia(l) ak=a(k) u=g(k)+ak*xv(k) v=abs(u)-vp(k)*ab a(k)=0.0 if(v.gt.0.0) a(k)=max(cl(1,k),min(cl(2,k),sign(v,u)/(xv(k)+vp(k)*d *em))) if(a(k).eq.ak)goto 11551 del=a(k)-ak rsq=rsq+del*(2.0*g(k)-del*xv(k)) dlx=max(xv(k)*del**2,dlx) do 11561 j=1,nin g(ia(j))=g(ia(j))-c(ia(j),mm(k))*del 11561 continue continue 11551 continue continue if(dlx.lt.thr)goto 11542 if(nlp .le. maxit)goto 11581 jerr=-m return 11581 continue goto 11541 11542 continue da(1:nin)=a(ia(1:nin))-da(1:nin) do 11591 j=1,ni if(mm(j).ne.0)goto 11591 if(ju(j).ne.0) g(j)=g(j)-dot_product(da(1:nin),c(j,1:nin)) 11591 continue continue jz=0 goto 11421 11422 continue if(nin .le. nx)goto 11611 jerr=-10000-m goto 11352 11611 continue if(nin.gt.0) ao(1:nin,m)=a(ia(1:nin)) kin(m)=nin rsqo(m)=rsq almo(m)=alm lmu=m if(m.lt.mnl)goto 11351 if(flmin.ge.1.0)goto 11351 me=0 do 11621 j=1,nin if(ao(j,m).ne.0.0) me=me+1 11621 continue continue if(me.gt.ne)goto 11352 if(rsq-rsq0.lt.sml*rsq)goto 11352 if(rsq.gt.rsqmax)goto 11352 11351 continue 11352 continue deallocate(a,mm,c,da) return end subroutine spelnetn(parm,no,ni,x,ix,jx,y,w,jd,vp,cl,ne,nx,nlam,flm *in,ulam, thr,isd,intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr) implicit double precision(a-h,o-z) double precision x(*),vp(ni),y(no),w(no),ulam(nlam),cl(2,ni) double precision ca(nx,nlam),a0(nlam),rsq(nlam),alm(nlam) integer ix(*),jx(*),jd(*),ia(nx),nin(nlam) double precision, dimension (:), allocatable :: xm,xs,xv,vlam integer, dimension (:), allocatable :: ju allocate(xm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xs(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ju(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xv(1:ni),stat=jerr) if(jerr.ne.0) return allocate(vlam(1:nlam),stat=jerr) if(jerr.ne.0) return call spchkvars(no,ni,x,ix,ju) if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0 if(maxval(ju) .gt. 0)goto 11641 jerr=7777 return 11641 continue call spstandard1(no,ni,x,ix,jx,y,w,ju,isd,intr,xm,xs,ym,ys,xv,jerr *) if(jerr.ne.0) return cl=cl/ys if(isd .le. 0)goto 11661 do 11671 j=1,ni cl(:,j)=cl(:,j)*xs(j) 11671 continue continue 11661 continue if(flmin.ge.1.0) vlam=ulam/ys call spelnet2(parm,ni,y,w,no,ne,nx,x,ix,jx,ju,vp,cl,nlam,flmin,vla *m,thr,maxit, xm,xs,xv,lmu,ca,ia,nin,rsq,alm,nlp,jerr) if(jerr.gt.0) return do 11681 k=1,lmu alm(k)=ys*alm(k) nk=nin(k) do 11691 l=1,nk ca(l,k)=ys*ca(l,k)/xs(ia(l)) 11691 continue continue a0(k)=0.0 if(intr.ne.0) a0(k)=ym-dot_product(ca(1:nk,k),xm(ia(1:nk))) 11681 continue continue deallocate(xm,xs,ju,xv,vlam) return end subroutine spstandard1(no,ni,x,ix,jx,y,w,ju,isd,intr,xm,xs,ym,ys,x *v,jerr) implicit double precision(a-h,o-z) double precision x(*),y(no),w(no),xm(ni),xs(ni),xv(ni) integer ix(*),jx(*),ju(ni) jerr = jerr*1 w=w/sum(w) if(intr .ne. 0)goto 11711 ym=0.0 ys=sqrt(dot_product(w,y**2)-dot_product(w,y)**2) y=y/ys do 11721 j=1,ni if(ju(j).eq.0)goto 11721 xm(j)=0.0 jb=ix(j) je=ix(j+1)-1 xv(j)=dot_product(w(jx(jb:je)),x(jb:je)**2) if(isd .eq. 0)goto 11741 xbq=dot_product(w(jx(jb:je)),x(jb:je))**2 vc=xv(j)-xbq xs(j)=sqrt(vc) xv(j)=1.0+xbq/vc goto 11751 11741 continue xs(j)=1.0 11751 continue continue 11721 continue continue return 11711 continue do 11761 j=1,ni if(ju(j).eq.0)goto 11761 jb=ix(j) je=ix(j+1)-1 xm(j)=dot_product(w(jx(jb:je)),x(jb:je)) xv(j)=dot_product(w(jx(jb:je)),x(jb:je)**2)-xm(j)**2 if(isd.gt.0) xs(j)=sqrt(xv(j)) 11761 continue continue if(isd .ne. 0)goto 11781 xs=1.0 goto 11791 11781 continue xv=1.0 11791 continue continue ym=dot_product(w,y) y=y-ym ys=sqrt(dot_product(w,y**2)) y=y/ys return end subroutine spelnet2(beta,ni,y,w,no,ne,nx,x,ix,jx,ju,vp,cl,nlam,flm *in,ulam, thr,maxit,xm,xs,xv,lmu,ao,ia,kin,rsqo,almo,nlp,jerr) implicit double precision(a-h,o-z) double precision y(no),w(no),x(*),vp(ni),ulam(nlam),cl(2,ni) double precision ao(nx,nlam),rsqo(nlam),almo(nlam),xm(ni),xs(ni),x *v(ni) integer ix(*),jx(*),ju(ni),ia(nx),kin(nlam) double precision, dimension (:), allocatable :: a,g integer, dimension (:), allocatable :: mm,iy call get_int_parms(sml,eps,big,mnlam,rsqmax,pmin,exmx,itrace) allocate(a(1:ni),stat=jerr) if(jerr.ne.0) return allocate(mm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(g(1:ni),stat=jerr) if(jerr.ne.0) return allocate(iy(1:ni),stat=jerr) if(jerr.ne.0) return bta=beta omb=1.0-bta alm=0.0 iy=0 alf=1.0 if(flmin .ge. 1.0)goto 11811 eqs=max(eps,flmin) alf=eqs**(1.0/(nlam-1)) 11811 continue rsq=0.0 a=0.0 mm=0 o=0.0 nlp=0 nin=nlp iz=0 mnl=min(mnlam,nlam) do 11821 j=1,ni if(ju(j).eq.0)goto 11821 jb=ix(j) je=ix(j+1)-1 g(j)=abs(dot_product(y(jx(jb:je))+o,w(jx(jb:je))*x(jb:je))/xs(j)) 11821 continue continue do 11831 m=1,nlam if(itrace.ne.0) call setpb(m-1) alm0=alm if(flmin .lt. 1.0)goto 11851 alm=ulam(m) goto 11841 11851 if(m .le. 2)goto 11861 alm=alm*alf goto 11841 11861 if(m .ne. 1)goto 11871 alm=big goto 11881 11871 continue alm0=0.0 do 11891 j=1,ni if(ju(j).eq.0)goto 11891 if(vp(j).gt.0.0) alm0=max(alm0,g(j)/vp(j)) 11891 continue continue alm0=alm0/max(bta,1.0d-3) alm=alf*alm0 11881 continue 11841 continue dem=alm*omb ab=alm*bta rsq0=rsq jz=1 tlam=bta*(2.0*alm-alm0) do 11901 k=1,ni if(iy(k).eq.1)goto 11901 if(ju(k).eq.0)goto 11901 if(g(k).gt.tlam*vp(k)) iy(k)=1 11901 continue continue continue 11911 continue if(iz*jz.ne.0) go to 10360 10880 continue nlp=nlp+1 dlx=0.0 do 11921 k=1,ni if(iy(k).eq.0)goto 11921 jb=ix(k) je=ix(k+1)-1 gk=dot_product(y(jx(jb:je))+o,w(jx(jb:je))*x(jb:je))/xs(k) ak=a(k) u=gk+ak*xv(k) v=abs(u)-vp(k)*ab a(k)=0.0 if(v.gt.0.0) a(k)=max(cl(1,k),min(cl(2,k),sign(v,u)/(xv(k)+vp(k)*d *em))) if(a(k).eq.ak)goto 11921 if(mm(k) .ne. 0)goto 11941 nin=nin+1 if(nin.gt.nx)goto 11922 mm(k)=nin ia(nin)=k 11941 continue del=a(k)-ak rsq=rsq+del*(2.0*gk-del*xv(k)) y(jx(jb:je))=y(jx(jb:je))-del*x(jb:je)/xs(k) o=o+del*xm(k)/xs(k) dlx=max(xv(k)*del**2,dlx) 11921 continue 11922 continue if(nin.gt.nx)goto 11912 if(dlx .ge. thr)goto 11961 ixx=0 do 11971 j=1,ni if(iy(j).eq.1)goto 11971 if(ju(j).eq.0)goto 11971 jb=ix(j) je=ix(j+1)-1 g(j)=abs(dot_product(y(jx(jb:je))+o,w(jx(jb:je))*x(jb:je))/xs(j)) if(g(j) .le. ab*vp(j))goto 11991 iy(j)=1 ixx=1 11991 continue 11971 continue continue if(ixx.eq.1) go to 10880 goto 11912 11961 continue if(nlp .le. maxit)goto 12011 jerr=-m return 12011 continue 10360 continue iz=1 continue 12021 continue nlp=nlp+1 dlx=0.0 do 12031 l=1,nin k=ia(l) jb=ix(k) je=ix(k+1)-1 gk=dot_product(y(jx(jb:je))+o,w(jx(jb:je))*x(jb:je))/xs(k) ak=a(k) u=gk+ak*xv(k) v=abs(u)-vp(k)*ab a(k)=0.0 if(v.gt.0.0) a(k)=max(cl(1,k),min(cl(2,k),sign(v,u)/(xv(k)+vp(k)*d *em))) if(a(k).eq.ak)goto 12031 del=a(k)-ak rsq=rsq+del*(2.0*gk-del*xv(k)) y(jx(jb:je))=y(jx(jb:je))-del*x(jb:je)/xs(k) o=o+del*xm(k)/xs(k) dlx=max(xv(k)*del**2,dlx) 12031 continue continue if(dlx.lt.thr)goto 12022 if(nlp .le. maxit)goto 12051 jerr=-m return 12051 continue goto 12021 12022 continue jz=0 goto 11911 11912 continue if(nin .le. nx)goto 12071 jerr=-10000-m goto 11832 12071 continue if(nin.gt.0) ao(1:nin,m)=a(ia(1:nin)) kin(m)=nin rsqo(m)=rsq almo(m)=alm lmu=m if(m.lt.mnl)goto 11831 if(flmin.ge.1.0)goto 11831 me=0 do 12081 j=1,nin if(ao(j,m).ne.0.0) me=me+1 12081 continue continue if(me.gt.ne)goto 11832 if(rsq-rsq0.lt.sml*rsq)goto 11832 if(rsq.gt.rsqmax)goto 11832 11831 continue 11832 continue deallocate(a,mm,g,iy) return end subroutine spchkvars(no,ni,x,ix,ju) implicit double precision(a-h,o-z) double precision x(*) integer ix(*),ju(ni) do 12091 j=1,ni ju(j)=0 jb=ix(j) nj=ix(j+1)-jb if(nj.eq.0)goto 12091 je=ix(j+1)-1 if(nj .ge. no)goto 12111 do 12121 i=jb,je if(x(i).eq.0.0)goto 12121 ju(j)=1 goto 12122 12121 continue 12122 continue goto 12131 12111 continue t=x(jb) do 12141 i=jb+1,je if(x(i).eq.t)goto 12141 ju(j)=1 goto 12142 12141 continue 12142 continue 12131 continue continue 12091 continue continue return end subroutine cmodval(a0,ca,ia,nin,x,ix,jx,n,f) implicit double precision(a-h,o-z) double precision ca(*),x(*),f(n) integer ia(*),ix(*),jx(*) f=a0 do 12151 j=1,nin k=ia(j) kb=ix(k) ke=ix(k+1)-1 f(jx(kb:ke))=f(jx(kb:ke))+ca(j)*x(kb:ke) 12151 continue continue return end function row_prod(i,j,ia,ja,ra,w) implicit double precision(a-h,o-z) integer ia(*),ja(*) double precision ra(*),w(*) row_prod=dot(ra(ia(i)),ra(ia(j)),ja(ia(i)),ja(ia(j)), ia(i+1)-ia( *i),ia(j+1)-ia(j),w) return end function dot(x,y,mx,my,nx,ny,w) implicit double precision(a-h,o-z) double precision x(*),y(*),w(*) integer mx(*),my(*) i=1 j=i s=0.0 continue 12161 continue continue 12171 if(mx(i).ge.my(j))goto 12172 i=i+1 if(i.gt.nx) go to 12180 goto 12171 12172 continue if(mx(i).eq.my(j)) go to 12190 continue 12201 if(my(j).ge.mx(i))goto 12202 j=j+1 if(j.gt.ny) go to 12180 goto 12201 12202 continue if(mx(i).eq.my(j)) go to 12190 goto 12161 12190 continue s=s+w(mx(i))*x(i)*y(j) i=i+1 if(i.gt.nx)goto 12162 j=j+1 if(j.gt.ny)goto 12162 goto 12161 12162 continue 12180 continue dot=s return end subroutine lognet(parm,no,ni,nc,x,y,g,jd,vp,cl,ne,nx,nlam,flmin,ul *am,thr, isd,intr,maxit,kopt,lmu,a0,ca,ia,nin,dev0,dev,alm,nlp,jer *r) implicit double precision(a-h,o-z) double precision x(no,ni),y(no,max(2,nc)),g(no,nc),vp(ni),ulam(nla *m) double precision ca(nx,nc,nlam),a0(nc,nlam),dev(nlam),alm(nlam),cl *(2,ni) integer jd(*),ia(nx),nin(nlam) double precision, dimension (:), allocatable :: xm,xs,ww,vq,xv integer, dimension (:), allocatable :: ju if(maxval(vp) .gt. 0.0)goto 12221 jerr=10000 return 12221 continue allocate(ww(1:no),stat=jerr) if(jerr.ne.0) return allocate(ju(1:ni),stat=jerr) if(jerr.ne.0) return allocate(vq(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xm(1:ni),stat=jerr) if(jerr.ne.0) return if(kopt .ne. 2)goto 12241 allocate(xv(1:ni),stat=jerr) if(jerr.ne.0) return 12241 continue if(isd .le. 0)goto 12261 allocate(xs(1:ni),stat=jerr) if(jerr.ne.0) return 12261 continue call chkvars(no,ni,x,ju) if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0 if(maxval(ju) .gt. 0)goto 12281 jerr=7777 return 12281 continue vq=max(0d0,vp) vq=vq*ni/sum(vq) do 12291 i=1,no ww(i)=sum(y(i,:)) if(ww(i).gt.0.0) y(i,:)=y(i,:)/ww(i) 12291 continue continue sw=sum(ww) ww=ww/sw if(nc .ne. 1)goto 12311 call lstandard1(no,ni,x,ww,ju,isd,intr,xm,xs) if(isd .le. 0)goto 12331 do 12341 j=1,ni cl(:,j)=cl(:,j)*xs(j) 12341 continue continue 12331 continue call lognet2n(parm,no,ni,x,y(:,1),g(:,1),ww,ju,vq,cl,ne,nx,nlam,fl *min,ulam, thr,isd,intr,maxit,kopt,lmu,a0,ca,ia,nin,dev0,dev,alm,n *lp,jerr) goto 12301 12311 if(kopt .ne. 2)goto 12351 call multlstandard1(no,ni,x,ww,ju,isd,intr,xm,xs,xv) if(isd .le. 0)goto 12371 do 12381 j=1,ni cl(:,j)=cl(:,j)*xs(j) 12381 continue continue 12371 continue call multlognetn(parm,no,ni,nc,x,y,g,ww,ju,vq,cl,ne,nx,nlam,flmin, *ulam,thr, intr,maxit,xv,lmu,a0,ca,ia,nin,dev0,dev,alm,nlp,jerr) goto 12391 12351 continue call lstandard1(no,ni,x,ww,ju,isd,intr,xm,xs) if(isd .le. 0)goto 12411 do 12421 j=1,ni cl(:,j)=cl(:,j)*xs(j) 12421 continue continue 12411 continue call lognetn(parm,no,ni,nc,x,y,g,ww,ju,vq,cl,ne,nx,nlam,flmin,ulam *,thr, isd,intr,maxit,kopt,lmu,a0,ca,ia,nin,dev0,dev,alm,nlp,jerr) 12391 continue 12301 continue if(jerr.gt.0) return dev0=2.0*sw*dev0 do 12431 k=1,lmu nk=nin(k) do 12441 ic=1,nc if(isd .le. 0)goto 12461 do 12471 l=1,nk ca(l,ic,k)=ca(l,ic,k)/xs(ia(l)) 12471 continue continue 12461 continue if(intr .ne. 0)goto 12491 a0(ic,k)=0.0 goto 12501 12491 continue a0(ic,k)=a0(ic,k)-dot_product(ca(1:nk,ic,k),xm(ia(1:nk))) 12501 continue continue 12441 continue continue 12431 continue continue deallocate(ww,ju,vq,xm) if(isd.gt.0) deallocate(xs) if(kopt.eq.2) deallocate(xv) return end subroutine lstandard1(no,ni,x,w,ju,isd,intr,xm,xs) implicit double precision(a-h,o-z) double precision x(no,ni),w(no),xm(ni),xs(ni) integer ju(ni) if(intr .ne. 0)goto 12521 do 12531 j=1,ni if(ju(j).eq.0)goto 12531 xm(j)=0.0 if(isd .eq. 0)goto 12551 vc=dot_product(w,x(:,j)**2)-dot_product(w,x(:,j))**2 xs(j)=sqrt(vc) x(:,j)=x(:,j)/xs(j) 12551 continue 12531 continue continue return 12521 continue do 12561 j=1,ni if(ju(j).eq.0)goto 12561 xm(j)=dot_product(w,x(:,j)) x(:,j)=x(:,j)-xm(j) if(isd .le. 0)goto 12581 xs(j)=sqrt(dot_product(w,x(:,j)**2)) x(:,j)=x(:,j)/xs(j) 12581 continue 12561 continue continue return end subroutine multlstandard1(no,ni,x,w,ju,isd,intr,xm,xs,xv) implicit double precision(a-h,o-z) double precision x(no,ni),w(no),xm(ni),xs(ni),xv(ni) integer ju(ni) if(intr .ne. 0)goto 12601 do 12611 j=1,ni if(ju(j).eq.0)goto 12611 xm(j)=0.0 xv(j)=dot_product(w,x(:,j)**2) if(isd .eq. 0)goto 12631 xbq=dot_product(w,x(:,j))**2 vc=xv(j)-xbq xs(j)=sqrt(vc) x(:,j)=x(:,j)/xs(j) xv(j)=1.0+xbq/vc 12631 continue 12611 continue continue return 12601 continue do 12641 j=1,ni if(ju(j).eq.0)goto 12641 xm(j)=dot_product(w,x(:,j)) x(:,j)=x(:,j)-xm(j) xv(j)=dot_product(w,x(:,j)**2) if(isd .le. 0)goto 12661 xs(j)=sqrt(xv(j)) x(:,j)=x(:,j)/xs(j) xv(j)=1.0 12661 continue 12641 continue continue return end subroutine lognet2n(parm,no,ni,x,y,g,w,ju,vp,cl,ne,nx,nlam,flmin,u *lam,shri, isd,intr,maxit,kopt,lmu,a0,a,m,kin,dev0,dev,alm,nlp,jer *r) implicit double precision(a-h,o-z) double precision x(no,ni),y(no),g(no),w(no),vp(ni),ulam(nlam),cl(2 *,ni) double precision a(nx,nlam),a0(nlam),dev(nlam),alm(nlam) integer ju(ni),m(nx),kin(nlam) double precision, dimension (:), allocatable :: b,bs,v,r,xv,q,ga integer, dimension (:), allocatable :: mm,ixx call get_int_parms(sml,eps,big,mnlam,devmax,pmin,exmx,itrace) allocate(b(0:ni),stat=jerr) if(jerr.ne.0) return allocate(xv(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ga(1:ni),stat=jerr) if(jerr.ne.0) return allocate(bs(0:ni),stat=jerr) if(jerr.ne.0) return allocate(mm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ixx(1:ni),stat=jerr) if(jerr.ne.0) return allocate(r(1:no),stat=jerr) if(jerr.ne.0) return allocate(v(1:no),stat=jerr) if(jerr.ne.0) return allocate(q(1:no),stat=jerr) if(jerr.ne.0) return fmax=log(1.0/pmin-1.0) fmin=-fmax vmin=(1.0+pmin)*pmin*(1.0-pmin) bta=parm omb=1.0-bta q0=dot_product(w,y) if(q0 .gt. pmin)goto 12681 jerr=8001 return 12681 continue if(q0 .lt. 1.0-pmin)goto 12701 jerr=9001 return 12701 continue if(intr.eq.0.0) q0=0.5 ixx=0 al=0.0 bz=0.0 if(intr.ne.0) bz=log(q0/(1.0-q0)) if(nonzero(no,g) .ne. 0)goto 12721 vi=q0*(1.0-q0) b(0)=bz v=vi*w r=w*(y-q0) q=q0 xmz=vi dev1=-(bz*q0+log(1.0-q0)) goto 12731 12721 continue b(0)=0.0 if(intr .eq. 0)goto 12751 b(0)=azero(no,y,g,w,jerr) if(jerr.ne.0) return 12751 continue q=1.0/(1.0+exp(-b(0)-g)) v=w*q*(1.0-q) r=w*(y-q) xmz=sum(v) dev1=-(b(0)*q0+dot_product(w,y*g+log(1.0-q))) 12731 continue continue if(kopt .le. 0)goto 12771 if(isd .le. 0 .or. intr .eq. 0)goto 12791 xv=0.25 goto 12801 12791 continue do 12811 j=1,ni if(ju(j).ne.0) xv(j)=0.25*dot_product(w,x(:,j)**2) 12811 continue continue 12801 continue continue 12771 continue dev0=dev1 do 12821 i=1,no if(y(i).gt.0.0) dev0=dev0+w(i)*y(i)*log(y(i)) if(y(i).lt.1.0) dev0=dev0+w(i)*(1.0-y(i))*log(1.0-y(i)) 12821 continue continue alf=1.0 if(flmin .ge. 1.0)goto 12841 eqs=max(eps,flmin) alf=eqs**(1.0/(nlam-1)) 12841 continue m=0 mm=0 nlp=0 nin=nlp mnl=min(mnlam,nlam) bs=0.0 b(1:ni)=0.0 shr=shri*dev0 do 12851 j=1,ni if(ju(j).eq.0)goto 12851 ga(j)=abs(dot_product(r,x(:,j))) 12851 continue continue do 12861 ilm=1,nlam if(itrace.ne.0) call setpb(ilm-1) al0=al if(flmin .lt. 1.0)goto 12881 al=ulam(ilm) goto 12871 12881 if(ilm .le. 2)goto 12891 al=al*alf goto 12871 12891 if(ilm .ne. 1)goto 12901 al=big goto 12911 12901 continue al0=0.0 do 12921 j=1,ni if(ju(j).eq.0)goto 12921 if(vp(j).gt.0.0) al0=max(al0,ga(j)/vp(j)) 12921 continue continue al0=al0/max(bta,1.0d-3) al=alf*al0 12911 continue 12871 continue al2=al*omb al1=al*bta tlam=bta*(2.0*al-al0) do 12931 k=1,ni if(ixx(k).eq.1)goto 12931 if(ju(k).eq.0)goto 12931 if(ga(k).gt.tlam*vp(k)) ixx(k)=1 12931 continue continue 10880 continue continue 12941 continue bs(0)=b(0) if(nin.gt.0) bs(m(1:nin))=b(m(1:nin)) if(kopt .ne. 0)goto 12961 do 12971 j=1,ni if(ixx(j).gt.0) xv(j)=dot_product(v,x(:,j)**2) 12971 continue continue 12961 continue continue 12981 continue nlp=nlp+1 dlx=0.0 do 12991 k=1,ni if(ixx(k).eq.0)goto 12991 bk=b(k) gk=dot_product(r,x(:,k)) u=gk+xv(k)*b(k) au=abs(u)-vp(k)*al1 if(au .gt. 0.0)goto 13011 b(k)=0.0 goto 13021 13011 continue b(k)=max(cl(1,k),min(cl(2,k),sign(au,u)/(xv(k)+vp(k)*al2))) 13021 continue continue d=b(k)-bk if(abs(d).le.0.0)goto 12991 dlx=max(dlx,xv(k)*d**2) r=r-d*v*x(:,k) if(mm(k) .ne. 0)goto 13041 nin=nin+1 if(nin.gt.nx)goto 12992 mm(k)=nin m(nin)=k 13041 continue 12991 continue 12992 continue if(nin.gt.nx)goto 12982 d=0.0 if(intr.ne.0) d=sum(r)/xmz if(d .eq. 0.0)goto 13061 b(0)=b(0)+d dlx=max(dlx,xmz*d**2) r=r-d*v 13061 continue if(dlx.lt.shr)goto 12982 if(nlp .le. maxit)goto 13081 jerr=-ilm return 13081 continue continue 13091 continue nlp=nlp+1 dlx=0.0 do 13101 l=1,nin k=m(l) bk=b(k) gk=dot_product(r,x(:,k)) u=gk+xv(k)*b(k) au=abs(u)-vp(k)*al1 if(au .gt. 0.0)goto 13121 b(k)=0.0 goto 13131 13121 continue b(k)=max(cl(1,k),min(cl(2,k),sign(au,u)/(xv(k)+vp(k)*al2))) 13131 continue continue d=b(k)-bk if(abs(d).le.0.0)goto 13101 dlx=max(dlx,xv(k)*d**2) r=r-d*v*x(:,k) 13101 continue continue d=0.0 if(intr.ne.0) d=sum(r)/xmz if(d .eq. 0.0)goto 13151 b(0)=b(0)+d dlx=max(dlx,xmz*d**2) r=r-d*v 13151 continue if(dlx.lt.shr)goto 13092 if(nlp .le. maxit)goto 13171 jerr=-ilm return 13171 continue goto 13091 13092 continue goto 12981 12982 continue if(nin.gt.nx)goto 12942 do 13181 i=1,no fi=b(0)+g(i) if(nin.gt.0) fi=fi+dot_product(b(m(1:nin)),x(i,m(1:nin))) if(fi .ge. fmin)goto 13201 q(i)=0.0 goto 13191 13201 if(fi .le. fmax)goto 13211 q(i)=1.0 goto 13221 13211 continue q(i)=1.0/(1.0+exp(-fi)) 13221 continue 13191 continue 13181 continue continue v=w*q*(1.0-q) xmz=sum(v) if(xmz.le.vmin)goto 12942 r=w*(y-q) if(xmz*(b(0)-bs(0))**2 .ge. shr)goto 13241 ix=0 do 13251 j=1,nin k=m(j) if(xv(k)*(b(k)-bs(k))**2.lt.shr)goto 13251 ix=1 goto 13252 13251 continue 13252 continue if(ix .ne. 0)goto 13271 do 13281 k=1,ni if(ixx(k).eq.1)goto 13281 if(ju(k).eq.0)goto 13281 ga(k)=abs(dot_product(r,x(:,k))) if(ga(k) .le. al1*vp(k))goto 13301 ixx(k)=1 ix=1 13301 continue 13281 continue continue if(ix.eq.1) go to 10880 goto 12942 13271 continue 13241 continue goto 12941 12942 continue if(nin .le. nx)goto 13321 jerr=-10000-ilm goto 12862 13321 continue if(nin.gt.0) a(1:nin,ilm)=b(m(1:nin)) kin(ilm)=nin a0(ilm)=b(0) alm(ilm)=al lmu=ilm devi=dev2(no,w,y,q,pmin) dev(ilm)=(dev1-devi)/dev0 if(xmz.le.vmin)goto 12862 if(ilm.lt.mnl)goto 12861 if(flmin.ge.1.0)goto 12861 me=0 do 13331 j=1,nin if(a(j,ilm).ne.0.0) me=me+1 13331 continue continue if(me.gt.ne)goto 12862 if(dev(ilm).gt.devmax)goto 12862 if(dev(ilm)-dev(ilm-1).lt.sml)goto 12862 12861 continue 12862 continue g=log(q/(1.0-q)) deallocate(b,bs,v,r,xv,q,mm,ga,ixx) return end function dev2(n,w,y,p,pmin) implicit double precision(a-h,o-z) double precision w(n),y(n),p(n) pmax=1.0-pmin s=0.0 do 13341 i=1,n pi=min(max(pmin,p(i)),pmax) s=s-w(i)*(y(i)*log(pi)+(1.0-y(i))*log(1.0-pi)) 13341 continue continue dev2=s return end function azero(n,y,g,q,jerr) implicit double precision(a-h,o-z) parameter(eps=1.0d-7) double precision y(n),g(n),q(n) double precision, dimension (:), allocatable :: e,p,w azero = 0.0 allocate(e(1:n),stat=jerr) if(jerr.ne.0) return allocate(p(1:n),stat=jerr) if(jerr.ne.0) return allocate(w(1:n),stat=jerr) if(jerr.ne.0) return az=0.0 e=exp(-g) qy=dot_product(q,y) p=1.0/(1.0+e) continue 13351 continue w=q*p*(1.0-p) d=(qy-dot_product(q,p))/sum(w) az=az+d if(abs(d).lt.eps)goto 13352 ea0=exp(-az) p=1.0/(1.0+ea0*e) goto 13351 13352 continue azero=az deallocate(e,p,w) return end subroutine lognetn(parm,no,ni,nc,x,y,g,w,ju,vp,cl,ne,nx,nlam,flmin *,ulam,shri, isd,intr,maxit,kopt,lmu,a0,a,m,kin,dev0,dev,alm,nlp,j *err) implicit double precision(a-h,o-z) double precision x(no,ni),y(no,nc),g(no,nc),w(no),vp(ni),ulam(nlam *) double precision a(nx,nc,nlam),a0(nc,nlam),dev(nlam),alm(nlam),cl( *2,ni) integer ju(ni),m(nx),kin(nlam) double precision, dimension (:,:), allocatable :: q double precision, dimension (:), allocatable :: sxp,sxpl double precision, dimension (:), allocatable :: di,v,r,ga double precision, dimension (:,:), allocatable :: b,bs,xv integer, dimension (:), allocatable :: mm,is,ixx allocate(b(0:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(xv(1:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(bs(0:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(q(1:no,1:nc),stat=jerr) if(jerr.ne.0) return call get_int_parms(sml,eps,big,mnlam,devmax,pmin,exmx,itrace) exmn=-exmx allocate(r(1:no),stat=jerr) if(jerr.ne.0) return allocate(v(1:no),stat=jerr) if(jerr.ne.0) return allocate(mm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(is(1:max(nc,ni)),stat=jerr) if(jerr.ne.0) return allocate(sxp(1:no),stat=jerr) if(jerr.ne.0) return allocate(sxpl(1:no),stat=jerr) if(jerr.ne.0) return allocate(di(1:no),stat=jerr) if(jerr.ne.0) return allocate(ga(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ixx(1:ni),stat=jerr) if(jerr.ne.0) return pmax=1.0-pmin emin=pmin/pmax emax=1.0/emin pfm=(1.0+pmin)*pmin pfx=(1.0-pmin)*pmax vmin=pfm*pmax bta=parm omb=1.0-bta dev1=0.0 dev0=0.0 do 13361 ic=1,nc q0=dot_product(w,y(:,ic)) if(q0 .gt. pmin)goto 13381 jerr =8000+ic return 13381 continue if(q0 .lt. 1.0-pmin)goto 13401 jerr =9000+ic return 13401 continue if(intr .ne. 0)goto 13421 q0=1.0/nc b(0,ic)=0.0 goto 13431 13421 continue b(0,ic)=log(q0) dev1=dev1-q0*b(0,ic) 13431 continue continue b(1:ni,ic)=0.0 13361 continue continue if(intr.eq.0) dev1=log(float(nc)) ixx=0 al=0.0 if(nonzero(no*nc,g) .ne. 0)goto 13451 b(0,:)=b(0,:)-sum(b(0,:))/nc sxp=0.0 do 13461 ic=1,nc q(:,ic)=exp(b(0,ic)) sxp=sxp+q(:,ic) 13461 continue continue goto 13471 13451 continue do 13481 i=1,no g(i,:)=g(i,:)-sum(g(i,:))/nc 13481 continue continue sxp=0.0 if(intr .ne. 0)goto 13501 b(0,:)=0.0 goto 13511 13501 continue call kazero(nc,no,y,g,w,b(0,:),jerr) if(jerr.ne.0) return 13511 continue continue dev1=0.0 do 13521 ic=1,nc q(:,ic)=b(0,ic)+g(:,ic) dev1=dev1-dot_product(w,y(:,ic)*q(:,ic)) q(:,ic)=exp(q(:,ic)) sxp=sxp+q(:,ic) 13521 continue continue sxpl=w*log(sxp) do 13531 ic=1,nc dev1=dev1+dot_product(y(:,ic),sxpl) 13531 continue continue 13471 continue continue do 13541 ic=1,nc do 13551 i=1,no if(y(i,ic).gt.0.0) dev0=dev0+w(i)*y(i,ic)*log(y(i,ic)) 13551 continue continue 13541 continue continue dev0=dev0+dev1 if(kopt .le. 0)goto 13571 if(isd .le. 0 .or. intr .eq. 0)goto 13591 xv=0.25 goto 13601 13591 continue do 13611 j=1,ni if(ju(j).ne.0) xv(j,:)=0.25*dot_product(w,x(:,j)**2) 13611 continue continue 13601 continue continue 13571 continue alf=1.0 if(flmin .ge. 1.0)goto 13631 eqs=max(eps,flmin) alf=eqs**(1.0/(nlam-1)) 13631 continue m=0 mm=0 nin=0 nlp=0 mnl=min(mnlam,nlam) bs=0.0 shr=shri*dev0 ga=0.0 do 13641 ic=1,nc r=w*(y(:,ic)-q(:,ic)/sxp) do 13651 j=1,ni if(ju(j).ne.0) ga(j)=max(ga(j),abs(dot_product(r,x(:,j)))) 13651 continue continue 13641 continue continue do 13661 ilm=1,nlam if(itrace.ne.0) call setpb(ilm-1) al0=al if(flmin .lt. 1.0)goto 13681 al=ulam(ilm) goto 13671 13681 if(ilm .le. 2)goto 13691 al=al*alf goto 13671 13691 if(ilm .ne. 1)goto 13701 al=big goto 13711 13701 continue al0=0.0 do 13721 j=1,ni if(ju(j).eq.0)goto 13721 if(vp(j).gt.0.0) al0=max(al0,ga(j)/vp(j)) 13721 continue continue al0=al0/max(bta,1.0d-3) al=alf*al0 13711 continue 13671 continue al2=al*omb al1=al*bta tlam=bta*(2.0*al-al0) do 13731 k=1,ni if(ixx(k).eq.1)goto 13731 if(ju(k).eq.0)goto 13731 if(ga(k).gt.tlam*vp(k)) ixx(k)=1 13731 continue continue 10880 continue continue 13741 continue ix=0 jx=ix ig=0 do 13751 ic=1,nc bs(0,ic)=b(0,ic) if(nin.gt.0) bs(m(1:nin),ic)=b(m(1:nin),ic) xmz=0.0 do 13761 i=1,no pic=q(i,ic)/sxp(i) if(pic .ge. pfm)goto 13781 pic=0.0 v(i)=0.0 goto 13771 13781 if(pic .le. pfx)goto 13791 pic=1.0 v(i)=0.0 goto 13801 13791 continue v(i)=w(i)*pic*(1.0-pic) xmz=xmz+v(i) 13801 continue 13771 continue r(i)=w(i)*(y(i,ic)-pic) 13761 continue continue if(xmz.le.vmin)goto 13751 ig=1 if(kopt .ne. 0)goto 13821 do 13831 j=1,ni if(ixx(j).gt.0) xv(j,ic)=dot_product(v,x(:,j)**2) 13831 continue continue 13821 continue continue 13841 continue nlp=nlp+1 dlx=0.0 do 13851 k=1,ni if(ixx(k).eq.0)goto 13851 bk=b(k,ic) gk=dot_product(r,x(:,k)) u=gk+xv(k,ic)*b(k,ic) au=abs(u)-vp(k)*al1 if(au .gt. 0.0)goto 13871 b(k,ic)=0.0 goto 13881 13871 continue b(k,ic)=max(cl(1,k),min(cl(2,k),sign(au,u)/ (xv(k,ic)+vp(k)*al2)) *) 13881 continue continue d=b(k,ic)-bk if(abs(d).le.0.0)goto 13851 dlx=max(dlx,xv(k,ic)*d**2) r=r-d*v*x(:,k) if(mm(k) .ne. 0)goto 13901 nin=nin+1 if(nin .le. nx)goto 13921 jx=1 goto 13852 13921 continue mm(k)=nin m(nin)=k 13901 continue 13851 continue 13852 continue if(jx.gt.0)goto 13842 d=0.0 if(intr.ne.0) d=sum(r)/xmz if(d .eq. 0.0)goto 13941 b(0,ic)=b(0,ic)+d dlx=max(dlx,xmz*d**2) r=r-d*v 13941 continue if(dlx.lt.shr)goto 13842 if(nlp .le. maxit)goto 13961 jerr=-ilm return 13961 continue continue 13971 continue nlp=nlp+1 dlx=0.0 do 13981 l=1,nin k=m(l) bk=b(k,ic) gk=dot_product(r,x(:,k)) u=gk+xv(k,ic)*b(k,ic) au=abs(u)-vp(k)*al1 if(au .gt. 0.0)goto 14001 b(k,ic)=0.0 goto 14011 14001 continue b(k,ic)=max(cl(1,k),min(cl(2,k),sign(au,u)/ (xv(k,ic)+vp(k)*al2)) *) 14011 continue continue d=b(k,ic)-bk if(abs(d).le.0.0)goto 13981 dlx=max(dlx,xv(k,ic)*d**2) r=r-d*v*x(:,k) 13981 continue continue d=0.0 if(intr.ne.0) d=sum(r)/xmz if(d .eq. 0.0)goto 14031 b(0,ic)=b(0,ic)+d dlx=max(dlx,xmz*d**2) r=r-d*v 14031 continue if(dlx.lt.shr)goto 13972 if(nlp .le. maxit)goto 14051 jerr=-ilm return 14051 continue goto 13971 13972 continue goto 13841 13842 continue if(jx.gt.0)goto 13752 if(xmz*(b(0,ic)-bs(0,ic))**2.gt.shr) ix=1 if(ix .ne. 0)goto 14071 do 14081 j=1,nin k=m(j) if(xv(k,ic)*(b(k,ic)-bs(k,ic))**2 .le. shr)goto 14101 ix=1 goto 14082 14101 continue 14081 continue 14082 continue 14071 continue do 14111 i=1,no fi=b(0,ic)+g(i,ic) if(nin.gt.0) fi=fi+dot_product(b(m(1:nin),ic),x(i,m(1:nin))) fi=min(max(exmn,fi),exmx) sxp(i)=sxp(i)-q(i,ic) q(i,ic)=min(max(emin*sxp(i),exp(fi)),emax*sxp(i)) sxp(i)=sxp(i)+q(i,ic) 14111 continue continue 13751 continue 13752 continue s=-sum(b(0,:))/nc b(0,:)=b(0,:)+s di=s do 14121 j=1,nin l=m(j) if(vp(l) .gt. 0.0)goto 14141 s=sum(b(l,:))/nc goto 14151 14141 continue s=elc(parm,nc,cl(:,l),b(l,:),is) 14151 continue continue b(l,:)=b(l,:)-s di=di-s*x(:,l) 14121 continue continue di=exp(di) sxp=sxp*di do 14161 ic=1,nc q(:,ic)=q(:,ic)*di 14161 continue continue if(jx.gt.0)goto 13742 if(ig.eq.0)goto 13742 if(ix .ne. 0)goto 14181 do 14191 k=1,ni if(ixx(k).eq.1)goto 14191 if(ju(k).eq.0)goto 14191 ga(k)=0.0 14191 continue continue do 14201 ic=1,nc r=w*(y(:,ic)-q(:,ic)/sxp) do 14211 k=1,ni if(ixx(k).eq.1)goto 14211 if(ju(k).eq.0)goto 14211 ga(k)=max(ga(k),abs(dot_product(r,x(:,k)))) 14211 continue continue 14201 continue continue do 14221 k=1,ni if(ixx(k).eq.1)goto 14221 if(ju(k).eq.0)goto 14221 if(ga(k) .le. al1*vp(k))goto 14241 ixx(k)=1 ix=1 14241 continue 14221 continue continue if(ix.eq.1) go to 10880 goto 13742 14181 continue goto 13741 13742 continue if(jx .le. 0)goto 14261 jerr=-10000-ilm goto 13662 14261 continue devi=0.0 do 14271 ic=1,nc if(nin.gt.0) a(1:nin,ic,ilm)=b(m(1:nin),ic) a0(ic,ilm)=b(0,ic) do 14281 i=1,no if(y(i,ic).le.0.0)goto 14281 devi=devi-w(i)*y(i,ic)*log(q(i,ic)/sxp(i)) 14281 continue continue 14271 continue continue kin(ilm)=nin alm(ilm)=al lmu=ilm dev(ilm)=(dev1-devi)/dev0 if(ig.eq.0)goto 13662 if(ilm.lt.mnl)goto 13661 if(flmin.ge.1.0)goto 13661 if(nintot(ni,nx,nc,a(1,1,ilm),m,nin,is).gt.ne)goto 13662 if(dev(ilm).gt.devmax)goto 13662 if(dev(ilm)-dev(ilm-1).lt.sml)goto 13662 13661 continue 13662 continue g=log(q) do 14291 i=1,no g(i,:)=g(i,:)-sum(g(i,:))/nc 14291 continue continue deallocate(sxp,b,bs,v,r,xv,q,mm,is,ga,ixx) return end subroutine kazero(kk,n,y,g,q,az,jerr) implicit double precision(a-h,o-z) parameter(eps=1.0d-7) double precision y(n,kk),g(n,kk),q(n),az(kk) double precision, dimension (:), allocatable :: s double precision, dimension (:,:), allocatable :: e allocate(e(1:n,1:kk),stat=jerr) if(jerr.ne.0) return allocate(s(1:n),stat=jerr) if(jerr.ne.0) return az=0.0 e=exp(g) do 14301 i=1,n s(i)=sum(e(i,:)) 14301 continue continue continue 14311 continue dm=0.0 do 14321 k=1,kk t=0.0 u=t do 14331 i=1,n pik=e(i,k)/s(i) t=t+q(i)*(y(i,k)-pik) u=u+q(i)*pik*(1.0-pik) 14331 continue continue d=t/u az(k)=az(k)+d ed=exp(d) dm=max(dm,abs(d)) do 14341 i=1,n z=e(i,k) e(i,k)=z*ed s(i)=s(i)-z+e(i,k) 14341 continue continue 14321 continue continue if(dm.lt.eps)goto 14312 goto 14311 14312 continue az=az-sum(az)/kk deallocate(e,s) return end function elc(parm,n,cl,a,m) implicit double precision(a-h,o-z) double precision a(n),cl(2) integer m(n) fn=n am=sum(a)/fn if((parm .ne. 0.0) .and. (n .ne. 2))goto 14361 elc=am go to 14370 14361 continue do 14381 i=1,n m(i)=i 14381 continue continue call psort7(a,m,1,n) if(a(m(1)) .ne. a(m(n)))goto 14401 elc=a(1) go to 14370 14401 continue if(mod(n,2) .ne. 1)goto 14421 ad=a(m(n/2+1)) goto 14431 14421 continue ad=0.5*(a(m(n/2+1))+a(m(n/2))) 14431 continue continue if(parm .ne. 1.0)goto 14451 elc=ad go to 14370 14451 continue b1=min(am,ad) b2=max(am,ad) k2=1 continue 14461 if(a(m(k2)).gt.b1)goto 14462 k2=k2+1 goto 14461 14462 continue k1=k2-1 continue 14471 if(a(m(k2)).ge.b2)goto 14472 k2=k2+1 goto 14471 14472 continue r=parm/((1.0-parm)*fn) is=0 sm=n-2*(k1-1) do 14481 k=k1,k2-1 sm=sm-2.0 s=r*sm+am if(s .le. a(m(k)) .or. s .gt. a(m(k+1)))goto 14501 is=k goto 14482 14501 continue 14481 continue 14482 continue if(is .eq. 0)goto 14521 elc=s go to 14370 14521 continue r2=2.0*r s1=a(m(k1)) am2=2.0*am cri=r2*sum(abs(a-s1))+s1*(s1-am2) elc=s1 do 14531 k=k1+1,k2 s=a(m(k)) if(s.eq.s1)goto 14531 c=r2*sum(abs(a-s))+s*(s-am2) if(c .ge. cri)goto 14551 cri=c elc=s 14551 continue s1=s 14531 continue continue 14370 continue elc=max(maxval(a-cl(2)),min(minval(a-cl(1)),elc)) return end function nintot(ni,nx,nc,a,m,nin,is) implicit double precision(a-h,o-z) double precision a(nx,nc) integer m(nx),is(ni) is=0 nintot=0 do 14561 ic=1,nc do 14571 j=1,nin k=m(j) if(is(k).ne.0)goto 14571 if(a(j,ic).eq.0.0)goto 14571 is(k)=k nintot=nintot+1 14571 continue continue 14561 continue continue return end subroutine luncomp(ni,nx,nc,ca,ia,nin,a) implicit double precision(a-h,o-z) double precision ca(nx,nc),a(ni,nc) integer ia(nx) a=0.0 do 14581 ic=1,nc if(nin.gt.0) a(ia(1:nin),ic)=ca(1:nin,ic) 14581 continue continue return end subroutine lmodval(nt,x,nc,nx,a0,ca,ia,nin,ans) implicit double precision(a-h,o-z) double precision a0(nc),ca(nx,nc),x(nt,*),ans(nc,nt) integer ia(nx) do 14591 i=1,nt do 14601 ic=1,nc ans(ic,i)=a0(ic) if(nin.gt.0) ans(ic,i)=ans(ic,i)+dot_product(ca(1:nin,ic),x(i,ia(1 *:nin))) 14601 continue continue 14591 continue continue return end subroutine splognet(parm,no,ni,nc,x,ix,jx,y,g,jd,vp,cl,ne,nx,nlam, *flmin, ulam,thr,isd,intr,maxit,kopt,lmu,a0,ca,ia,nin,dev0,dev,alm *,nlp,jerr) implicit double precision(a-h,o-z) double precision x(*),y(no,max(2,nc)),g(no,nc),vp(ni),ulam(nlam) double precision ca(nx,nc,nlam),a0(nc,nlam),dev(nlam),alm(nlam),cl *(2,ni) integer ix(*),jx(*),jd(*),ia(nx),nin(nlam) double precision, dimension (:), allocatable :: xm,xs,ww,vq,xv integer, dimension (:), allocatable :: ju if(maxval(vp) .gt. 0.0)goto 14621 jerr=10000 return 14621 continue allocate(ww(1:no),stat=jerr) if(jerr.ne.0) return allocate(ju(1:ni),stat=jerr) if(jerr.ne.0) return allocate(vq(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xs(1:ni),stat=jerr) if(jerr.ne.0) return if(kopt .ne. 2)goto 14641 allocate(xv(1:ni),stat=jerr) if(jerr.ne.0) return 14641 continue call spchkvars(no,ni,x,ix,ju) if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0 if(maxval(ju) .gt. 0)goto 14661 jerr=7777 return 14661 continue vq=max(0d0,vp) vq=vq*ni/sum(vq) do 14671 i=1,no ww(i)=sum(y(i,:)) if(ww(i).gt.0.0) y(i,:)=y(i,:)/ww(i) 14671 continue continue sw=sum(ww) ww=ww/sw if(nc .ne. 1)goto 14691 call splstandard2(no,ni,x,ix,jx,ww,ju,isd,intr,xm,xs) if(isd .le. 0)goto 14711 do 14721 j=1,ni cl(:,j)=cl(:,j)*xs(j) 14721 continue continue 14711 continue call sprlognet2n(parm,no,ni,x,ix,jx,y(:,1),g(:,1),ww,ju,vq,cl,ne,n *x,nlam, flmin,ulam,thr,isd,intr,maxit,kopt,xm,xs,lmu,a0,ca,ia,nin *,dev0,dev, alm,nlp,jerr) goto 14681 14691 if(kopt .ne. 2)goto 14731 call multsplstandard2(no,ni,x,ix,jx,ww,ju,isd,intr,xm,xs,xv) if(isd .le. 0)goto 14751 do 14761 j=1,ni cl(:,j)=cl(:,j)*xs(j) 14761 continue continue 14751 continue call multsprlognetn(parm,no,ni,nc,x,ix,jx,y,g,ww,ju,vq,cl,ne,nx,nl *am,flmin, ulam,thr,intr,maxit,xv,xm,xs,lmu,a0,ca,ia,nin,dev0,dev, *alm,nlp,jerr) goto 14771 14731 continue call splstandard2(no,ni,x,ix,jx,ww,ju,isd,intr,xm,xs) if(isd .le. 0)goto 14791 do 14801 j=1,ni cl(:,j)=cl(:,j)*xs(j) 14801 continue continue 14791 continue call sprlognetn(parm,no,ni,nc,x,ix,jx,y,g,ww,ju,vq,cl,ne,nx,nlam,f *lmin, ulam,thr,isd,intr,maxit,kopt,xm,xs,lmu,a0,ca, ia,nin,dev0, *dev,alm,nlp,jerr) 14771 continue 14681 continue if(jerr.gt.0) return dev0=2.0*sw*dev0 do 14811 k=1,lmu nk=nin(k) do 14821 ic=1,nc if(isd .le. 0)goto 14841 do 14851 l=1,nk ca(l,ic,k)=ca(l,ic,k)/xs(ia(l)) 14851 continue continue 14841 continue if(intr .ne. 0)goto 14871 a0(ic,k)=0.0 goto 14881 14871 continue a0(ic,k)=a0(ic,k)-dot_product(ca(1:nk,ic,k),xm(ia(1:nk))) 14881 continue continue 14821 continue continue 14811 continue continue deallocate(ww,ju,vq,xm,xs) if(kopt.eq.2) deallocate(xv) return end subroutine multsplstandard2(no,ni,x,ix,jx,w,ju,isd,intr,xm,xs,xv) implicit double precision(a-h,o-z) double precision x(*),w(no),xm(ni),xs(ni),xv(ni) integer ix(*),jx(*),ju(ni) if(intr .ne. 0)goto 14901 do 14911 j=1,ni if(ju(j).eq.0)goto 14911 xm(j)=0.0 jb=ix(j) je=ix(j+1)-1 xv(j)=dot_product(w(jx(jb:je)),x(jb:je)**2) if(isd .eq. 0)goto 14931 xbq=dot_product(w(jx(jb:je)),x(jb:je))**2 vc=xv(j)-xbq xs(j)=sqrt(vc) xv(j)=1.0+xbq/vc goto 14941 14931 continue xs(j)=1.0 14941 continue continue 14911 continue continue return 14901 continue do 14951 j=1,ni if(ju(j).eq.0)goto 14951 jb=ix(j) je=ix(j+1)-1 xm(j)=dot_product(w(jx(jb:je)),x(jb:je)) xv(j)=dot_product(w(jx(jb:je)),x(jb:je)**2)-xm(j)**2 if(isd .le. 0)goto 14971 xs(j)=sqrt(xv(j)) xv(j)=1.0 14971 continue 14951 continue continue if(isd.eq.0) xs=1.0 return end subroutine splstandard2(no,ni,x,ix,jx,w,ju,isd,intr,xm,xs) implicit double precision(a-h,o-z) double precision x(*),w(no),xm(ni),xs(ni) integer ix(*),jx(*),ju(ni) if(intr .ne. 0)goto 14991 do 15001 j=1,ni if(ju(j).eq.0)goto 15001 xm(j)=0.0 jb=ix(j) je=ix(j+1)-1 if(isd .eq. 0)goto 15021 vc=dot_product(w(jx(jb:je)),x(jb:je)**2) -dot_product(w(jx(jb:je) *),x(jb:je))**2 xs(j)=sqrt(vc) goto 15031 15021 continue xs(j)=1.0 15031 continue continue 15001 continue continue return 14991 continue do 15041 j=1,ni if(ju(j).eq.0)goto 15041 jb=ix(j) je=ix(j+1)-1 xm(j)=dot_product(w(jx(jb:je)),x(jb:je)) if(isd.ne.0) xs(j)=sqrt(dot_product(w(jx(jb:je)),x(jb:je)**2)-xm(j *)**2) 15041 continue continue if(isd.eq.0) xs=1.0 return end subroutine sprlognet2n(parm,no,ni,x,ix,jx,y,g,w,ju,vp,cl,ne,nx,nla *m, flmin,ulam,shri,isd,intr,maxit,kopt,xb,xs, lmu,a0,a,m,kin,dev *0,dev,alm,nlp,jerr) implicit double precision(a-h,o-z) double precision x(*),y(no),g(no),w(no),vp(ni),ulam(nlam),cl(2,ni) double precision a(nx,nlam),a0(nlam),dev(nlam),alm(nlam) double precision xb(ni),xs(ni) integer ix(*),jx(*),ju(ni),m(nx),kin(nlam) double precision, dimension (:), allocatable :: xm,b,bs,v,r double precision, dimension (:), allocatable :: sc,xv,q,ga integer, dimension (:), allocatable :: mm,ixx call get_int_parms(sml,eps,big,mnlam,devmax,pmin,exmx,itrace) allocate(b(0:ni),stat=jerr) if(jerr.ne.0) return allocate(xm(0:ni),stat=jerr) if(jerr.ne.0) return allocate(xv(1:ni),stat=jerr) if(jerr.ne.0) return allocate(bs(0:ni),stat=jerr) if(jerr.ne.0) return allocate(ga(1:ni),stat=jerr) if(jerr.ne.0) return allocate(mm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ixx(1:ni),stat=jerr) if(jerr.ne.0) return allocate(q(1:no),stat=jerr) if(jerr.ne.0) return allocate(r(1:no),stat=jerr) if(jerr.ne.0) return allocate(v(1:no),stat=jerr) if(jerr.ne.0) return allocate(sc(1:no),stat=jerr) if(jerr.ne.0) return fmax=log(1.0/pmin-1.0) fmin=-fmax vmin=(1.0+pmin)*pmin*(1.0-pmin) bta=parm omb=1.0-bta q0=dot_product(w,y) if(q0 .gt. pmin)goto 15061 jerr=8001 return 15061 continue if(q0 .lt. 1.0-pmin)goto 15081 jerr=9001 return 15081 continue if(intr.eq.0) q0=0.5 bz=0.0 if(intr.ne.0) bz=log(q0/(1.0-q0)) if(nonzero(no,g) .ne. 0)goto 15101 vi=q0*(1.0-q0) b(0)=bz v=vi*w r=w*(y-q0) q=q0 xm(0)=vi dev1=-(bz*q0+log(1.0-q0)) goto 15111 15101 continue b(0)=0.0 if(intr .eq. 0)goto 15131 b(0)=azero(no,y,g,w,jerr) if(jerr.ne.0) return 15131 continue q=1.0/(1.0+exp(-b(0)-g)) v=w*q*(1.0-q) r=w*(y-q) xm(0)=sum(v) dev1=-(b(0)*q0+dot_product(w,y*g+log(1.0-q))) 15111 continue continue if(kopt .le. 0)goto 15151 if(isd .le. 0 .or. intr .eq. 0)goto 15171 xv=0.25 goto 15181 15171 continue do 15191 j=1,ni if(ju(j).eq.0)goto 15191 jb=ix(j) je=ix(j+1)-1 xv(j)=0.25*(dot_product(w(jx(jb:je)),x(jb:je)**2)-xb(j)**2) 15191 continue continue 15181 continue continue 15151 continue b(1:ni)=0.0 dev0=dev1 do 15201 i=1,no if(y(i).gt.0.0) dev0=dev0+w(i)*y(i)*log(y(i)) if(y(i).lt.1.0) dev0=dev0+w(i)*(1.0-y(i))*log(1.0-y(i)) 15201 continue continue alf=1.0 if(flmin .ge. 1.0)goto 15221 eqs=max(eps,flmin) alf=eqs**(1.0/(nlam-1)) 15221 continue m=0 mm=0 nin=0 o=0.0 svr=o mnl=min(mnlam,nlam) bs=0.0 nlp=0 nin=nlp shr=shri*dev0 al=0.0 ixx=0 do 15231 j=1,ni if(ju(j).eq.0)goto 15231 jb=ix(j) je=ix(j+1)-1 jn=ix(j+1)-ix(j) sc(1:jn)=r(jx(jb:je))+v(jx(jb:je))*o gj=dot_product(sc(1:jn),x(jb:je)) ga(j)=abs((gj-svr*xb(j))/xs(j)) 15231 continue continue do 15241 ilm=1,nlam if(itrace.ne.0) call setpb(ilm-1) al0=al if(flmin .lt. 1.0)goto 15261 al=ulam(ilm) goto 15251 15261 if(ilm .le. 2)goto 15271 al=al*alf goto 15251 15271 if(ilm .ne. 1)goto 15281 al=big goto 15291 15281 continue al0=0.0 do 15301 j=1,ni if(ju(j).eq.0)goto 15301 if(vp(j).gt.0.0) al0=max(al0,ga(j)/vp(j)) 15301 continue continue al0=al0/max(bta,1.0d-3) al=alf*al0 15291 continue 15251 continue al2=al*omb al1=al*bta tlam=bta*(2.0*al-al0) do 15311 k=1,ni if(ixx(k).eq.1)goto 15311 if(ju(k).eq.0)goto 15311 if(ga(k).gt.tlam*vp(k)) ixx(k)=1 15311 continue continue 10880 continue continue 15321 continue bs(0)=b(0) if(nin.gt.0) bs(m(1:nin))=b(m(1:nin)) do 15331 j=1,ni if(ixx(j).eq.0)goto 15331 jb=ix(j) je=ix(j+1)-1 jn=ix(j+1)-ix(j) sc(1:jn)=v(jx(jb:je)) xm(j)=dot_product(sc(1:jn),x(jb:je)) if(kopt .ne. 0)goto 15351 xv(j)=dot_product(sc(1:jn),x(jb:je)**2) xv(j)=(xv(j)-2.0*xb(j)*xm(j)+xm(0)*xb(j)**2)/xs(j)**2 15351 continue 15331 continue continue continue 15361 continue nlp=nlp+1 dlx=0.0 do 15371 k=1,ni if(ixx(k).eq.0)goto 15371 jb=ix(k) je=ix(k+1)-1 jn=ix(k+1)-ix(k) bk=b(k) sc(1:jn)=r(jx(jb:je))+v(jx(jb:je))*o gk=dot_product(sc(1:jn),x(jb:je)) gk=(gk-svr*xb(k))/xs(k) u=gk+xv(k)*b(k) au=abs(u)-vp(k)*al1 if(au .gt. 0.0)goto 15391 b(k)=0.0 goto 15401 15391 continue b(k)=max(cl(1,k),min(cl(2,k),sign(au,u)/(xv(k)+vp(k)*al2))) 15401 continue continue d=b(k)-bk if(abs(d).le.0.0)goto 15371 dlx=max(dlx,xv(k)*d**2) if(mm(k) .ne. 0)goto 15421 nin=nin+1 if(nin.gt.nx)goto 15372 mm(k)=nin m(nin)=k sc(1:jn)=v(jx(jb:je)) xm(k)=dot_product(sc(1:jn),x(jb:je)) 15421 continue r(jx(jb:je))=r(jx(jb:je))-d*v(jx(jb:je))*x(jb:je)/xs(k) o=o+d*(xb(k)/xs(k)) svr=svr-d*(xm(k)-xb(k)*xm(0))/xs(k) 15371 continue 15372 continue if(nin.gt.nx)goto 15362 d=0.0 if(intr.ne.0) d=svr/xm(0) if(d .eq. 0.0)goto 15441 b(0)=b(0)+d dlx=max(dlx,xm(0)*d**2) r=r-d*v svr=svr-d*xm(0) 15441 continue if(dlx.lt.shr)goto 15362 if(nlp .le. maxit)goto 15461 jerr=-ilm return 15461 continue continue 15471 continue nlp=nlp+1 dlx=0.0 do 15481 l=1,nin k=m(l) jb=ix(k) je=ix(k+1)-1 jn=ix(k+1)-ix(k) bk=b(k) sc(1:jn)=r(jx(jb:je))+v(jx(jb:je))*o gk=dot_product(sc(1:jn),x(jb:je)) gk=(gk-svr*xb(k))/xs(k) u=gk+xv(k)*b(k) au=abs(u)-vp(k)*al1 if(au .gt. 0.0)goto 15501 b(k)=0.0 goto 15511 15501 continue b(k)=max(cl(1,k),min(cl(2,k),sign(au,u)/(xv(k)+vp(k)*al2))) 15511 continue continue d=b(k)-bk if(abs(d).le.0.0)goto 15481 dlx=max(dlx,xv(k)*d**2) r(jx(jb:je))=r(jx(jb:je))-d*v(jx(jb:je))*x(jb:je)/xs(k) o=o+d*(xb(k)/xs(k)) svr=svr-d*(xm(k)-xb(k)*xm(0))/xs(k) 15481 continue continue d=0.0 if(intr.ne.0) d=svr/xm(0) if(d .eq. 0.0)goto 15531 b(0)=b(0)+d dlx=max(dlx,xm(0)*d**2) r=r-d*v svr=svr-d*xm(0) 15531 continue if(dlx.lt.shr)goto 15472 if(nlp .le. maxit)goto 15551 jerr=-ilm return 15551 continue goto 15471 15472 continue goto 15361 15362 continue if(nin.gt.nx)goto 15322 sc=b(0) b0=0.0 do 15561 j=1,nin l=m(j) jb=ix(l) je=ix(l+1)-1 sc(jx(jb:je))=sc(jx(jb:je))+b(l)*x(jb:je)/xs(l) b0=b0-b(l)*xb(l)/xs(l) 15561 continue continue sc=sc+b0 do 15571 i=1,no fi=sc(i)+g(i) if(fi .ge. fmin)goto 15591 q(i)=0.0 goto 15581 15591 if(fi .le. fmax)goto 15601 q(i)=1.0 goto 15611 15601 continue q(i)=1.0/(1.0+exp(-fi)) 15611 continue 15581 continue 15571 continue continue v=w*q*(1.0-q) xm(0)=sum(v) if(xm(0).lt.vmin)goto 15322 r=w*(y-q) svr=sum(r) o=0.0 if(xm(0)*(b(0)-bs(0))**2 .ge. shr)goto 15631 kx=0 do 15641 j=1,nin k=m(j) if(xv(k)*(b(k)-bs(k))**2.lt.shr)goto 15641 kx=1 goto 15642 15641 continue 15642 continue if(kx .ne. 0)goto 15661 do 15671 j=1,ni if(ixx(j).eq.1)goto 15671 if(ju(j).eq.0)goto 15671 jb=ix(j) je=ix(j+1)-1 jn=ix(j+1)-ix(j) sc(1:jn)=r(jx(jb:je))+v(jx(jb:je))*o gj=dot_product(sc(1:jn),x(jb:je)) ga(j)=abs((gj-svr*xb(j))/xs(j)) if(ga(j) .le. al1*vp(j))goto 15691 ixx(j)=1 kx=1 15691 continue 15671 continue continue if(kx.eq.1) go to 10880 goto 15322 15661 continue 15631 continue goto 15321 15322 continue if(nin .le. nx)goto 15711 jerr=-10000-ilm goto 15242 15711 continue if(nin.gt.0) a(1:nin,ilm)=b(m(1:nin)) kin(ilm)=nin a0(ilm)=b(0) alm(ilm)=al lmu=ilm devi=dev2(no,w,y,q,pmin) dev(ilm)=(dev1-devi)/dev0 if(ilm.lt.mnl)goto 15241 if(flmin.ge.1.0)goto 15241 me=0 do 15721 j=1,nin if(a(j,ilm).ne.0.0) me=me+1 15721 continue continue if(me.gt.ne)goto 15242 if(dev(ilm).gt.devmax)goto 15242 if(dev(ilm)-dev(ilm-1).lt.sml)goto 15242 if(xm(0).lt.vmin)goto 15242 15241 continue 15242 continue g=log(q/(1.0-q)) deallocate(xm,b,bs,v,r,sc,xv,q,mm,ga,ixx) return end subroutine sprlognetn(parm,no,ni,nc,x,ix,jx,y,g,w,ju,vp,cl,ne,nx,n *lam,flmin, ulam,shri,isd,intr,maxit,kopt,xb,xs,lmu,a0,a,m,kin,dev *0,dev,alm,nlp,jerr) implicit double precision(a-h,o-z) double precision x(*),y(no,nc),g(no,nc),w(no),vp(ni),ulam(nlam),xb *(ni),xs(ni) double precision a(nx,nc,nlam),a0(nc,nlam),dev(nlam),alm(nlam),cl( *2,ni) integer ix(*),jx(*),ju(ni),m(nx),kin(nlam) double precision, dimension (:,:), allocatable :: q double precision, dimension (:), allocatable :: sxp,sxpl double precision, dimension (:), allocatable :: sc,xm,v,r,ga double precision, dimension (:,:), allocatable :: b,bs,xv integer, dimension (:), allocatable :: mm,is,iy allocate(b(0:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(xv(1:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(bs(0:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(q(1:no,1:nc),stat=jerr) if(jerr.ne.0) return call get_int_parms(sml,eps,big,mnlam,devmax,pmin,exmx,itrace) exmn=-exmx allocate(xm(0:ni),stat=jerr) if(jerr.ne.0) return allocate(r(1:no),stat=jerr) if(jerr.ne.0) return allocate(v(1:no),stat=jerr) if(jerr.ne.0) return allocate(mm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ga(1:ni),stat=jerr) if(jerr.ne.0) return allocate(iy(1:ni),stat=jerr) if(jerr.ne.0) return allocate(is(1:max(nc,ni)),stat=jerr) if(jerr.ne.0) return allocate(sxp(1:no),stat=jerr) if(jerr.ne.0) return allocate(sxpl(1:no),stat=jerr) if(jerr.ne.0) return allocate(sc(1:no),stat=jerr) if(jerr.ne.0) return pmax=1.0-pmin emin=pmin/pmax emax=1.0/emin pfm=(1.0+pmin)*pmin pfx=(1.0-pmin)*pmax vmin=pfm*pmax bta=parm omb=1.0-bta dev1=0.0 dev0=0.0 do 15731 ic=1,nc q0=dot_product(w,y(:,ic)) if(q0 .gt. pmin)goto 15751 jerr =8000+ic return 15751 continue if(q0 .lt. 1.0-pmin)goto 15771 jerr =9000+ic return 15771 continue if(intr.eq.0) q0=1.0/nc b(1:ni,ic)=0.0 b(0,ic)=0.0 if(intr .eq. 0)goto 15791 b(0,ic)=log(q0) dev1=dev1-q0*b(0,ic) 15791 continue 15731 continue continue if(intr.eq.0) dev1=log(float(nc)) iy=0 al=0.0 if(nonzero(no*nc,g) .ne. 0)goto 15811 b(0,:)=b(0,:)-sum(b(0,:))/nc sxp=0.0 do 15821 ic=1,nc q(:,ic)=exp(b(0,ic)) sxp=sxp+q(:,ic) 15821 continue continue goto 15831 15811 continue do 15841 i=1,no g(i,:)=g(i,:)-sum(g(i,:))/nc 15841 continue continue sxp=0.0 if(intr .ne. 0)goto 15861 b(0,:)=0.0 goto 15871 15861 continue call kazero(nc,no,y,g,w,b(0,:),jerr) if(jerr.ne.0) return 15871 continue continue dev1=0.0 do 15881 ic=1,nc q(:,ic)=b(0,ic)+g(:,ic) dev1=dev1-dot_product(w,y(:,ic)*q(:,ic)) q(:,ic)=exp(q(:,ic)) sxp=sxp+q(:,ic) 15881 continue continue sxpl=w*log(sxp) do 15891 ic=1,nc dev1=dev1+dot_product(y(:,ic),sxpl) 15891 continue continue 15831 continue continue do 15901 ic=1,nc do 15911 i=1,no if(y(i,ic).gt.0.0) dev0=dev0+w(i)*y(i,ic)*log(y(i,ic)) 15911 continue continue 15901 continue continue dev0=dev0+dev1 if(kopt .le. 0)goto 15931 if(isd .le. 0 .or. intr .eq. 0)goto 15951 xv=0.25 goto 15961 15951 continue do 15971 j=1,ni if(ju(j).eq.0)goto 15971 jb=ix(j) je=ix(j+1)-1 xv(j,:)=0.25*(dot_product(w(jx(jb:je)),x(jb:je)**2)-xb(j)**2) 15971 continue continue 15961 continue continue 15931 continue alf=1.0 if(flmin .ge. 1.0)goto 15991 eqs=max(eps,flmin) alf=eqs**(1.0/(nlam-1)) 15991 continue m=0 mm=0 nin=0 nlp=0 mnl=min(mnlam,nlam) bs=0.0 svr=0.0 o=0.0 shr=shri*dev0 ga=0.0 do 16001 ic=1,nc v=q(:,ic)/sxp r=w*(y(:,ic)-v) v=w*v*(1.0-v) do 16011 j=1,ni if(ju(j).eq.0)goto 16011 jb=ix(j) je=ix(j+1)-1 jn=ix(j+1)-ix(j) sc(1:jn)=r(jx(jb:je))+o*v(jx(jb:je)) gj=dot_product(sc(1:jn),x(jb:je)) ga(j)=max(ga(j),abs(gj-svr*xb(j))/xs(j)) 16011 continue continue 16001 continue continue do 16021 ilm=1,nlam if(itrace.ne.0) call setpb(ilm-1) al0=al if(flmin .lt. 1.0)goto 16041 al=ulam(ilm) goto 16031 16041 if(ilm .le. 2)goto 16051 al=al*alf goto 16031 16051 if(ilm .ne. 1)goto 16061 al=big goto 16071 16061 continue al0=0.0 do 16081 j=1,ni if(ju(j).eq.0)goto 16081 if(vp(j).gt.0.0) al0=max(al0,ga(j)/vp(j)) 16081 continue continue al0=al0/max(bta,1.0d-3) al=alf*al0 16071 continue 16031 continue al2=al*omb al1=al*bta tlam=bta*(2.0*al-al0) do 16091 k=1,ni if(iy(k).eq.1)goto 16091 if(ju(k).eq.0)goto 16091 if(ga(k).gt.tlam*vp(k)) iy(k)=1 16091 continue continue 10880 continue continue 16101 continue ixx=0 jxx=ixx ig=0 do 16111 ic=1,nc bs(0,ic)=b(0,ic) if(nin.gt.0) bs(m(1:nin),ic)=b(m(1:nin),ic) xm(0)=0.0 svr=0.0 o=0.0 do 16121 i=1,no pic=q(i,ic)/sxp(i) if(pic .ge. pfm)goto 16141 pic=0.0 v(i)=0.0 goto 16131 16141 if(pic .le. pfx)goto 16151 pic=1.0 v(i)=0.0 goto 16161 16151 continue v(i)=w(i)*pic*(1.0-pic) xm(0)=xm(0)+v(i) 16161 continue 16131 continue r(i)=w(i)*(y(i,ic)-pic) svr=svr+r(i) 16121 continue continue if(xm(0).le.vmin)goto 16111 ig=1 do 16171 j=1,ni if(iy(j).eq.0)goto 16171 jb=ix(j) je=ix(j+1)-1 xm(j)=dot_product(v(jx(jb:je)),x(jb:je)) if(kopt .ne. 0)goto 16191 xv(j,ic)=dot_product(v(jx(jb:je)),x(jb:je)**2) xv(j,ic)=(xv(j,ic)-2.0*xb(j)*xm(j)+xm(0)*xb(j)**2)/xs(j)**2 16191 continue 16171 continue continue continue 16201 continue nlp=nlp+1 dlx=0.0 do 16211 k=1,ni if(iy(k).eq.0)goto 16211 jb=ix(k) je=ix(k+1)-1 jn=ix(k+1)-ix(k) bk=b(k,ic) sc(1:jn)=r(jx(jb:je))+o*v(jx(jb:je)) gk=dot_product(sc(1:jn),x(jb:je)) gk=(gk-svr*xb(k))/xs(k) u=gk+xv(k,ic)*b(k,ic) au=abs(u)-vp(k)*al1 if(au .gt. 0.0)goto 16231 b(k,ic)=0.0 goto 16241 16231 continue b(k,ic)=max(cl(1,k),min(cl(2,k),sign(au,u)/ (xv(k,ic)+vp(k)*al2)) *) 16241 continue continue d=b(k,ic)-bk if(abs(d).le.0.0)goto 16211 dlx=max(dlx,xv(k,ic)*d**2) if(mm(k) .ne. 0)goto 16261 nin=nin+1 if(nin .le. nx)goto 16281 jxx=1 goto 16212 16281 continue mm(k)=nin m(nin)=k xm(k)=dot_product(v(jx(jb:je)),x(jb:je)) 16261 continue r(jx(jb:je))=r(jx(jb:je))-d*v(jx(jb:je))*x(jb:je)/xs(k) o=o+d*(xb(k)/xs(k)) svr=svr-d*(xm(k)-xb(k)*xm(0))/xs(k) 16211 continue 16212 continue if(jxx.gt.0)goto 16202 d=0.0 if(intr.ne.0) d=svr/xm(0) if(d .eq. 0.0)goto 16301 b(0,ic)=b(0,ic)+d dlx=max(dlx,xm(0)*d**2) r=r-d*v svr=svr-d*xm(0) 16301 continue if(dlx.lt.shr)goto 16202 if(nlp .le. maxit)goto 16321 jerr=-ilm return 16321 continue continue 16331 continue nlp=nlp+1 dlx=0.0 do 16341 l=1,nin k=m(l) jb=ix(k) je=ix(k+1)-1 jn=ix(k+1)-ix(k) bk=b(k,ic) sc(1:jn)=r(jx(jb:je))+o*v(jx(jb:je)) gk=dot_product(sc(1:jn),x(jb:je)) gk=(gk-svr*xb(k))/xs(k) u=gk+xv(k,ic)*b(k,ic) au=abs(u)-vp(k)*al1 if(au .gt. 0.0)goto 16361 b(k,ic)=0.0 goto 16371 16361 continue b(k,ic)=max(cl(1,k),min(cl(2,k),sign(au,u)/ (xv(k,ic)+vp(k)*al2)) *) 16371 continue continue d=b(k,ic)-bk if(abs(d).le.0.0)goto 16341 dlx=max(dlx,xv(k,ic)*d**2) r(jx(jb:je))=r(jx(jb:je))-d*v(jx(jb:je))*x(jb:je)/xs(k) o=o+d*(xb(k)/xs(k)) svr=svr-d*(xm(k)-xb(k)*xm(0))/xs(k) 16341 continue continue d=0.0 if(intr.ne.0) d=svr/xm(0) if(d .eq. 0.0)goto 16391 b(0,ic)=b(0,ic)+d dlx=max(dlx,xm(0)*d**2) r=r-d*v svr=svr-d*xm(0) 16391 continue if(dlx.lt.shr)goto 16332 if(nlp .le. maxit)goto 16411 jerr=-ilm return 16411 continue goto 16331 16332 continue goto 16201 16202 continue if(jxx.gt.0)goto 16112 if(xm(0)*(b(0,ic)-bs(0,ic))**2.gt.shr) ixx=1 if(ixx .ne. 0)goto 16431 do 16441 j=1,nin k=m(j) if(xv(k,ic)*(b(k,ic)-bs(k,ic))**2 .le. shr)goto 16461 ixx=1 goto 16442 16461 continue 16441 continue 16442 continue 16431 continue sc=b(0,ic)+g(:,ic) b0=0.0 do 16471 j=1,nin l=m(j) jb=ix(l) je=ix(l+1)-1 sc(jx(jb:je))=sc(jx(jb:je))+b(l,ic)*x(jb:je)/xs(l) b0=b0-b(l,ic)*xb(l)/xs(l) 16471 continue continue sc=min(max(exmn,sc+b0),exmx) sxp=sxp-q(:,ic) q(:,ic)=min(max(emin*sxp,exp(sc)),emax*sxp) sxp=sxp+q(:,ic) 16111 continue 16112 continue s=-sum(b(0,:))/nc b(0,:)=b(0,:)+s sc=s b0=0.0 do 16481 j=1,nin l=m(j) if(vp(l) .gt. 0.0)goto 16501 s=sum(b(l,:))/nc goto 16511 16501 continue s=elc(parm,nc,cl(:,l),b(l,:),is) 16511 continue continue b(l,:)=b(l,:)-s jb=ix(l) je=ix(l+1)-1 sc(jx(jb:je))=sc(jx(jb:je))-s*x(jb:je)/xs(l) b0=b0+s*xb(l)/xs(l) 16481 continue continue sc=sc+b0 sc=exp(sc) sxp=sxp*sc do 16521 ic=1,nc q(:,ic)=q(:,ic)*sc 16521 continue continue if(jxx.gt.0)goto 16102 if(ig.eq.0)goto 16102 if(ixx .ne. 0)goto 16541 do 16551 j=1,ni if(iy(j).eq.1)goto 16551 if(ju(j).eq.0)goto 16551 ga(j)=0.0 16551 continue continue do 16561 ic=1,nc v=q(:,ic)/sxp r=w*(y(:,ic)-v) v=w*v*(1.0-v) do 16571 j=1,ni if(iy(j).eq.1)goto 16571 if(ju(j).eq.0)goto 16571 jb=ix(j) je=ix(j+1)-1 jn=ix(j+1)-ix(j) sc(1:jn)=r(jx(jb:je))+o*v(jx(jb:je)) gj=dot_product(sc(1:jn),x(jb:je)) ga(j)=max(ga(j),abs(gj-svr*xb(j))/xs(j)) 16571 continue continue 16561 continue continue do 16581 k=1,ni if(iy(k).eq.1)goto 16581 if(ju(k).eq.0)goto 16581 if(ga(k) .le. al1*vp(k))goto 16601 iy(k)=1 ixx=1 16601 continue 16581 continue continue if(ixx.eq.1) go to 10880 goto 16102 16541 continue goto 16101 16102 continue if(jxx .le. 0)goto 16621 jerr=-10000-ilm goto 16022 16621 continue devi=0.0 do 16631 ic=1,nc if(nin.gt.0) a(1:nin,ic,ilm)=b(m(1:nin),ic) a0(ic,ilm)=b(0,ic) do 16641 i=1,no if(y(i,ic).le.0.0)goto 16641 devi=devi-w(i)*y(i,ic)*log(q(i,ic)/sxp(i)) 16641 continue continue 16631 continue continue kin(ilm)=nin alm(ilm)=al lmu=ilm dev(ilm)=(dev1-devi)/dev0 if(ig.eq.0)goto 16022 if(ilm.lt.mnl)goto 16021 if(flmin.ge.1.0)goto 16021 if(nintot(ni,nx,nc,a(1,1,ilm),m,nin,is).gt.ne)goto 16022 if(dev(ilm).gt.devmax)goto 16022 if(dev(ilm)-dev(ilm-1).lt.sml)goto 16022 16021 continue 16022 continue g=log(q) do 16651 i=1,no g(i,:)=g(i,:)-sum(g(i,:))/nc 16651 continue continue deallocate(sxp,b,bs,v,r,xv,q,mm,is,xm,sc,ga,iy) return end subroutine lcmodval(nc,nx,a0,ca,ia,nin,x,ix,jx,n,f) implicit double precision(a-h,o-z) double precision a0(nc),ca(nx,nc),x(*),f(nc,n) integer ia(*),ix(*),jx(*) do 16661 ic=1,nc f(ic,:)=a0(ic) 16661 continue continue do 16671 j=1,nin k=ia(j) kb=ix(k) ke=ix(k+1)-1 do 16681 ic=1,nc f(ic,jx(kb:ke))=f(ic,jx(kb:ke))+ca(j,ic)*x(kb:ke) 16681 continue continue 16671 continue continue return end subroutine coxnet(parm,no,ni,x,y,d,g,w,jd,vp,cl,ne,nx,nlam,flmin,u *lam,thr, maxit,isd,lmu,ca,ia,nin,dev0,dev,alm,nlp,jerr) implicit double precision(a-h,o-z) double precision x(no,ni),y(no),d(no),g(no),w(no),vp(ni),ulam(nlam *) double precision ca(nx,nlam),dev(nlam),alm(nlam),cl(2,ni) integer jd(*),ia(nx),nin(nlam) double precision, dimension (:), allocatable :: xs,ww,vq integer, dimension (:), allocatable :: ju if(maxval(vp) .gt. 0.0)goto 16701 jerr=10000 return 16701 continue allocate(ww(1:no),stat=jerr) if(jerr.ne.0) return allocate(ju(1:ni),stat=jerr) if(jerr.ne.0) return allocate(vq(1:ni),stat=jerr) if(jerr.ne.0) return if(isd .le. 0)goto 16721 allocate(xs(1:ni),stat=jerr) if(jerr.ne.0) return 16721 continue call chkvars(no,ni,x,ju) if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0 if(maxval(ju) .gt. 0)goto 16741 jerr=7777 return 16741 continue vq=max(0d0,vp) vq=vq*ni/sum(vq) ww=max(0d0,w) sw=sum(ww) if(sw .gt. 0.0)goto 16761 jerr=9999 return 16761 continue ww=ww/sw call cstandard(no,ni,x,ww,ju,isd,xs) if(isd .le. 0)goto 16781 do 16791 j=1,ni cl(:,j)=cl(:,j)*xs(j) 16791 continue continue 16781 continue call coxnet1(parm,no,ni,x,y,d,g,ww,ju,vq,cl,ne,nx,nlam,flmin,ulam, *thr, isd,maxit,lmu,ca,ia,nin,dev0,dev,alm,nlp,jerr) if(jerr.gt.0) return dev0=2.0*sw*dev0 if(isd .le. 0)goto 16811 do 16821 k=1,lmu nk=nin(k) ca(1:nk,k)=ca(1:nk,k)/xs(ia(1:nk)) 16821 continue continue 16811 continue deallocate(ww,ju,vq) if(isd.gt.0) deallocate(xs) return end subroutine cstandard(no,ni,x,w,ju,isd,xs) implicit double precision(a-h,o-z) double precision x(no,ni),w(no),xs(ni) integer ju(ni) do 16831 j=1,ni if(ju(j).eq.0)goto 16831 xm=dot_product(w,x(:,j)) x(:,j)=x(:,j)-xm if(isd .le. 0)goto 16851 xs(j)=sqrt(dot_product(w,x(:,j)**2)) x(:,j)=x(:,j)/xs(j) 16851 continue 16831 continue continue return end subroutine coxnet1(parm,no,ni,x,y,d,g,q,ju,vp,cl,ne,nx,nlam,flmin, *ulam,cthri, isd,maxit,lmu,ao,m,kin,dev0,dev,alm,nlp,jerr) implicit double precision(a-h,o-z) double precision x(no,ni),y(no),q(no),d(no),g(no),vp(ni),ulam(nlam *) double precision ao(nx,nlam),dev(nlam),alm(nlam),cl(2,ni) integer ju(ni),m(nx),kin(nlam) double precision, dimension (:), allocatable :: w,dk,v,xs,wr double precision, dimension (:), allocatable :: a,as,f,dq double precision, dimension (:), allocatable :: e,uu,ga integer, dimension (:), allocatable :: jp,kp,mm,ixx call get_int_parms(sml,eps,big,mnlam,devmax,pmin,exmx,itrace) isd = isd*1 sml=sml*100.0 devmax=devmax*0.99/0.999 allocate(e(1:no),stat=jerr) if(jerr.ne.0) return allocate(uu(1:no),stat=jerr) if(jerr.ne.0) return allocate(f(1:no),stat=jerr) if(jerr.ne.0) return allocate(w(1:no),stat=jerr) if(jerr.ne.0) return allocate(v(1:ni),stat=jerr) if(jerr.ne.0) return allocate(a(1:ni),stat=jerr) if(jerr.ne.0) return allocate(as(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xs(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ga(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ixx(1:ni),stat=jerr) if(jerr.ne.0) return allocate(jp(1:no),stat=jerr) if(jerr.ne.0) return allocate(kp(1:no),stat=jerr) if(jerr.ne.0) return allocate(dk(1:no),stat=jerr) if(jerr.ne.0) return allocate(wr(1:no),stat=jerr) if(jerr.ne.0) return allocate(dq(1:no),stat=jerr) if(jerr.ne.0) return allocate(mm(1:ni),stat=jerr) if(jerr.ne.0) return call groups(no,y,d,q,nk,kp,jp,t0,jerr) if(jerr.ne.0) go to 12180 alpha=parm oma=1.0-alpha nlm=0 ixx=0 al=0.0 dq=d*q call died(no,nk,dq,kp,jp,dk) a=0.0 f(1)=0.0 fmax=log(huge(f(1))*0.1) if(nonzero(no,g) .eq. 0)goto 16871 f=g-dot_product(q,g) e=q*exp(sign(min(abs(f),fmax),f)) goto 16881 16871 continue f=0.0 e=q 16881 continue continue r0=risk(no,ni,nk,dq,dk,f,e,kp,jp,uu) rr=-(dot_product(dk(1:nk),log(dk(1:nk)))+r0) dev0=rr do 16891 i=1,no if((y(i) .ge. t0) .and. (q(i) .gt. 0.0))goto 16911 w(i)=0.0 wr(i)=w(i) 16911 continue 16891 continue continue call outer(no,nk,dq,dk,kp,jp,e,wr,w,jerr,uu) if(jerr.ne.0) go to 12180 alf=1.0 if(flmin .ge. 1.0)goto 16931 eqs=max(eps,flmin) alf=eqs**(1.0/(nlam-1)) 16931 continue m=0 mm=0 nlp=0 nin=nlp mnl=min(mnlam,nlam) as=0.0 cthr=cthri*dev0 do 16941 j=1,ni if(ju(j).eq.0)goto 16941 ga(j)=abs(dot_product(wr,x(:,j))) 16941 continue continue do 16951 ilm=1,nlam if(itrace.ne.0) call setpb(ilm-1) al0=al if(flmin .lt. 1.0)goto 16971 al=ulam(ilm) goto 16961 16971 if(ilm .le. 2)goto 16981 al=al*alf goto 16961 16981 if(ilm .ne. 1)goto 16991 al=big goto 17001 16991 continue al0=0.0 do 17011 j=1,ni if(ju(j).eq.0)goto 17011 if(vp(j).gt.0.0) al0=max(al0,ga(j)/vp(j)) 17011 continue continue al0=al0/max(parm,1.0d-3) al=alf*al0 17001 continue 16961 continue sa=alpha*al omal=oma*al tlam=alpha*(2.0*al-al0) do 17021 k=1,ni if(ixx(k).eq.1)goto 17021 if(ju(k).eq.0)goto 17021 if(ga(k).gt.tlam*vp(k)) ixx(k)=1 17021 continue continue 10880 continue continue 17031 continue if(nin.gt.0) as(m(1:nin))=a(m(1:nin)) call vars(no,ni,x,w,ixx,v) continue 17041 continue nlp=nlp+1 dli=0.0 do 17051 j=1,ni if(ixx(j).eq.0)goto 17051 u=a(j)*v(j)+dot_product(wr,x(:,j)) if(abs(u) .gt. vp(j)*sa)goto 17071 at=0.0 goto 17081 17071 continue at=max(cl(1,j),min(cl(2,j),sign(abs(u)-vp(j)*sa,u)/ (v(j)+vp(j)*o *mal))) 17081 continue continue if(at .eq. a(j))goto 17101 del=at-a(j) a(j)=at dli=max(dli,v(j)*del**2) wr=wr-del*w*x(:,j) f=f+del*x(:,j) if(mm(j) .ne. 0)goto 17121 nin=nin+1 if(nin.gt.nx)goto 17052 mm(j)=nin m(nin)=j 17121 continue 17101 continue 17051 continue 17052 continue if(nin.gt.nx)goto 17042 if(dli.lt.cthr)goto 17042 if(nlp .le. maxit)goto 17141 jerr=-ilm return 17141 continue continue 17151 continue nlp=nlp+1 dli=0.0 do 17161 l=1,nin j=m(l) u=a(j)*v(j)+dot_product(wr,x(:,j)) if(abs(u) .gt. vp(j)*sa)goto 17181 at=0.0 goto 17191 17181 continue at=max(cl(1,j),min(cl(2,j),sign(abs(u)-vp(j)*sa,u)/ (v(j)+vp(j)*o *mal))) 17191 continue continue if(at .eq. a(j))goto 17211 del=at-a(j) a(j)=at dli=max(dli,v(j)*del**2) wr=wr-del*w*x(:,j) f=f+del*x(:,j) 17211 continue 17161 continue continue if(dli.lt.cthr)goto 17152 if(nlp .le. maxit)goto 17231 jerr=-ilm return 17231 continue goto 17151 17152 continue goto 17041 17042 continue if(nin.gt.nx)goto 17032 e=q*exp(sign(min(abs(f),fmax),f)) call outer(no,nk,dq,dk,kp,jp,e,wr,w,jerr,uu) if(jerr .eq. 0)goto 17251 jerr=jerr-ilm go to 12180 17251 continue ix=0 do 17261 j=1,nin k=m(j) if(v(k)*(a(k)-as(k))**2.lt.cthr)goto 17261 ix=1 goto 17262 17261 continue 17262 continue if(ix .ne. 0)goto 17281 do 17291 k=1,ni if(ixx(k).eq.1)goto 17291 if(ju(k).eq.0)goto 17291 ga(k)=abs(dot_product(wr,x(:,k))) if(ga(k) .le. sa*vp(k))goto 17311 ixx(k)=1 ix=1 17311 continue 17291 continue continue if(ix.eq.1) go to 10880 goto 17032 17281 continue goto 17031 17032 continue if(nin .le. nx)goto 17331 jerr=-10000-ilm goto 16952 17331 continue if(nin.gt.0) ao(1:nin,ilm)=a(m(1:nin)) kin(ilm)=nin alm(ilm)=al lmu=ilm dev(ilm)=(risk(no,ni,nk,dq,dk,f,e,kp,jp,uu)-r0)/rr if(ilm.lt.mnl)goto 16951 if(flmin.ge.1.0)goto 16951 me=0 do 17341 j=1,nin if(ao(j,ilm).ne.0.0) me=me+1 17341 continue continue if(me.gt.ne)goto 16952 if((dev(ilm)-dev(ilm-mnl+1))/dev(ilm).lt.sml)goto 16952 if(dev(ilm).gt.devmax)goto 16952 16951 continue 16952 continue g=f 12180 continue deallocate(e,uu,w,dk,v,xs,f,wr,a,as,jp,kp,dq,mm,ga,ixx) return end subroutine cxmodval(ca,ia,nin,n,x,f) implicit double precision(a-h,o-z) double precision ca(nin),x(n,*),f(n) integer ia(nin) f=0.0 if(nin.le.0) return do 17351 i=1,n f(i)=f(i)+dot_product(ca(1:nin),x(i,ia(1:nin))) 17351 continue continue return end subroutine groups(no,y,d,q,nk,kp,jp,t0,jerr) implicit double precision(a-h,o-z) double precision y(no),d(no),q(no) integer jp(no),kp(*) do 17361 j=1,no jp(j)=j 17361 continue continue call psort7(y,jp,1,no) nj=0 do 17371 j=1,no if(q(jp(j)).le.0.0)goto 17371 nj=nj+1 jp(nj)=jp(j) 17371 continue continue if(nj .ne. 0)goto 17391 jerr=20000 return 17391 continue j=1 continue 17401 if(d(jp(j)).gt.0.0)goto 17402 j=j+1 if(j.gt.nj)goto 17402 goto 17401 17402 continue if(j .lt. nj-1)goto 17421 jerr=30000 return 17421 continue t0=y(jp(j)) j0=j-1 if(j0 .le. 0)goto 17441 continue 17451 if(y(jp(j0)).lt.t0)goto 17452 j0=j0-1 if(j0.eq.0)goto 17452 goto 17451 17452 continue if(j0 .le. 0)goto 17471 nj=nj-j0 do 17481 j=1,nj jp(j)=jp(j+j0) 17481 continue continue 17471 continue 17441 continue jerr=0 nk=0 yk=t0 j=2 continue 17491 continue continue 17501 if(d(jp(j)).gt.0.0.and.y(jp(j)).gt.yk)goto 17502 j=j+1 if(j.gt.nj)goto 17502 goto 17501 17502 continue nk=nk+1 kp(nk)=j-1 if(j.gt.nj)goto 17492 if(j .ne. nj)goto 17521 nk=nk+1 kp(nk)=nj goto 17492 17521 continue yk=y(jp(j)) j=j+1 goto 17491 17492 continue return end subroutine outer(no,nk,d,dk,kp,jp,e,wr,w,jerr,u) implicit double precision(a-h,o-z) double precision d(no),dk(nk),wr(no),w(no) double precision e(no),u(no),b,c integer kp(nk),jp(no) call usk(no,nk,kp,jp,e,u) b=dk(1)/u(1) c=dk(1)/u(1)**2 jerr=0 do 17531 j=1,kp(1) i=jp(j) w(i)=e(i)*(b-e(i)*c) if(w(i) .gt. 0.0)goto 17551 jerr=-30000 return 17551 continue wr(i)=d(i)-e(i)*b 17531 continue continue do 17561 k=2,nk j1=kp(k-1)+1 j2=kp(k) b=b+dk(k)/u(k) c=c+dk(k)/u(k)**2 do 17571 j=j1,j2 i=jp(j) w(i)=e(i)*(b-e(i)*c) if(w(i) .gt. 0.0)goto 17591 jerr=-30000 return 17591 continue wr(i)=d(i)-e(i)*b 17571 continue continue 17561 continue continue return end subroutine vars(no,ni,x,w,ixx,v) implicit double precision(a-h,o-z) double precision x(no,ni),w(no),v(ni) integer ixx(ni) do 17601 j=1,ni if(ixx(j).gt.0) v(j)=dot_product(w,x(:,j)**2) 17601 continue continue return end subroutine died(no,nk,d,kp,jp,dk) implicit double precision(a-h,o-z) double precision d(no),dk(nk) integer kp(nk),jp(no) dk(1)=sum(d(jp(1:kp(1)))) do 17611 k=2,nk dk(k)=sum(d(jp((kp(k-1)+1):kp(k)))) 17611 continue continue return end subroutine usk(no,nk,kp,jp,e,u) implicit double precision(a-h,o-z) double precision e(no),u(nk),h integer kp(nk),jp(no) h=0.0 do 17621 k=nk,1,-1 j2=kp(k) j1=1 if(k.gt.1) j1=kp(k-1)+1 do 17631 j=j2,j1,-1 h=h+e(jp(j)) 17631 continue continue u(k)=h 17621 continue continue return end function risk(no,ni,nk,d,dk,f,e,kp,jp,u) implicit double precision(a-h,o-z) double precision d(no),dk(nk),f(no) integer kp(nk),jp(no) double precision e(no),u(nk) ni = ni*1 call usk(no,nk,kp,jp,e,u) u=log(u) risk=dot_product(d,f)-dot_product(dk,u) return end subroutine loglike(no,ni,x,y,d,g,w,nlam,a,flog,jerr) implicit double precision(a-h,o-z) double precision x(no,ni),y(no),d(no),g(no),w(no),a(ni,nlam),flog( *nlam) double precision, dimension (:), allocatable :: dk,f,xm,dq,q double precision, dimension (:), allocatable :: e,uu integer, dimension (:), allocatable :: jp,kp allocate(e(1:no),stat=jerr) if(jerr.ne.0) return allocate(q(1:no),stat=jerr) if(jerr.ne.0) return allocate(uu(1:no),stat=jerr) if(jerr.ne.0) return allocate(f(1:no),stat=jerr) if(jerr.ne.0) return allocate(dk(1:no),stat=jerr) if(jerr.ne.0) return allocate(jp(1:no),stat=jerr) if(jerr.ne.0) return allocate(kp(1:no),stat=jerr) if(jerr.ne.0) return allocate(dq(1:no),stat=jerr) if(jerr.ne.0) return allocate(xm(1:ni),stat=jerr) if(jerr.ne.0) return q=max(0d0,w) sw=sum(q) if(sw .gt. 0.0)goto 17651 jerr=9999 go to 12180 17651 continue call groups(no,y,d,q,nk,kp,jp,t0,jerr) if(jerr.ne.0) go to 12180 fmax=log(huge(e(1))*0.1) dq=d*q call died(no,nk,dq,kp,jp,dk) gm=dot_product(q,g)/sw do 17661 j=1,ni xm(j)=dot_product(q,x(:,j))/sw 17661 continue continue do 17671 lam=1,nlam do 17681 i=1,no f(i)=g(i)-gm+dot_product(a(:,lam),(x(i,:)-xm)) e(i)=q(i)*exp(sign(min(abs(f(i)),fmax),f(i))) 17681 continue continue flog(lam)=risk(no,ni,nk,dq,dk,f,e,kp,jp,uu) 17671 continue continue 12180 continue deallocate(e,uu,dk,f,jp,kp,dq) return end subroutine fishnet(parm,no,ni,x,y,g,w,jd,vp,cl,ne,nx,nlam,flmin,ul *am,thr, isd,intr,maxit,lmu,a0,ca,ia,nin,dev0,dev,alm,nlp,jerr) implicit double precision(a-h,o-z) double precision x(no,ni),y(no),g(no),w(no),vp(ni),ulam(nlam) double precision ca(nx,nlam),a0(nlam),dev(nlam),alm(nlam),cl(2,ni) integer jd(*),ia(nx),nin(nlam) double precision, dimension (:), allocatable :: xm,xs,ww,vq integer, dimension (:), allocatable :: ju if(maxval(vp) .gt. 0.0)goto 17701 jerr=10000 return 17701 continue if(minval(y) .ge. 0.0)goto 17721 jerr=8888 return 17721 continue allocate(ww(1:no),stat=jerr) if(jerr.ne.0) return allocate(ju(1:ni),stat=jerr) if(jerr.ne.0) return allocate(vq(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xm(1:ni),stat=jerr) if(jerr.ne.0) return if(isd .le. 0)goto 17741 allocate(xs(1:ni),stat=jerr) if(jerr.ne.0) return 17741 continue call chkvars(no,ni,x,ju) if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0 if(maxval(ju) .gt. 0)goto 17761 jerr=7777 go to 12180 17761 continue vq=max(0d0,vp) vq=vq*ni/sum(vq) ww=max(0d0,w) sw=sum(ww) if(sw .gt. 0.0)goto 17781 jerr=9999 go to 12180 17781 continue ww=ww/sw call lstandard1(no,ni,x,ww,ju,isd,intr,xm,xs) if(isd .le. 0)goto 17801 do 17811 j=1,ni cl(:,j)=cl(:,j)*xs(j) 17811 continue continue 17801 continue call fishnet1(parm,no,ni,x,y,g,ww,ju,vq,cl,ne,nx,nlam,flmin,ulam,t *hr, isd,intr,maxit,lmu,a0,ca,ia,nin,dev0,dev,alm,nlp,jerr) if(jerr.gt.0) go to 12180 dev0=2.0*sw*dev0 do 17821 k=1,lmu nk=nin(k) if(isd.gt.0) ca(1:nk,k)=ca(1:nk,k)/xs(ia(1:nk)) if(intr .ne. 0)goto 17841 a0(k)=0.0 goto 17851 17841 continue a0(k)=a0(k)-dot_product(ca(1:nk,k),xm(ia(1:nk))) 17851 continue continue 17821 continue continue 12180 continue deallocate(ww,ju,vq,xm) if(isd.gt.0) deallocate(xs) return end subroutine fishnet1(parm,no,ni,x,y,g,q,ju,vp,cl,ne,nx,nlam,flmin,u *lam,shri, isd,intr,maxit,lmu,a0,ca,m,kin,dev0,dev,alm,nlp,jerr) implicit double precision(a-h,o-z) double precision x(no,ni),y(no),g(no),q(no),vp(ni),ulam(nlam) double precision ca(nx,nlam),a0(nlam),dev(nlam),alm(nlam),cl(2,ni) integer ju(ni),m(nx),kin(nlam) double precision, dimension (:), allocatable :: t,w,wr,v,a,f,as,ga integer, dimension (:), allocatable :: mm,ixx call get_int_parms(sml,eps,big,mnlam,devmax,pmin,exmx,itrace) sml=sml*10.0 isd = isd*1 allocate(a(1:ni),stat=jerr) if(jerr.ne.0) return allocate(as(1:ni),stat=jerr) if(jerr.ne.0) return allocate(t(1:no),stat=jerr) if(jerr.ne.0) return allocate(mm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ga(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ixx(1:ni),stat=jerr) if(jerr.ne.0) return allocate(wr(1:no),stat=jerr) if(jerr.ne.0) return allocate(v(1:ni),stat=jerr) if(jerr.ne.0) return allocate(w(1:no),stat=jerr) if(jerr.ne.0) return allocate(f(1:no),stat=jerr) if(jerr.ne.0) return bta=parm omb=1.0-bta t=q*y yb=sum(t) fmax=log(huge(bta)*0.1) if(nonzero(no,g) .ne. 0)goto 17871 if(intr .eq. 0)goto 17891 w=q*yb az=log(yb) f=az dv0=yb*(az-1.0) goto 17901 17891 continue w=q az=0.0 f=az dv0=-1.0 17901 continue continue goto 17911 17871 continue w=q*exp(sign(min(abs(g),fmax),g)) v0=sum(w) if(intr .eq. 0)goto 17931 eaz=yb/v0 w=eaz*w az=log(eaz) f=az+g dv0=dot_product(t,g)-yb*(1.0-az) goto 17941 17931 continue az=0.0 f=g dv0=dot_product(t,g)-v0 17941 continue continue 17911 continue continue a=0.0 as=0.0 wr=t-w v0=1.0 if(intr.ne.0) v0=yb dvr=-yb do 17951 i=1,no if(t(i).gt.0.0) dvr=dvr+t(i)*log(y(i)) 17951 continue continue dvr=dvr-dv0 dev0=dvr alf=1.0 if(flmin .ge. 1.0)goto 17971 eqs=max(eps,flmin) alf=eqs**(1.0/(nlam-1)) 17971 continue m=0 mm=0 nlp=0 nin=nlp mnl=min(mnlam,nlam) shr=shri*dev0 ixx=0 al=0.0 do 17981 j=1,ni if(ju(j).eq.0)goto 17981 ga(j)=abs(dot_product(wr,x(:,j))) 17981 continue continue do 17991 ilm=1,nlam if(itrace.ne.0) call setpb(ilm-1) al0=al if(flmin .lt. 1.0)goto 18011 al=ulam(ilm) goto 18001 18011 if(ilm .le. 2)goto 18021 al=al*alf goto 18001 18021 if(ilm .ne. 1)goto 18031 al=big goto 18041 18031 continue al0=0.0 do 18051 j=1,ni if(ju(j).eq.0)goto 18051 if(vp(j).gt.0.0) al0=max(al0,ga(j)/vp(j)) 18051 continue continue al0=al0/max(bta,1.0d-3) al=alf*al0 18041 continue 18001 continue al2=al*omb al1=al*bta tlam=bta*(2.0*al-al0) do 18061 k=1,ni if(ixx(k).eq.1)goto 18061 if(ju(k).eq.0)goto 18061 if(ga(k).gt.tlam*vp(k)) ixx(k)=1 18061 continue continue 10880 continue continue 18071 continue az0=az if(nin.gt.0) as(m(1:nin))=a(m(1:nin)) do 18081 j=1,ni if(ixx(j).ne.0) v(j)=dot_product(w,x(:,j)**2) 18081 continue continue continue 18091 continue nlp=nlp+1 dlx=0.0 do 18101 k=1,ni if(ixx(k).eq.0)goto 18101 ak=a(k) u=dot_product(wr,x(:,k))+v(k)*ak au=abs(u)-vp(k)*al1 if(au .gt. 0.0)goto 18121 a(k)=0.0 goto 18131 18121 continue a(k)=max(cl(1,k),min(cl(2,k),sign(au,u)/(v(k)+vp(k)*al2))) 18131 continue continue if(a(k).eq.ak)goto 18101 d=a(k)-ak dlx=max(dlx,v(k)*d**2) wr=wr-d*w*x(:,k) f=f+d*x(:,k) if(mm(k) .ne. 0)goto 18151 nin=nin+1 if(nin.gt.nx)goto 18102 mm(k)=nin m(nin)=k 18151 continue 18101 continue 18102 continue if(nin.gt.nx)goto 18092 if(intr .eq. 0)goto 18171 d=sum(wr)/v0 az=az+d dlx=max(dlx,v0*d**2) wr=wr-d*w f=f+d 18171 continue if(dlx.lt.shr)goto 18092 if(nlp .le. maxit)goto 18191 jerr=-ilm return 18191 continue continue 18201 continue nlp=nlp+1 dlx=0.0 do 18211 l=1,nin k=m(l) ak=a(k) u=dot_product(wr,x(:,k))+v(k)*ak au=abs(u)-vp(k)*al1 if(au .gt. 0.0)goto 18231 a(k)=0.0 goto 18241 18231 continue a(k)=max(cl(1,k),min(cl(2,k),sign(au,u)/(v(k)+vp(k)*al2))) 18241 continue continue if(a(k).eq.ak)goto 18211 d=a(k)-ak dlx=max(dlx,v(k)*d**2) wr=wr-d*w*x(:,k) f=f+d*x(:,k) 18211 continue continue if(intr .eq. 0)goto 18261 d=sum(wr)/v0 az=az+d dlx=max(dlx,v0*d**2) wr=wr-d*w f=f+d 18261 continue if(dlx.lt.shr)goto 18202 if(nlp .le. maxit)goto 18281 jerr=-ilm return 18281 continue goto 18201 18202 continue goto 18091 18092 continue if(nin.gt.nx)goto 18072 w=q*exp(sign(min(abs(f),fmax),f)) v0=sum(w) wr=t-w if(v0*(az-az0)**2 .ge. shr)goto 18301 ix=0 do 18311 j=1,nin k=m(j) if(v(k)*(a(k)-as(k))**2.lt.shr)goto 18311 ix=1 goto 18312 18311 continue 18312 continue if(ix .ne. 0)goto 18331 do 18341 k=1,ni if(ixx(k).eq.1)goto 18341 if(ju(k).eq.0)goto 18341 ga(k)=abs(dot_product(wr,x(:,k))) if(ga(k) .le. al1*vp(k))goto 18361 ixx(k)=1 ix=1 18361 continue 18341 continue continue if(ix.eq.1) go to 10880 goto 18072 18331 continue 18301 continue goto 18071 18072 continue if(nin .le. nx)goto 18381 jerr=-10000-ilm goto 17992 18381 continue if(nin.gt.0) ca(1:nin,ilm)=a(m(1:nin)) kin(ilm)=nin a0(ilm)=az alm(ilm)=al lmu=ilm dev(ilm)=(dot_product(t,f)-v0-dv0)/dvr if(ilm.lt.mnl)goto 17991 if(flmin.ge.1.0)goto 17991 me=0 do 18391 j=1,nin if(ca(j,ilm).ne.0.0) me=me+1 18391 continue continue if(me.gt.ne)goto 17992 if((dev(ilm)-dev(ilm-mnl+1))/dev(ilm).lt.sml)goto 17992 if(dev(ilm).gt.devmax)goto 17992 17991 continue 17992 continue g=f continue deallocate(t,w,wr,v,a,f,as,mm,ga,ixx) return end function nonzero(n,v) implicit double precision(a-h,o-z) double precision v(n) nonzero=0 do 18401 i=1,n if(v(i) .eq. 0.0)goto 18421 nonzero=1 return 18421 continue 18401 continue continue return end subroutine solns(ni,nx,lmu,a,ia,nin,b) implicit double precision(a-h,o-z) double precision a(nx,lmu),b(ni,lmu) integer ia(nx),nin(lmu) do 18431 lam=1,lmu call uncomp(ni,a(:,lam),ia,nin(lam),b(:,lam)) 18431 continue continue return end subroutine lsolns(ni,nx,nc,lmu,a,ia,nin,b) implicit double precision(a-h,o-z) double precision a(nx,nc,lmu),b(ni,nc,lmu) integer ia(nx),nin(lmu) do 18441 lam=1,lmu call luncomp(ni,nx,nc,a(1,1,lam),ia,nin(lam),b(1,1,lam)) 18441 continue continue return end subroutine deviance(no,ni,x,y,g,q,nlam,a0,a,flog,jerr) implicit double precision(a-h,o-z) double precision x(no,ni),y(no),g(no),q(no),a(ni,nlam),a0(nlam),fl *og(nlam) double precision, dimension (:), allocatable :: w if(minval(y) .ge. 0.0)goto 18461 jerr=8888 return 18461 continue allocate(w(1:no),stat=jerr) if(jerr.ne.0) return w=max(0d0,q) sw=sum(w) if(sw .gt. 0.0)goto 18481 jerr=9999 go to 12180 18481 continue yb=dot_product(w,y)/sw fmax=log(huge(y(1))*0.1) do 18491 lam=1,nlam s=0.0 do 18501 i=1,no if(w(i).le.0.0)goto 18501 f=g(i)+a0(lam)+dot_product(a(:,lam),x(i,:)) s=s+w(i)*(y(i)*f-exp(sign(min(abs(f),fmax),f))) 18501 continue continue flog(lam)=2.0*(sw*yb*(log(yb)-1.0)-s) 18491 continue continue 12180 continue deallocate(w) return end subroutine spfishnet(parm,no,ni,x,ix,jx,y,g,w,jd,vp,cl,ne,nx,nlam, *flmin, ulam,thr,isd,intr,maxit,lmu,a0,ca,ia,nin,dev0,dev,alm,nlp, *jerr) implicit double precision(a-h,o-z) double precision x(*),y(no),g(no),w(no),vp(ni),ulam(nlam),cl(2,ni) double precision ca(nx,nlam),a0(nlam),dev(nlam),alm(nlam) integer ix(*),jx(*),jd(*),ia(nx),nin(nlam) double precision, dimension (:), allocatable :: xm,xs,ww,vq integer, dimension (:), allocatable :: ju if(maxval(vp) .gt. 0.0)goto 18521 jerr=10000 return 18521 continue if(minval(y) .ge. 0.0)goto 18541 jerr=8888 return 18541 continue allocate(ww(1:no),stat=jerr) if(jerr.ne.0) return allocate(ju(1:ni),stat=jerr) if(jerr.ne.0) return allocate(vq(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xs(1:ni),stat=jerr) if(jerr.ne.0) return call spchkvars(no,ni,x,ix,ju) if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0 if(maxval(ju) .gt. 0)goto 18561 jerr=7777 go to 12180 18561 continue vq=max(0d0,vp) vq=vq*ni/sum(vq) ww=max(0d0,w) sw=sum(ww) if(sw .gt. 0.0)goto 18581 jerr=9999 go to 12180 18581 continue ww=ww/sw call splstandard2(no,ni,x,ix,jx,ww,ju,isd,intr,xm,xs) if(isd .le. 0)goto 18601 do 18611 j=1,ni cl(:,j)=cl(:,j)*xs(j) 18611 continue continue 18601 continue call spfishnet1(parm,no,ni,x,ix,jx,y,g,ww,ju,vq,cl,ne,nx,nlam,flmi *n,ulam,thr, isd,intr,maxit,xm,xs,lmu,a0,ca,ia,nin,dev0,dev,alm,nl *p,jerr) if(jerr.gt.0) go to 12180 dev0=2.0*sw*dev0 do 18621 k=1,lmu nk=nin(k) if(isd.gt.0) ca(1:nk,k)=ca(1:nk,k)/xs(ia(1:nk)) if(intr .ne. 0)goto 18641 a0(k)=0.0 goto 18651 18641 continue a0(k)=a0(k)-dot_product(ca(1:nk,k),xm(ia(1:nk))) 18651 continue continue 18621 continue continue 12180 continue deallocate(ww,ju,vq,xm,xs) return end subroutine spfishnet1(parm,no,ni,x,ix,jx,y,g,q,ju,vp,cl,ne,nx,nlam *,flmin,ulam, shri,isd,intr,maxit,xb,xs,lmu,a0,ca,m,kin,dev0,dev,a *lm,nlp,jerr) implicit double precision(a-h,o-z) double precision x(*),y(no),g(no),q(no),vp(ni),ulam(nlam),xb(ni),x *s(ni) double precision ca(nx,nlam),a0(nlam),dev(nlam),alm(nlam),cl(2,ni) integer ix(*),jx(*),ju(ni),m(nx),kin(nlam) double precision, dimension (:), allocatable :: qy,t,w,wr,v double precision, dimension (:), allocatable :: a,as,xm,ga integer, dimension (:), allocatable :: mm,ixx call get_int_parms(sml,eps,big,mnlam,devmax,pmin,exmx,itrace) sml=sml*10.0 isd = isd*1 allocate(a(1:ni),stat=jerr) if(jerr.ne.0) return allocate(as(1:ni),stat=jerr) if(jerr.ne.0) return allocate(t(1:no),stat=jerr) if(jerr.ne.0) return allocate(mm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ga(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ixx(1:ni),stat=jerr) if(jerr.ne.0) return allocate(wr(1:no),stat=jerr) if(jerr.ne.0) return allocate(v(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(w(1:no),stat=jerr) if(jerr.ne.0) return allocate(qy(1:no),stat=jerr) if(jerr.ne.0) return bta=parm omb=1.0-bta fmax=log(huge(bta)*0.1) qy=q*y yb=sum(qy) if(nonzero(no,g) .ne. 0)goto 18671 t=0.0 if(intr .eq. 0)goto 18691 w=q*yb az=log(yb) uu=az xm=yb*xb dv0=yb*(az-1.0) goto 18701 18691 continue w=q xm=0.0 uu=0.0 az=uu dv0=-1.0 18701 continue continue goto 18711 18671 continue w=q*exp(sign(min(abs(g),fmax),g)) ww=sum(w) t=g if(intr .eq. 0)goto 18731 eaz=yb/ww w=eaz*w az=log(eaz) uu=az dv0=dot_product(qy,g)-yb*(1.0-az) goto 18741 18731 continue uu=0.0 az=uu dv0=dot_product(qy,g)-ww 18741 continue continue do 18751 j=1,ni if(ju(j).eq.0)goto 18751 jb=ix(j) je=ix(j+1)-1 xm(j)=dot_product(w(jx(jb:je)),x(jb:je)) 18751 continue continue 18711 continue continue tt=yb*uu ww=1.0 if(intr.ne.0) ww=yb wr=qy-q*(yb*(1.0-uu)) a=0.0 as=0.0 dvr=-yb do 18761 i=1,no if(qy(i).gt.0.0) dvr=dvr+qy(i)*log(y(i)) 18761 continue continue dvr=dvr-dv0 dev0=dvr alf=1.0 if(flmin .ge. 1.0)goto 18781 eqs=max(eps,flmin) alf=eqs**(1.0/(nlam-1)) 18781 continue m=0 mm=0 nlp=0 nin=nlp mnl=min(mnlam,nlam) shr=shri*dev0 al=0.0 ixx=0 do 18791 j=1,ni if(ju(j).eq.0)goto 18791 jb=ix(j) je=ix(j+1)-1 ga(j)=abs(dot_product(wr(jx(jb:je)),x(jb:je)) -uu*(xm(j)-ww*xb(j) *)-xb(j)*tt)/xs(j) 18791 continue continue do 18801 ilm=1,nlam if(itrace.ne.0) call setpb(ilm-1) al0=al if(flmin .lt. 1.0)goto 18821 al=ulam(ilm) goto 18811 18821 if(ilm .le. 2)goto 18831 al=al*alf goto 18811 18831 if(ilm .ne. 1)goto 18841 al=big goto 18851 18841 continue al0=0.0 do 18861 j=1,ni if(ju(j).eq.0)goto 18861 if(vp(j).gt.0.0) al0=max(al0,ga(j)/vp(j)) 18861 continue continue al0=al0/max(bta,1.0d-3) al=alf*al0 18851 continue 18811 continue al2=al*omb al1=al*bta tlam=bta*(2.0*al-al0) do 18871 k=1,ni if(ixx(k).eq.1)goto 18871 if(ju(k).eq.0)goto 18871 if(ga(k).gt.tlam*vp(k)) ixx(k)=1 18871 continue continue 10880 continue continue 18881 continue az0=az if(nin.gt.0) as(m(1:nin))=a(m(1:nin)) do 18891 j=1,ni if(ixx(j).eq.0)goto 18891 jb=ix(j) je=ix(j+1)-1 xm(j)=dot_product(w(jx(jb:je)),x(jb:je)) v(j)=(dot_product(w(jx(jb:je)),x(jb:je)**2) -2.0*xb(j)*xm(j)+ww*x *b(j)**2)/xs(j)**2 18891 continue continue continue 18901 continue nlp=nlp+1 dlx=0.0 do 18911 k=1,ni if(ixx(k).eq.0)goto 18911 jb=ix(k) je=ix(k+1)-1 ak=a(k) u=(dot_product(wr(jx(jb:je)),x(jb:je)) -uu*(xm(k)-ww*xb(k))-xb(k) **tt)/xs(k)+v(k)*ak au=abs(u)-vp(k)*al1 if(au .gt. 0.0)goto 18931 a(k)=0.0 goto 18941 18931 continue a(k)=max(cl(1,k),min(cl(2,k),sign(au,u)/(v(k)+vp(k)*al2))) 18941 continue continue if(a(k).eq.ak)goto 18911 if(mm(k) .ne. 0)goto 18961 nin=nin+1 if(nin.gt.nx)goto 18912 mm(k)=nin m(nin)=k 18961 continue d=a(k)-ak dlx=max(dlx,v(k)*d**2) dv=d/xs(k) wr(jx(jb:je))=wr(jx(jb:je))-dv*w(jx(jb:je))*x(jb:je) t(jx(jb:je))=t(jx(jb:je))+dv*x(jb:je) uu=uu-dv*xb(k) tt=tt-dv*xm(k) 18911 continue 18912 continue if(nin.gt.nx)goto 18902 if(intr .eq. 0)goto 18981 d=tt/ww-uu az=az+d dlx=max(dlx,ww*d**2) uu=uu+d 18981 continue if(dlx.lt.shr)goto 18902 if(nlp .le. maxit)goto 19001 jerr=-ilm return 19001 continue continue 19011 continue nlp=nlp+1 dlx=0.0 do 19021 l=1,nin k=m(l) jb=ix(k) je=ix(k+1)-1 ak=a(k) u=(dot_product(wr(jx(jb:je)),x(jb:je)) -uu*(xm(k)-ww*xb(k))-xb(k) **tt)/xs(k)+v(k)*ak au=abs(u)-vp(k)*al1 if(au .gt. 0.0)goto 19041 a(k)=0.0 goto 19051 19041 continue a(k)=max(cl(1,k),min(cl(2,k),sign(au,u)/(v(k)+vp(k)*al2))) 19051 continue continue if(a(k).eq.ak)goto 19021 d=a(k)-ak dlx=max(dlx,v(k)*d**2) dv=d/xs(k) wr(jx(jb:je))=wr(jx(jb:je))-dv*w(jx(jb:je))*x(jb:je) t(jx(jb:je))=t(jx(jb:je))+dv*x(jb:je) uu=uu-dv*xb(k) tt=tt-dv*xm(k) 19021 continue continue if(intr .eq. 0)goto 19071 d=tt/ww-uu az=az+d dlx=max(dlx,ww*d**2) uu=uu+d 19071 continue if(dlx.lt.shr)goto 19012 if(nlp .le. maxit)goto 19091 jerr=-ilm return 19091 continue goto 19011 19012 continue goto 18901 18902 continue if(nin.gt.nx)goto 18882 euu=exp(sign(min(abs(uu),fmax),uu)) w=euu*q*exp(sign(min(abs(t),fmax),t)) ww=sum(w) wr=qy-w*(1.0-uu) tt=sum(wr) if(ww*(az-az0)**2 .ge. shr)goto 19111 kx=0 do 19121 j=1,nin k=m(j) if(v(k)*(a(k)-as(k))**2.lt.shr)goto 19121 kx=1 goto 19122 19121 continue 19122 continue if(kx .ne. 0)goto 19141 do 19151 j=1,ni if(ixx(j).eq.1)goto 19151 if(ju(j).eq.0)goto 19151 jb=ix(j) je=ix(j+1)-1 xm(j)=dot_product(w(jx(jb:je)),x(jb:je)) ga(j)=abs(dot_product(wr(jx(jb:je)),x(jb:je)) -uu*(xm(j)-ww*xb(j) *)-xb(j)*tt)/xs(j) if(ga(j) .le. al1*vp(j))goto 19171 ixx(j)=1 kx=1 19171 continue 19151 continue continue if(kx.eq.1) go to 10880 goto 18882 19141 continue 19111 continue goto 18881 18882 continue if(nin .le. nx)goto 19191 jerr=-10000-ilm goto 18802 19191 continue if(nin.gt.0) ca(1:nin,ilm)=a(m(1:nin)) kin(ilm)=nin a0(ilm)=az alm(ilm)=al lmu=ilm dev(ilm)=(dot_product(qy,t)+yb*uu-ww-dv0)/dvr if(ilm.lt.mnl)goto 18801 if(flmin.ge.1.0)goto 18801 me=0 do 19201 j=1,nin if(ca(j,ilm).ne.0.0) me=me+1 19201 continue continue if(me.gt.ne)goto 18802 if((dev(ilm)-dev(ilm-mnl+1))/dev(ilm).lt.sml)goto 18802 if(dev(ilm).gt.devmax)goto 18802 18801 continue 18802 continue g=t+uu continue deallocate(t,w,wr,v,a,qy,xm,as,mm,ga,ixx) return end subroutine spdeviance(no,ni,x,ix,jx,y,g,q,nlam,a0,a,flog,jerr) implicit double precision(a-h,o-z) double precision x(*),y(no),g(no),q(no),a(ni,nlam),a0(nlam),flog(n *lam) integer ix(*),jx(*) double precision, dimension (:), allocatable :: w,f if(minval(y) .ge. 0.0)goto 19221 jerr=8888 return 19221 continue allocate(w(1:no),stat=jerr) if(jerr.ne.0) return allocate(f(1:no),stat=jerr) if(jerr.ne.0) return w=max(0d0,q) sw=sum(w) if(sw .gt. 0.0)goto 19241 jerr=9999 go to 12180 19241 continue yb=dot_product(w,y)/sw fmax=log(huge(y(1))*0.1) do 19251 lam=1,nlam f=a0(lam) do 19261 j=1,ni if(a(j,lam).eq.0.0)goto 19261 jb=ix(j) je=ix(j+1)-1 f(jx(jb:je))=f(jx(jb:je))+a(j,lam)*x(jb:je) 19261 continue continue f=f+g s=dot_product(w,y*f-exp(sign(min(abs(f),fmax),f))) flog(lam)=2.0*(sw*yb*(log(yb)-1.0)-s) 19251 continue continue 12180 continue deallocate(w,f) return end subroutine cspdeviance(no,x,ix,jx,y,g,q,nx,nlam,a0,ca,ia,nin,flog, *jerr) implicit double precision(a-h,o-z) double precision x(*),y(no),g(no),q(no),ca(nx,nlam),a0(nlam),flog( *nlam) integer ix(*),jx(*),nin(nlam),ia(nx) double precision, dimension (:), allocatable :: w,f if(minval(y) .ge. 0.0)goto 19281 jerr=8888 return 19281 continue allocate(w(1:no),stat=jerr) if(jerr.ne.0) return allocate(f(1:no),stat=jerr) if(jerr.ne.0) return w=max(0d0,q) sw=sum(w) if(sw .gt. 0.0)goto 19301 jerr=9999 go to 12180 19301 continue yb=dot_product(w,y)/sw fmax=log(huge(y(1))*0.1) do 19311 lam=1,nlam f=a0(lam) do 19321 k=1,nin(lam) j=ia(k) jb=ix(j) je=ix(j+1)-1 f(jx(jb:je))=f(jx(jb:je))+ca(k,lam)*x(jb:je) 19321 continue continue f=f+g s=dot_product(w,y*f-exp(sign(min(abs(f),fmax),f))) flog(lam)=2.0*(sw*yb*(log(yb)-1.0)-s) 19311 continue continue 12180 continue deallocate(w,f) return end subroutine multelnet(parm,no,ni,nr,x,y,w,jd,vp,cl,ne,nx,nlam, flm *in,ulam,thr,isd,jsd,intr,maxit, lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr *) implicit double precision(a-h,o-z) double precision x(no,ni),y(no,nr),w(no),vp(ni),ca(nx,nr,nlam) double precision ulam(nlam),a0(nr,nlam),rsq(nlam),alm(nlam),cl(2,n *i) integer jd(*),ia(nx),nin(nlam) double precision, dimension (:), allocatable :: vq; if(maxval(vp) .gt. 0.0)goto 19341 jerr=10000 return 19341 continue allocate(vq(1:ni),stat=jerr) if(jerr.ne.0) return vq=max(0d0,vp) vq=vq*ni/sum(vq) call multelnetn(parm,no,ni,nr,x,y,w,jd,vq,cl,ne,nx,nlam,flmin,ulam *,thr,isd, jsd,intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr) deallocate(vq) return end subroutine multelnetn(parm,no,ni,nr,x,y,w,jd,vp,cl,ne,nx,nlam,flmi *n,ulam,thr, isd,jsd,intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr) implicit double precision(a-h,o-z) double precision vp(ni),x(no,ni),y(no,nr),w(no),ulam(nlam),cl(2,ni *) double precision ca(nx,nr,nlam),a0(nr,nlam),rsq(nlam),alm(nlam) integer jd(*),ia(nx),nin(nlam) double precision, dimension (:), allocatable :: xm,xs,xv,ym,ys integer, dimension (:), allocatable :: ju double precision, dimension (:,:,:), allocatable :: clt allocate(clt(1:2,1:nr,1:ni),stat=jerr); if(jerr.ne.0) return allocate(xm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xs(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ym(1:nr),stat=jerr) if(jerr.ne.0) return allocate(ys(1:nr),stat=jerr) if(jerr.ne.0) return allocate(ju(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xv(1:ni),stat=jerr) if(jerr.ne.0) return call chkvars(no,ni,x,ju) if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0 if(maxval(ju) .gt. 0)goto 19361 jerr=7777 return 19361 continue call multstandard1(no,ni,nr,x,y,w,isd,jsd,intr,ju,xm,xs,ym,ys,xv,y *s0,jerr) if(jerr.ne.0) return do 19371 j=1,ni do 19381 k=1,nr do 19391 i=1,2 clt(i,k,j)=cl(i,j) 19391 continue continue 19381 continue continue 19371 continue continue if(isd .le. 0)goto 19411 do 19421 j=1,ni do 19431 k=1,nr do 19441 i=1,2 clt(i,k,j)=clt(i,k,j)*xs(j) 19441 continue continue 19431 continue continue 19421 continue continue 19411 continue if(jsd .le. 0)goto 19461 do 19471 j=1,ni do 19481 k=1,nr do 19491 i=1,2 clt(i,k,j)=clt(i,k,j)/ys(k) 19491 continue continue 19481 continue continue 19471 continue continue 19461 continue call multelnet2(parm,ni,nr,ju,vp,clt,y,no,ne,nx,x,nlam,flmin,ulam, *thr,maxit,xv, ys0,lmu,ca,ia,nin,rsq,alm,nlp,jerr) if(jerr.gt.0) return do 19501 k=1,lmu nk=nin(k) do 19511 j=1,nr do 19521 l=1,nk ca(l,j,k)=ys(j)*ca(l,j,k)/xs(ia(l)) 19521 continue continue if(intr .ne. 0)goto 19541 a0(j,k)=0.0 goto 19551 19541 continue a0(j,k)=ym(j)-dot_product(ca(1:nk,j,k),xm(ia(1:nk))) 19551 continue continue 19511 continue continue 19501 continue continue deallocate(xm,xs,ym,ys,ju,xv,clt) return end subroutine multstandard1(no,ni,nr,x,y,w,isd,jsd,intr,ju, xm,xs,ym *,ys,xv,ys0,jerr) implicit double precision(a-h,o-z) double precision x(no,ni),y(no,nr),w(no),xm(ni),xs(ni),xv(ni),ym(n *r),ys(nr) integer ju(ni) double precision, dimension (:), allocatable :: v allocate(v(1:no),stat=jerr) if(jerr.ne.0) return w=w/sum(w) v=sqrt(w) if(intr .ne. 0)goto 19571 do 19581 j=1,ni if(ju(j).eq.0)goto 19581 xm(j)=0.0 x(:,j)=v*x(:,j) z=dot_product(x(:,j),x(:,j)) if(isd .le. 0)goto 19601 xbq=dot_product(v,x(:,j))**2 vc=z-xbq xs(j)=sqrt(vc) x(:,j)=x(:,j)/xs(j) xv(j)=1.0+xbq/vc goto 19611 19601 continue xs(j)=1.0 xv(j)=z 19611 continue continue 19581 continue continue ys0=0.0 do 19621 j=1,nr ym(j)=0.0 y(:,j)=v*y(:,j) z=dot_product(y(:,j),y(:,j)) if(jsd .le. 0)goto 19641 u=z-dot_product(v,y(:,j))**2 ys0=ys0+z/u ys(j)=sqrt(u) y(:,j)=y(:,j)/ys(j) goto 19651 19641 continue ys(j)=1.0 ys0=ys0+z 19651 continue continue 19621 continue continue go to 10700 19571 continue do 19661 j=1,ni if(ju(j).eq.0)goto 19661 xm(j)=dot_product(w,x(:,j)) x(:,j)=v*(x(:,j)-xm(j)) xv(j)=dot_product(x(:,j),x(:,j)) if(isd.gt.0) xs(j)=sqrt(xv(j)) 19661 continue continue if(isd .ne. 0)goto 19681 xs=1.0 goto 19691 19681 continue do 19701 j=1,ni if(ju(j).eq.0)goto 19701 x(:,j)=x(:,j)/xs(j) 19701 continue continue xv=1.0 19691 continue continue ys0=0.0 do 19711 j=1,nr ym(j)=dot_product(w,y(:,j)) y(:,j)=v*(y(:,j)-ym(j)) z=dot_product(y(:,j),y(:,j)) if(jsd .le. 0)goto 19731 ys(j)=sqrt(z) y(:,j)=y(:,j)/ys(j) goto 19741 19731 continue ys0=ys0+z 19741 continue continue 19711 continue continue if(jsd .ne. 0)goto 19761 ys=1.0 goto 19771 19761 continue ys0=nr 19771 continue continue 10700 continue deallocate(v) return end subroutine multelnet2(beta,ni,nr,ju,vp,cl,y,no,ne,nx,x,nlam,flmin, *ulam,thri, maxit,xv,ys0,lmu,ao,ia,kin,rsqo,almo,nlp,jerr) implicit double precision(a-h,o-z) double precision vp(ni),y(no,nr),x(no,ni),ulam(nlam),ao(nx,nr,nlam *) double precision rsqo(nlam),almo(nlam),xv(ni),cl(2,nr,ni) integer ju(ni),ia(nx),kin(nlam) double precision, dimension (:), allocatable :: g,gk,del,gj integer, dimension (:), allocatable :: mm,ix,isc double precision, dimension (:,:), allocatable :: a allocate(a(1:nr,1:ni),stat=jerr) if(jerr.ne.0) return call get_int_parms(sml,eps,big,mnlam,rsqmax,pmin,exmx,itrace) allocate(gj(1:nr),stat=jerr) if(jerr.ne.0) return allocate(gk(1:nr),stat=jerr) if(jerr.ne.0) return allocate(del(1:nr),stat=jerr) if(jerr.ne.0) return allocate(mm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(g(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ix(1:ni),stat=jerr) if(jerr.ne.0) return allocate(isc(1:nr),stat=jerr) if(jerr.ne.0) return bta=beta omb=1.0-bta ix=0 thr=thri*ys0/nr alf=1.0 if(flmin .ge. 1.0)goto 19791 eqs=max(eps,flmin) alf=eqs**(1.0/(nlam-1)) 19791 continue rsq=ys0 a=0.0 mm=0 nlp=0 nin=nlp iz=0 mnl=min(mnlam,nlam) alm=0.0 do 19801 j=1,ni if(ju(j).eq.0)goto 19801 g(j)=0.0 do 19811 k=1,nr g(j)=g(j)+dot_product(y(:,k),x(:,j))**2 19811 continue continue g(j)=sqrt(g(j)) 19801 continue continue do 19821 m=1,nlam if(itrace.ne.0) call setpb(m-1) alm0=alm if(flmin .lt. 1.0)goto 19841 alm=ulam(m) goto 19831 19841 if(m .le. 2)goto 19851 alm=alm*alf goto 19831 19851 if(m .ne. 1)goto 19861 alm=big goto 19871 19861 continue alm0=0.0 do 19881 j=1,ni if(ju(j).eq.0)goto 19881 if(vp(j).gt.0.0) alm0=max(alm0,g(j)/vp(j)) 19881 continue continue alm0=alm0/max(bta,1.0d-3) alm=alf*alm0 19871 continue 19831 continue dem=alm*omb ab=alm*bta rsq0=rsq jz=1 tlam=bta*(2.0*alm-alm0) do 19891 k=1,ni if(ix(k).eq.1)goto 19891 if(ju(k).eq.0)goto 19891 if(g(k).gt.tlam*vp(k)) ix(k)=1 19891 continue continue continue 19901 continue if(iz*jz.ne.0) go to 10360 10880 continue nlp=nlp+1 dlx=0.0 do 19911 k=1,ni if(ix(k).eq.0)goto 19911 gkn=0.0 do 19921 j=1,nr gj(j)=dot_product(y(:,j),x(:,k)) gk(j)=gj(j)+a(j,k)*xv(k) gkn=gkn+gk(j)**2 19921 continue continue gkn=sqrt(gkn) u=1.0-ab*vp(k)/gkn del=a(:,k) if(u .gt. 0.0)goto 19941 a(:,k)=0.0 goto 19951 19941 continue a(:,k)=gk*(u/(xv(k)+dem*vp(k))) call chkbnds(nr,gk,gkn,xv(k),cl(1,1,k), dem*vp(k),ab*vp(k),a(:,k) *,isc,jerr) if(jerr.ne.0) return 19951 continue continue del=a(:,k)-del if(maxval(abs(del)).le.0.0)goto 19911 do 19961 j=1,nr rsq=rsq-del(j)*(2.0*gj(j)-del(j)*xv(k)) y(:,j)=y(:,j)-del(j)*x(:,k) dlx=max(dlx,xv(k)*del(j)**2) 19961 continue continue if(mm(k) .ne. 0)goto 19981 nin=nin+1 if(nin.gt.nx)goto 19912 mm(k)=nin ia(nin)=k 19981 continue 19911 continue 19912 continue if(nin.gt.nx)goto 19902 if(dlx .ge. thr)goto 20001 ixx=0 do 20011 k=1,ni if(ix(k).eq.1)goto 20011 if(ju(k).eq.0)goto 20011 g(k)=0.0 do 20021 j=1,nr g(k)=g(k)+dot_product(y(:,j),x(:,k))**2 20021 continue continue g(k)=sqrt(g(k)) if(g(k) .le. ab*vp(k))goto 20041 ix(k)=1 ixx=1 20041 continue 20011 continue continue if(ixx.eq.1) go to 10880 goto 19902 20001 continue if(nlp .le. maxit)goto 20061 jerr=-m return 20061 continue 10360 continue iz=1 continue 20071 continue nlp=nlp+1 dlx=0.0 do 20081 l=1,nin k=ia(l) gkn=0.0 do 20091 j=1,nr gj(j)=dot_product(y(:,j),x(:,k)) gk(j)=gj(j)+a(j,k)*xv(k) gkn=gkn+gk(j)**2 20091 continue continue gkn=sqrt(gkn) u=1.0-ab*vp(k)/gkn del=a(:,k) if(u .gt. 0.0)goto 20111 a(:,k)=0.0 goto 20121 20111 continue a(:,k)=gk*(u/(xv(k)+dem*vp(k))) call chkbnds(nr,gk,gkn,xv(k),cl(1,1,k), dem*vp(k),ab*vp(k),a(:,k) *,isc,jerr) if(jerr.ne.0) return 20121 continue continue del=a(:,k)-del if(maxval(abs(del)).le.0.0)goto 20081 do 20131 j=1,nr rsq=rsq-del(j)*(2.0*gj(j)-del(j)*xv(k)) y(:,j)=y(:,j)-del(j)*x(:,k) dlx=max(dlx,xv(k)*del(j)**2) 20131 continue continue 20081 continue continue if(dlx.lt.thr)goto 20072 if(nlp .le. maxit)goto 20151 jerr=-m return 20151 continue goto 20071 20072 continue jz=0 goto 19901 19902 continue if(nin .le. nx)goto 20171 jerr=-10000-m goto 19822 20171 continue if(nin .le. 0)goto 20191 do 20201 j=1,nr ao(1:nin,j,m)=a(j,ia(1:nin)) 20201 continue continue 20191 continue kin(m)=nin rsqo(m)=1.0-rsq/ys0 almo(m)=alm lmu=m if(m.lt.mnl)goto 19821 if(flmin.ge.1.0)goto 19821 me=0 do 20211 j=1,nin if(ao(j,1,m).ne.0.0) me=me+1 20211 continue continue if(me.gt.ne)goto 19822 if(rsq0-rsq.lt.sml*rsq)goto 19822 if(rsqo(m).gt.rsqmax)goto 19822 19821 continue 19822 continue deallocate(a,mm,g,ix,del,gj,gk) return end subroutine chkbnds(nr,gk,gkn,xv,cl,al1,al2,a,isc,jerr) implicit double precision(a-h,o-z) double precision gk(nr),cl(2,nr),a(nr) integer isc(nr) kerr=0 al1p=1.0+al1/xv al2p=al2/xv isc=0 gsq=gkn**2 asq=dot_product(a,a) usq=0.0 u=0.0 kn=-1 continue 20221 continue vmx=0.0 do 20231 k=1,nr v=max(a(k)-cl(2,k),cl(1,k)-a(k)) if(v .le. vmx)goto 20251 vmx=v kn=k 20251 continue 20231 continue continue if(vmx.le.0.0)goto 20222 if(isc(kn).ne.0)goto 20222 gsq=gsq-gk(kn)**2 g=sqrt(gsq)/xv if(a(kn).lt.cl(1,kn)) u=cl(1,kn) if(a(kn).gt.cl(2,kn)) u=cl(2,kn) usq=usq+u**2 if(usq .ne. 0.0)goto 20271 b=max(0d0,(g-al2p)/al1p) goto 20281 20271 continue b0=sqrt(asq-a(kn)**2) b=bnorm(b0,al1p,al2p,g,usq,kerr) if(kerr.ne.0)goto 20222 20281 continue continue asq=usq+b**2 if(asq .gt. 0.0)goto 20301 a=0.0 goto 20222 20301 continue a(kn)=u isc(kn)=1 f=1.0/(xv*(al1p+al2p/sqrt(asq))) do 20311 j=1,nr if(isc(j).eq.0) a(j)=f*gk(j) 20311 continue continue goto 20221 20222 continue if(kerr.ne.0) jerr=kerr return end subroutine chkbnds1(nr,gk,gkn,xv,cl1,cl2,al1,al2,a,isc,jerr) implicit double precision(a-h,o-z) double precision gk(nr),a(nr) integer isc(nr) kerr=0 al1p=1.0+al1/xv al2p=al2/xv isc=0 gsq=gkn**2 asq=dot_product(a,a) usq=0.0 u=0.0 kn=-1 continue 20321 continue vmx=0.0 do 20331 k=1,nr v=max(a(k)-cl2,cl1-a(k)) if(v .le. vmx)goto 20351 vmx=v kn=k 20351 continue 20331 continue continue if(vmx.le.0.0)goto 20322 if(isc(kn).ne.0)goto 20322 gsq=gsq-gk(kn)**2 g=sqrt(gsq)/xv if(a(kn).lt.cl1) u=cl1 if(a(kn).gt.cl2) u=cl2 usq=usq+u**2 if(usq .ne. 0.0)goto 20371 b=max(0d0,(g-al2p)/al1p) goto 20381 20371 continue b0=sqrt(asq-a(kn)**2) b=bnorm(b0,al1p,al2p,g,usq,kerr) if(kerr.ne.0)goto 20322 20381 continue continue asq=usq+b**2 if(asq .gt. 0.0)goto 20401 a=0.0 goto 20322 20401 continue a(kn)=u isc(kn)=1 f=1.0/(xv*(al1p+al2p/sqrt(asq))) do 20411 j=1,nr if(isc(j).eq.0) a(j)=f*gk(j) 20411 continue continue goto 20321 20322 continue if(kerr.ne.0) jerr=kerr return end function bnorm(b0,al1p,al2p,g,usq,jerr) implicit double precision(a-h,o-z) data thr,mxit /1.0d-10,100/ b=b0 zsq=b**2+usq if(zsq .gt. 0.0)goto 20431 bnorm=0.0 return 20431 continue z=sqrt(zsq) f=b*(al1p+al2p/z)-g jerr=0 do 20441 it=1,mxit b=b-f/(al1p+al2p*usq/(z*zsq)) zsq=b**2+usq if(zsq .gt. 0.0)goto 20461 bnorm=0.0 return 20461 continue z=sqrt(zsq) f=b*(al1p+al2p/z)-g if(abs(f).le.thr)goto 20442 if(b .gt. 0.0)goto 20481 b=0.0 goto 20442 20481 continue 20441 continue 20442 continue bnorm=b if(it.ge.mxit) jerr=90000 return entry chg_bnorm(arg,irg) bnorm = 0.0 thr=arg mxit=irg return entry get_bnorm(arg,irg) bnorm = 0.0 arg=thr irg=mxit return end subroutine multsolns(ni,nx,nr,lmu,a,ia,nin,b) implicit double precision(a-h,o-z) double precision a(nx,nr,lmu),b(ni,nr,lmu) integer ia(nx),nin(lmu) do 20491 lam=1,lmu call multuncomp(ni,nr,nx,a(1,1,lam),ia,nin(lam),b(1,1,lam)) 20491 continue continue return end subroutine multuncomp(ni,nr,nx,ca,ia,nin,a) implicit double precision(a-h,o-z) double precision ca(nx,nr),a(ni,nr) integer ia(nx) a=0.0 if(nin .le. 0)goto 20511 do 20521 j=1,nr a(ia(1:nin),j)=ca(1:nin,j) 20521 continue continue 20511 continue return end subroutine multmodval(nx,nr,a0,ca,ia,nin,n,x,f) implicit double precision(a-h,o-z) double precision a0(nr),ca(nx,nr),x(n,*),f(nr,n) integer ia(nx) do 20531 i=1,n f(:,i)=a0 20531 continue continue if(nin.le.0) return do 20541 i=1,n do 20551 j=1,nr f(j,i)=f(j,i)+dot_product(ca(1:nin,j),x(i,ia(1:nin))) 20551 continue continue 20541 continue continue return end subroutine multspelnet(parm,no,ni,nr,x,ix,jx,y,w,jd,vp,cl,ne,nx, *nlam,flmin,ulam,thr,isd, jsd,intr,maxit,lmu,a0,ca,ia,nin,rsq,alm, *nlp,jerr) implicit double precision(a-h,o-z) double precision x(*),y(no,nr),w(no),vp(ni),ulam(nlam),cl(2,ni) double precision ca(nx,nr,nlam),a0(nr,nlam),rsq(nlam),alm(nlam) integer ix(*),jx(*),jd(*),ia(nx),nin(nlam) double precision, dimension (:), allocatable :: vq; if(maxval(vp) .gt. 0.0)goto 20571 jerr=10000 return 20571 continue allocate(vq(1:ni),stat=jerr) if(jerr.ne.0) return vq=max(0d0,vp) vq=vq*ni/sum(vq) call multspelnetn(parm,no,ni,nr,x,ix,jx,y,w,jd,vq,cl,ne,nx,nlam,fl *min, ulam,thr,isd,jsd,intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jer *r) deallocate(vq) return end subroutine multspelnetn(parm,no,ni,nr,x,ix,jx,y,w,jd,vp,cl,ne,nx,n *lam,flmin, ulam,thr,isd,jsd,intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,n *lp,jerr) implicit double precision(a-h,o-z) double precision x(*),vp(ni),y(no,nr),w(no),ulam(nlam),cl(2,ni) double precision ca(nx,nr,nlam),a0(nr,nlam),rsq(nlam),alm(nlam) integer ix(*),jx(*),jd(*),ia(nx),nin(nlam) double precision, dimension (:), allocatable :: xm,xs,xv,ym,ys integer, dimension (:), allocatable :: ju double precision, dimension (:,:,:), allocatable :: clt allocate(clt(1:2,1:nr,1:ni),stat=jerr) if(jerr.ne.0) return allocate(xm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xs(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ym(1:nr),stat=jerr) if(jerr.ne.0) return allocate(ys(1:nr),stat=jerr) if(jerr.ne.0) return allocate(ju(1:ni),stat=jerr) if(jerr.ne.0) return allocate(xv(1:ni),stat=jerr) if(jerr.ne.0) return call spchkvars(no,ni,x,ix,ju) if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0 if(maxval(ju) .gt. 0)goto 20591 jerr=7777 return 20591 continue call multspstandard1(no,ni,nr,x,ix,jx,y,w,ju,isd,jsd,intr, xm,xs, *ym,ys,xv,ys0,jerr) if(jerr.ne.0) return do 20601 j=1,ni do 20611 k=1,nr do 20621 i=1,2 clt(i,k,j)=cl(i,j) 20621 continue continue 20611 continue continue 20601 continue continue if(isd .le. 0)goto 20641 do 20651 j=1,ni do 20661 k=1,nr do 20671 i=1,2 clt(i,k,j)=clt(i,k,j)*xs(j) 20671 continue continue 20661 continue continue 20651 continue continue 20641 continue if(jsd .le. 0)goto 20691 do 20701 j=1,ni do 20711 k=1,nr do 20721 i=1,2 clt(i,k,j)=clt(i,k,j)/ys(k) 20721 continue continue 20711 continue continue 20701 continue continue 20691 continue call multspelnet2(parm,ni,nr,y,w,no,ne,nx,x,ix,jx,ju,vp,clt,nlam,f *lmin, ulam,thr,maxit,xm,xs,xv,ys0,lmu,ca,ia,nin,rsq,alm,nlp,jerr) if(jerr.gt.0) return do 20731 k=1,lmu nk=nin(k) do 20741 j=1,nr do 20751 l=1,nk ca(l,j,k)=ys(j)*ca(l,j,k)/xs(ia(l)) 20751 continue continue if(intr .ne. 0)goto 20771 a0(j,k)=0.0 goto 20781 20771 continue a0(j,k)=ym(j)-dot_product(ca(1:nk,j,k),xm(ia(1:nk))) 20781 continue continue 20741 continue continue 20731 continue continue deallocate(xm,xs,ym,ys,ju,xv,clt) return end subroutine multspstandard1(no,ni,nr,x,ix,jx,y,w,ju,isd,jsd,intr, *xm,xs,ym,ys,xv,ys0,jerr) implicit double precision(a-h,o-z) double precision x(*),y(no,nr),w(no),xm(ni),xs(ni),xv(ni),ym(nr),y *s(nr) integer ix(*),jx(*),ju(ni) jerr = jerr*1 w=w/sum(w) if(intr .ne. 0)goto 20801 do 20811 j=1,ni if(ju(j).eq.0)goto 20811 xm(j)=0.0 jb=ix(j) je=ix(j+1)-1 z=dot_product(w(jx(jb:je)),x(jb:je)**2) if(isd .le. 0)goto 20831 xbq=dot_product(w(jx(jb:je)),x(jb:je))**2 vc=z-xbq xs(j)=sqrt(vc) xv(j)=1.0+xbq/vc goto 20841 20831 continue xs(j)=1.0 xv(j)=z 20841 continue continue 20811 continue continue ys0=0.0 do 20851 j=1,nr ym(j)=0.0 z=dot_product(w,y(:,j)**2) if(jsd .le. 0)goto 20871 u=z-dot_product(w,y(:,j))**2 ys0=ys0+z/u ys(j)=sqrt(u) y(:,j)=y(:,j)/ys(j) goto 20881 20871 continue ys(j)=1.0 ys0=ys0+z 20881 continue continue 20851 continue continue return 20801 continue do 20891 j=1,ni if(ju(j).eq.0)goto 20891 jb=ix(j) je=ix(j+1)-1 xm(j)=dot_product(w(jx(jb:je)),x(jb:je)) xv(j)=dot_product(w(jx(jb:je)),x(jb:je)**2)-xm(j)**2 if(isd.gt.0) xs(j)=sqrt(xv(j)) 20891 continue continue if(isd .ne. 0)goto 20911 xs=1.0 goto 20921 20911 continue xv=1.0 20921 continue continue ys0=0.0 do 20931 j=1,nr ym(j)=dot_product(w,y(:,j)) y(:,j)=y(:,j)-ym(j) z=dot_product(w,y(:,j)**2) if(jsd .le. 0)goto 20951 ys(j)=sqrt(z) y(:,j)=y(:,j)/ys(j) goto 20961 20951 continue ys0=ys0+z 20961 continue continue 20931 continue continue if(jsd .ne. 0)goto 20981 ys=1.0 goto 20991 20981 continue ys0=nr 20991 continue continue return end subroutine multspelnet2(beta,ni,nr,y,w,no,ne,nx,x,ix,jx,ju,vp,cl,n *lam,flmin, ulam,thri,maxit,xm,xs,xv,ys0,lmu,ao,ia,kin,rsqo,almo,n *lp,jerr) implicit double precision(a-h,o-z) double precision y(no,nr),w(no),x(*),vp(ni),ulam(nlam),cl(2,nr,ni) double precision ao(nx,nr,nlam),rsqo(nlam),almo(nlam),xm(ni),xs(ni *),xv(ni) integer ix(*),jx(*),ju(ni),ia(nx),kin(nlam) double precision, dimension (:), allocatable :: g,gj,gk,del,o integer, dimension (:), allocatable :: mm,iy,isc double precision, dimension (:,:), allocatable :: a allocate(a(1:nr,1:ni),stat=jerr) if(jerr.ne.0) return call get_int_parms(sml,eps,big,mnlam,rsqmax,pmin,exmx,itrace) allocate(mm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(g(1:ni),stat=jerr) if(jerr.ne.0) return allocate(gj(1:nr),stat=jerr) if(jerr.ne.0) return allocate(gk(1:nr),stat=jerr) if(jerr.ne.0) return allocate(del(1:nr),stat=jerr) if(jerr.ne.0) return allocate(o(1:nr),stat=jerr) if(jerr.ne.0) return allocate(iy(1:ni),stat=jerr) if(jerr.ne.0) return allocate(isc(1:nr),stat=jerr) if(jerr.ne.0) return bta=beta omb=1.0-bta alm=0.0 iy=0 thr=thri*ys0/nr alf=1.0 if(flmin .ge. 1.0)goto 21011 eqs=max(eps,flmin) alf=eqs**(1.0/(nlam-1)) 21011 continue rsq=ys0 a=0.0 mm=0 o=0.0 nlp=0 nin=nlp iz=0 mnl=min(mnlam,nlam) do 21021 j=1,ni if(ju(j).eq.0)goto 21021 jb=ix(j) je=ix(j+1)-1 g(j)=0.0 do 21031 k=1,nr g(j)=g(j)+(dot_product(y(jx(jb:je),k),w(jx(jb:je))*x(jb:je))/xs(j) *)**2 21031 continue continue g(j)=sqrt(g(j)) 21021 continue continue do 21041 m=1,nlam if(itrace.ne.0) call setpb(m-1) alm0=alm if(flmin .lt. 1.0)goto 21061 alm=ulam(m) goto 21051 21061 if(m .le. 2)goto 21071 alm=alm*alf goto 21051 21071 if(m .ne. 1)goto 21081 alm=big goto 21091 21081 continue alm0=0.0 do 21101 j=1,ni if(ju(j).eq.0)goto 21101 if(vp(j).gt.0.0) alm0=max(alm0,g(j)/vp(j)) 21101 continue continue alm0=alm0/max(bta,1.0d-3) alm=alf*alm0 21091 continue 21051 continue dem=alm*omb ab=alm*bta rsq0=rsq jz=1 tlam=bta*(2.0*alm-alm0) do 21111 k=1,ni if(iy(k).eq.1)goto 21111 if(ju(k).eq.0)goto 21111 if(g(k).gt.tlam*vp(k)) iy(k)=1 21111 continue continue continue 21121 continue if(iz*jz.ne.0) go to 10360 10880 continue nlp=nlp+1 dlx=0.0 do 21131 k=1,ni if(iy(k).eq.0)goto 21131 jb=ix(k) je=ix(k+1)-1 gkn=0.0 do 21141 j=1,nr gj(j)=dot_product(y(jx(jb:je),j)+o(j),w(jx(jb:je))*x(jb:je))/xs(k) gk(j)=gj(j)+a(j,k)*xv(k) gkn=gkn+gk(j)**2 21141 continue continue gkn=sqrt(gkn) u=1.0-ab*vp(k)/gkn del=a(:,k) if(u .gt. 0.0)goto 21161 a(:,k)=0.0 goto 21171 21161 continue a(:,k)=gk*(u/(xv(k)+dem*vp(k))) call chkbnds(nr,gk,gkn,xv(k),cl(1,1,k), dem*vp(k),ab*vp(k),a(:,k) *,isc,jerr) if(jerr.ne.0) return 21171 continue continue del=a(:,k)-del if(maxval(abs(del)).le.0.0)goto 21131 if(mm(k) .ne. 0)goto 21191 nin=nin+1 if(nin.gt.nx)goto 21132 mm(k)=nin ia(nin)=k 21191 continue do 21201 j=1,nr rsq=rsq-del(j)*(2.0*gj(j)-del(j)*xv(k)) y(jx(jb:je),j)=y(jx(jb:je),j)-del(j)*x(jb:je)/xs(k) o(j)=o(j)+del(j)*xm(k)/xs(k) dlx=max(xv(k)*del(j)**2,dlx) 21201 continue continue 21131 continue 21132 continue if(nin.gt.nx)goto 21122 if(dlx .ge. thr)goto 21221 ixx=0 do 21231 j=1,ni if(iy(j).eq.1)goto 21231 if(ju(j).eq.0)goto 21231 jb=ix(j) je=ix(j+1)-1 g(j)=0.0 do 21241 k=1,nr g(j)=g(j)+ (dot_product(y(jx(jb:je),k)+o(k),w(jx(jb:je))*x(jb:je) *)/xs(j))**2 21241 continue continue g(j)=sqrt(g(j)) if(g(j) .le. ab*vp(j))goto 21261 iy(j)=1 ixx=1 21261 continue 21231 continue continue if(ixx.eq.1) go to 10880 goto 21122 21221 continue if(nlp .le. maxit)goto 21281 jerr=-m return 21281 continue 10360 continue iz=1 continue 21291 continue nlp=nlp+1 dlx=0.0 do 21301 l=1,nin k=ia(l) jb=ix(k) je=ix(k+1)-1 gkn=0.0 do 21311 j=1,nr gj(j)= dot_product(y(jx(jb:je),j)+o(j),w(jx(jb:je))*x(jb:je))/xs( *k) gk(j)=gj(j)+a(j,k)*xv(k) gkn=gkn+gk(j)**2 21311 continue continue gkn=sqrt(gkn) u=1.0-ab*vp(k)/gkn del=a(:,k) if(u .gt. 0.0)goto 21331 a(:,k)=0.0 goto 21341 21331 continue a(:,k)=gk*(u/(xv(k)+dem*vp(k))) call chkbnds(nr,gk,gkn,xv(k),cl(1,1,k), dem*vp(k),ab*vp(k),a(:,k) *,isc,jerr) if(jerr.ne.0) return 21341 continue continue del=a(:,k)-del if(maxval(abs(del)).le.0.0)goto 21301 do 21351 j=1,nr rsq=rsq-del(j)*(2.0*gj(j)-del(j)*xv(k)) y(jx(jb:je),j)=y(jx(jb:je),j)-del(j)*x(jb:je)/xs(k) o(j)=o(j)+del(j)*xm(k)/xs(k) dlx=max(xv(k)*del(j)**2,dlx) 21351 continue continue 21301 continue continue if(dlx.lt.thr)goto 21292 if(nlp .le. maxit)goto 21371 jerr=-m return 21371 continue goto 21291 21292 continue jz=0 goto 21121 21122 continue if(nin .le. nx)goto 21391 jerr=-10000-m goto 21042 21391 continue if(nin .le. 0)goto 21411 do 21421 j=1,nr ao(1:nin,j,m)=a(j,ia(1:nin)) 21421 continue continue 21411 continue kin(m)=nin rsqo(m)=1.0-rsq/ys0 almo(m)=alm lmu=m if(m.lt.mnl)goto 21041 if(flmin.ge.1.0)goto 21041 me=0 do 21431 j=1,nin if(ao(j,1,m).ne.0.0) me=me+1 21431 continue continue if(me.gt.ne)goto 21042 if(rsq0-rsq.lt.sml*rsq)goto 21042 if(rsqo(m).gt.rsqmax)goto 21042 21041 continue 21042 continue deallocate(a,mm,g,iy,gj,gk,del,o) return end subroutine multlognetn(parm,no,ni,nc,x,y,g,w,ju,vp,cl,ne,nx,nlam,f *lmin,ulam, shri,intr,maxit,xv,lmu,a0,a,m,kin,dev0,dev,alm,nlp,jer *r) implicit double precision(a-h,o-z) double precision x(no,ni),y(no,nc),g(no,nc),w(no),vp(ni),ulam(nlam *),cl(2,ni) double precision a(nx,nc,nlam),a0(nc,nlam),dev(nlam),alm(nlam),xv( *ni) integer ju(ni),m(nx),kin(nlam) double precision, dimension (:,:), allocatable :: q,r,b,bs double precision, dimension (:), allocatable :: sxp,sxpl,ga,gk,del integer, dimension (:), allocatable :: mm,is,ixx,isc allocate(b(0:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(bs(0:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(q(1:no,1:nc),stat=jerr) if(jerr.ne.0) return allocate(r(1:no,1:nc),stat=jerr) if(jerr.ne.0) return call get_int_parms(sml,eps,big,mnlam,devmax,pmin,exmx,itrace) exmn=-exmx allocate(mm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(is(1:max(nc,ni)),stat=jerr) if(jerr.ne.0) return allocate(sxp(1:no),stat=jerr) if(jerr.ne.0) return allocate(sxpl(1:no),stat=jerr) if(jerr.ne.0) return allocate(ga(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ixx(1:ni),stat=jerr) if(jerr.ne.0) return allocate(gk(1:nc),stat=jerr) if(jerr.ne.0) return allocate(del(1:nc),stat=jerr) if(jerr.ne.0) return allocate(isc(1:nc),stat=jerr) if(jerr.ne.0) return pmax=1.0-pmin emin=pmin/pmax emax=1.0/emin bta=parm omb=1.0-bta dev1=0.0 dev0=0.0 do 21441 ic=1,nc q0=dot_product(w,y(:,ic)) if(q0 .gt. pmin)goto 21461 jerr =8000+ic return 21461 continue if(q0 .lt. pmax)goto 21481 jerr =9000+ic return 21481 continue if(intr .ne. 0)goto 21501 q0=1.0/nc b(0,ic)=0.0 goto 21511 21501 continue b(0,ic)=log(q0) dev1=dev1-q0*b(0,ic) 21511 continue continue b(1:ni,ic)=0.0 21441 continue continue if(intr.eq.0) dev1=log(float(nc)) ixx=0 al=0.0 if(nonzero(no*nc,g) .ne. 0)goto 21531 b(0,:)=b(0,:)-sum(b(0,:))/nc sxp=0.0 do 21541 ic=1,nc q(:,ic)=exp(b(0,ic)) sxp=sxp+q(:,ic) 21541 continue continue goto 21551 21531 continue do 21561 i=1,no g(i,:)=g(i,:)-sum(g(i,:))/nc 21561 continue continue sxp=0.0 if(intr .ne. 0)goto 21581 b(0,:)=0.0 goto 21591 21581 continue call kazero(nc,no,y,g,w,b(0,:),jerr) if(jerr.ne.0) return 21591 continue continue dev1=0.0 do 21601 ic=1,nc q(:,ic)=b(0,ic)+g(:,ic) dev1=dev1-dot_product(w,y(:,ic)*q(:,ic)) q(:,ic)=exp(q(:,ic)) sxp=sxp+q(:,ic) 21601 continue continue sxpl=w*log(sxp) do 21611 ic=1,nc dev1=dev1+dot_product(y(:,ic),sxpl) 21611 continue continue 21551 continue continue do 21621 ic=1,nc do 21631 i=1,no if(y(i,ic).gt.0.0) dev0=dev0+w(i)*y(i,ic)*log(y(i,ic)) 21631 continue continue 21621 continue continue dev0=dev0+dev1 alf=1.0 if(flmin .ge. 1.0)goto 21651 eqs=max(eps,flmin) alf=eqs**(1.0/(nlam-1)) 21651 continue m=0 mm=0 nin=0 nlp=0 mnl=min(mnlam,nlam) bs=0.0 shr=shri*dev0 ga=0.0 do 21661 ic=1,nc r(:,ic)=w*(y(:,ic)-q(:,ic)/sxp) do 21671 j=1,ni if(ju(j).ne.0) ga(j)=ga(j)+dot_product(r(:,ic),x(:,j))**2 21671 continue continue 21661 continue continue ga=sqrt(ga) do 21681 ilm=1,nlam if(itrace.ne.0) call setpb(ilm-1) al0=al if(flmin .lt. 1.0)goto 21701 al=ulam(ilm) goto 21691 21701 if(ilm .le. 2)goto 21711 al=al*alf goto 21691 21711 if(ilm .ne. 1)goto 21721 al=big goto 21731 21721 continue al0=0.0 do 21741 j=1,ni if(ju(j).eq.0)goto 21741 if(vp(j).gt.0.0) al0=max(al0,ga(j)/vp(j)) 21741 continue continue al0=al0/max(bta,1.0d-3) al=alf*al0 21731 continue 21691 continue al2=al*omb al1=al*bta tlam=bta*(2.0*al-al0) do 21751 k=1,ni if(ixx(k).eq.1)goto 21751 if(ju(k).eq.0)goto 21751 if(ga(k).gt.tlam*vp(k)) ixx(k)=1 21751 continue continue 10880 continue continue 21761 continue ix=0 jx=ix kx=jx t=0.0 do 21771 ic=1,nc t=max(t,maxval(q(:,ic)*(1.0-q(:,ic)/sxp)/sxp)) 21771 continue continue if(t .ge. eps)goto 21791 kx=1 goto 21762 21791 continue t=2.0*t alt=al1/t al2t=al2/t do 21801 ic=1,nc bs(0,ic)=b(0,ic) if(nin.gt.0) bs(m(1:nin),ic)=b(m(1:nin),ic) r(:,ic)=w*(y(:,ic)-q(:,ic)/sxp)/t d=0.0 if(intr.ne.0) d=sum(r(:,ic)) if(d .eq. 0.0)goto 21821 b(0,ic)=b(0,ic)+d r(:,ic)=r(:,ic)-d*w dlx=max(dlx,d**2) 21821 continue 21801 continue continue continue 21831 continue nlp=nlp+nc dlx=0.0 do 21841 k=1,ni if(ixx(k).eq.0)goto 21841 gkn=0.0 do 21851 ic=1,nc gk(ic)=dot_product(r(:,ic),x(:,k))+b(k,ic)*xv(k) gkn=gkn+gk(ic)**2 21851 continue continue gkn=sqrt(gkn) u=1.0-alt*vp(k)/gkn del=b(k,:) if(u .gt. 0.0)goto 21871 b(k,:)=0.0 goto 21881 21871 continue b(k,:)=gk*(u/(xv(k)+vp(k)*al2t)) call chkbnds1(nc,gk,gkn,xv(k),cl(1,k), cl(2,k),vp(k)*al2t,alt*vp( *k),b(k,:),isc,jerr) if(jerr.ne.0) return 21881 continue continue del=b(k,:)-del if(maxval(abs(del)).le.0.0)goto 21841 do 21891 ic=1,nc dlx=max(dlx,xv(k)*del(ic)**2) r(:,ic)=r(:,ic)-del(ic)*w*x(:,k) 21891 continue continue if(mm(k) .ne. 0)goto 21911 nin=nin+1 if(nin .le. nx)goto 21931 jx=1 goto 21842 21931 continue mm(k)=nin m(nin)=k 21911 continue 21841 continue 21842 continue if(jx.gt.0)goto 21832 if(dlx.lt.shr)goto 21832 if(nlp .le. maxit)goto 21951 jerr=-ilm return 21951 continue continue 21961 continue nlp=nlp+nc dlx=0.0 do 21971 l=1,nin k=m(l) gkn=0.0 do 21981 ic=1,nc gk(ic)=dot_product(r(:,ic),x(:,k))+b(k,ic)*xv(k) gkn=gkn+gk(ic)**2 21981 continue continue gkn=sqrt(gkn) u=1.0-alt*vp(k)/gkn del=b(k,:) if(u .gt. 0.0)goto 22001 b(k,:)=0.0 goto 22011 22001 continue b(k,:)=gk*(u/(xv(k)+vp(k)*al2t)) call chkbnds1(nc,gk,gkn,xv(k),cl(1,k), cl(2,k),vp(k)*al2t,alt*vp( *k),b(k,:),isc,jerr) if(jerr.ne.0) return 22011 continue continue del=b(k,:)-del if(maxval(abs(del)).le.0.0)goto 21971 do 22021 ic=1,nc dlx=max(dlx,xv(k)*del(ic)**2) r(:,ic)=r(:,ic)-del(ic)*w*x(:,k) 22021 continue continue 21971 continue continue if(dlx.lt.shr)goto 21962 if(nlp .le. maxit)goto 22041 jerr=-ilm return 22041 continue goto 21961 21962 continue goto 21831 21832 continue if(jx.gt.0)goto 21762 do 22051 ic=1,nc if((b(0,ic)-bs(0,ic))**2.gt.shr) ix=1 if(ix .ne. 0)goto 22071 do 22081 j=1,nin k=m(j) if(xv(k)*(b(k,ic)-bs(k,ic))**2 .le. shr)goto 22101 ix=1 goto 22082 22101 continue 22081 continue 22082 continue 22071 continue do 22111 i=1,no fi=b(0,ic)+g(i,ic) if(nin.gt.0) fi=fi+dot_product(b(m(1:nin),ic),x(i,m(1:nin))) fi=min(max(exmn,fi),exmx) sxp(i)=sxp(i)-q(i,ic) q(i,ic)=min(max(emin*sxp(i),exp(fi)),emax*sxp(i)) sxp(i)=sxp(i)+q(i,ic) 22111 continue continue 22051 continue continue s=-sum(b(0,:))/nc b(0,:)=b(0,:)+s if(jx.gt.0)goto 21762 if(ix .ne. 0)goto 22131 do 22141 k=1,ni if(ixx(k).eq.1)goto 22141 if(ju(k).eq.0)goto 22141 ga(k)=0.0 22141 continue continue do 22151 ic=1,nc r(:,ic)=w*(y(:,ic)-q(:,ic)/sxp) do 22161 k=1,ni if(ixx(k).eq.1)goto 22161 if(ju(k).eq.0)goto 22161 ga(k)=ga(k)+dot_product(r(:,ic),x(:,k))**2 22161 continue continue 22151 continue continue ga=sqrt(ga) do 22171 k=1,ni if(ixx(k).eq.1)goto 22171 if(ju(k).eq.0)goto 22171 if(ga(k) .le. al1*vp(k))goto 22191 ixx(k)=1 ix=1 22191 continue 22171 continue continue if(ix.eq.1) go to 10880 goto 21762 22131 continue goto 21761 21762 continue if(kx .le. 0)goto 22211 jerr=-20000-ilm goto 21682 22211 continue if(jx .le. 0)goto 22231 jerr=-10000-ilm goto 21682 22231 continue devi=0.0 do 22241 ic=1,nc if(nin.gt.0) a(1:nin,ic,ilm)=b(m(1:nin),ic) a0(ic,ilm)=b(0,ic) do 22251 i=1,no if(y(i,ic).le.0.0)goto 22251 devi=devi-w(i)*y(i,ic)*log(q(i,ic)/sxp(i)) 22251 continue continue 22241 continue continue kin(ilm)=nin alm(ilm)=al lmu=ilm dev(ilm)=(dev1-devi)/dev0 if(ilm.lt.mnl)goto 21681 if(flmin.ge.1.0)goto 21681 me=0 do 22261 j=1,nin if(a(j,1,ilm).ne.0.0) me=me+1 22261 continue continue if(me.gt.ne)goto 21682 if(dev(ilm).gt.devmax)goto 21682 if(dev(ilm)-dev(ilm-1).lt.sml)goto 21682 21681 continue 21682 continue g=log(q) do 22271 i=1,no g(i,:)=g(i,:)-sum(g(i,:))/nc 22271 continue continue deallocate(sxp,b,bs,r,q,mm,is,ga,ixx,gk,del,sxpl) return end subroutine multsprlognetn(parm,no,ni,nc,x,ix,jx,y,g,w,ju,vp,cl,ne, *nx,nlam, flmin,ulam,shri,intr,maxit,xv,xb,xs,lmu,a0,a,m,kin,dev0, *dev,alm,nlp,jerr) implicit double precision(a-h,o-z) double precision x(*),y(no,nc),g(no,nc),w(no),vp(ni) double precision ulam(nlam),xb(ni),xs(ni),xv(ni) double precision a(nx,nc,nlam),a0(nc,nlam),dev(nlam),alm(nlam),cl( *2,ni) integer ix(*),jx(*),ju(ni),m(nx),kin(nlam) double precision, dimension (:,:), allocatable :: q,r,b,bs double precision, dimension (:), allocatable :: sxp,sxpl,ga,gk double precision, dimension (:), allocatable :: del,sc,svr integer, dimension (:), allocatable :: mm,is,iy,isc allocate(b(0:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(bs(0:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(q(1:no,1:nc),stat=jerr) if(jerr.ne.0) return allocate(r(1:no,1:nc),stat=jerr) if(jerr.ne.0) return call get_int_parms(sml,eps,big,mnlam,devmax,pmin,exmx,itrace) exmn=-exmx allocate(mm(1:ni),stat=jerr) if(jerr.ne.0) return allocate(ga(1:ni),stat=jerr) if(jerr.ne.0) return allocate(gk(1:nc),stat=jerr) if(jerr.ne.0) return allocate(del(1:nc),stat=jerr) if(jerr.ne.0) return allocate(iy(1:ni),stat=jerr) if(jerr.ne.0) return allocate(is(1:max(nc,ni)),stat=jerr) if(jerr.ne.0) return allocate(sxp(1:no),stat=jerr) if(jerr.ne.0) return allocate(sxpl(1:no),stat=jerr) if(jerr.ne.0) return allocate(svr(1:nc),stat=jerr) if(jerr.ne.0) return allocate(sc(1:no),stat=jerr) if(jerr.ne.0) return allocate(isc(1:nc),stat=jerr) if(jerr.ne.0) return pmax=1.0-pmin emin=pmin/pmax emax=1.0/emin bta=parm omb=1.0-bta dev1=0.0 dev0=0.0 do 22281 ic=1,nc q0=dot_product(w,y(:,ic)) if(q0 .gt. pmin)goto 22301 jerr =8000+ic return 22301 continue if(q0 .lt. pmax)goto 22321 jerr =9000+ic return 22321 continue b(1:ni,ic)=0.0 if(intr .ne. 0)goto 22341 q0=1.0/nc b(0,ic)=0.0 goto 22351 22341 continue b(0,ic)=log(q0) dev1=dev1-q0*b(0,ic) 22351 continue continue 22281 continue continue if(intr.eq.0) dev1=log(float(nc)) iy=0 al=0.0 if(nonzero(no*nc,g) .ne. 0)goto 22371 b(0,:)=b(0,:)-sum(b(0,:))/nc sxp=0.0 do 22381 ic=1,nc q(:,ic)=exp(b(0,ic)) sxp=sxp+q(:,ic) 22381 continue continue goto 22391 22371 continue do 22401 i=1,no g(i,:)=g(i,:)-sum(g(i,:))/nc 22401 continue continue sxp=0.0 if(intr .ne. 0)goto 22421 b(0,:)=0.0 goto 22431 22421 continue call kazero(nc,no,y,g,w,b(0,:),jerr) if(jerr.ne.0) return 22431 continue continue dev1=0.0 do 22441 ic=1,nc q(:,ic)=b(0,ic)+g(:,ic) dev1=dev1-dot_product(w,y(:,ic)*q(:,ic)) q(:,ic)=exp(q(:,ic)) sxp=sxp+q(:,ic) 22441 continue continue sxpl=w*log(sxp) do 22451 ic=1,nc dev1=dev1+dot_product(y(:,ic),sxpl) 22451 continue continue 22391 continue continue do 22461 ic=1,nc do 22471 i=1,no if(y(i,ic).gt.0.0) dev0=dev0+w(i)*y(i,ic)*log(y(i,ic)) 22471 continue continue 22461 continue continue dev0=dev0+dev1 alf=1.0 if(flmin .ge. 1.0)goto 22491 eqs=max(eps,flmin) alf=eqs**(1.0/(nlam-1)) 22491 continue m=0 mm=0 nin=0 nlp=0 mnl=min(mnlam,nlam) bs=0.0 shr=shri*dev0 ga=0.0 do 22501 ic=1,nc r(:,ic)=w*(y(:,ic)-q(:,ic)/sxp) svr(ic)=sum(r(:,ic)) do 22511 j=1,ni if(ju(j).eq.0)goto 22511 jb=ix(j) je=ix(j+1)-1 gj=dot_product(r(jx(jb:je),ic),x(jb:je)) ga(j)=ga(j)+((gj-svr(ic)*xb(j))/xs(j))**2 22511 continue continue 22501 continue continue ga=sqrt(ga) do 22521 ilm=1,nlam if(itrace.ne.0) call setpb(ilm-1) al0=al if(flmin .lt. 1.0)goto 22541 al=ulam(ilm) goto 22531 22541 if(ilm .le. 2)goto 22551 al=al*alf goto 22531 22551 if(ilm .ne. 1)goto 22561 al=big goto 22571 22561 continue al0=0.0 do 22581 j=1,ni if(ju(j).eq.0)goto 22581 if(vp(j).gt.0.0) al0=max(al0,ga(j)/vp(j)) 22581 continue continue al0=al0/max(bta,1.0d-3) al=alf*al0 22571 continue 22531 continue al2=al*omb al1=al*bta tlam=bta*(2.0*al-al0) do 22591 k=1,ni if(iy(k).eq.1)goto 22591 if(ju(k).eq.0)goto 22591 if(ga(k).gt.tlam*vp(k)) iy(k)=1 22591 continue continue 10880 continue continue 22601 continue ixx=0 jxx=ixx kxx=jxx t=0.0 do 22611 ic=1,nc t=max(t,maxval(q(:,ic)*(1.0-q(:,ic)/sxp)/sxp)) 22611 continue continue if(t .ge. eps)goto 22631 kxx=1 goto 22602 22631 continue t=2.0*t alt=al1/t al2t=al2/t do 22641 ic=1,nc bs(0,ic)=b(0,ic) if(nin.gt.0) bs(m(1:nin),ic)=b(m(1:nin),ic) r(:,ic)=w*(y(:,ic)-q(:,ic)/sxp)/t svr(ic)=sum(r(:,ic)) if(intr .eq. 0)goto 22661 b(0,ic)=b(0,ic)+svr(ic) r(:,ic)=r(:,ic)-svr(ic)*w dlx=max(dlx,svr(ic)**2) 22661 continue 22641 continue continue continue 22671 continue nlp=nlp+nc dlx=0.0 do 22681 k=1,ni if(iy(k).eq.0)goto 22681 jb=ix(k) je=ix(k+1)-1 del=b(k,:) gkn=0.0 do 22691 ic=1,nc u=(dot_product(r(jx(jb:je),ic),x(jb:je))-svr(ic)*xb(k))/xs(k) gk(ic)=u+del(ic)*xv(k) gkn=gkn+gk(ic)**2 22691 continue continue gkn=sqrt(gkn) u=1.0-alt*vp(k)/gkn if(u .gt. 0.0)goto 22711 b(k,:)=0.0 goto 22721 22711 continue b(k,:)=gk*(u/(xv(k)+vp(k)*al2t)) call chkbnds1(nc,gk,gkn,xv(k),cl(1,k),cl(2,k), vp(k)*al2t,alt*vp( *k),b(k,:),isc,jerr) if(jerr.ne.0) return 22721 continue continue del=b(k,:)-del if(maxval(abs(del)).le.0.0)goto 22681 do 22731 ic=1,nc dlx=max(dlx,xv(k)*del(ic)**2) r(jx(jb:je),ic)=r(jx(jb:je),ic) -del(ic)*w(jx(jb:je))*(x(jb:je)-x *b(k))/xs(k) 22731 continue continue if(mm(k) .ne. 0)goto 22751 nin=nin+1 if(nin .le. nx)goto 22771 jxx=1 goto 22682 22771 continue mm(k)=nin m(nin)=k 22751 continue 22681 continue 22682 continue if(jxx.gt.0)goto 22672 if(dlx.lt.shr)goto 22672 if(nlp .le. maxit)goto 22791 jerr=-ilm return 22791 continue continue 22801 continue nlp=nlp+nc dlx=0.0 do 22811 l=1,nin k=m(l) jb=ix(k) je=ix(k+1)-1 del=b(k,:) gkn=0.0 do 22821 ic=1,nc u=(dot_product(r(jx(jb:je),ic),x(jb:je)) -svr(ic)*xb(k))/xs(k) gk(ic)=u+del(ic)*xv(k) gkn=gkn+gk(ic)**2 22821 continue continue gkn=sqrt(gkn) u=1.0-alt*vp(k)/gkn if(u .gt. 0.0)goto 22841 b(k,:)=0.0 goto 22851 22841 continue b(k,:)=gk*(u/(xv(k)+vp(k)*al2t)) call chkbnds1(nc,gk,gkn,xv(k),cl(1,k),cl(2,k), vp(k)*al2t,alt*vp( *k),b(k,:),isc,jerr) if(jerr.ne.0) return 22851 continue continue del=b(k,:)-del if(maxval(abs(del)).le.0.0)goto 22811 do 22861 ic=1,nc dlx=max(dlx,xv(k)*del(ic)**2) r(jx(jb:je),ic)=r(jx(jb:je),ic) -del(ic)*w(jx(jb:je))*(x(jb:je)-x *b(k))/xs(k) 22861 continue continue 22811 continue continue if(dlx.lt.shr)goto 22802 if(nlp .le. maxit)goto 22881 jerr=-ilm return 22881 continue goto 22801 22802 continue goto 22671 22672 continue if(jxx.gt.0)goto 22602 do 22891 ic=1,nc if((b(0,ic)-bs(0,ic))**2.gt.shr) ixx=1 if(ixx .ne. 0)goto 22911 do 22921 j=1,nin k=m(j) if(xv(k)*(b(k,ic)-bs(k,ic))**2 .le. shr)goto 22941 ixx=1 goto 22922 22941 continue 22921 continue 22922 continue 22911 continue sc=b(0,ic)+g(:,ic) b0=0.0 do 22951 j=1,nin l=m(j) jb=ix(l) je=ix(l+1)-1 sc(jx(jb:je))=sc(jx(jb:je))+b(l,ic)*x(jb:je)/xs(l) b0=b0-b(l,ic)*xb(l)/xs(l) 22951 continue continue sc=min(max(exmn,sc+b0),exmx) sxp=sxp-q(:,ic) q(:,ic)=min(max(emin*sxp,exp(sc)),emax*sxp) sxp=sxp+q(:,ic) 22891 continue continue s=sum(b(0,:))/nc b(0,:)=b(0,:)-s if(jxx.gt.0)goto 22602 if(ixx .ne. 0)goto 22971 do 22981 j=1,ni if(iy(j).eq.1)goto 22981 if(ju(j).eq.0)goto 22981 ga(j)=0.0 22981 continue continue do 22991 ic=1,nc r(:,ic)=w*(y(:,ic)-q(:,ic)/sxp) do 23001 j=1,ni if(iy(j).eq.1)goto 23001 if(ju(j).eq.0)goto 23001 jb=ix(j) je=ix(j+1)-1 gj=dot_product(r(jx(jb:je),ic),x(jb:je)) ga(j)=ga(j)+((gj-svr(ic)*xb(j))/xs(j))**2 23001 continue continue 22991 continue continue ga=sqrt(ga) do 23011 k=1,ni if(iy(k).eq.1)goto 23011 if(ju(k).eq.0)goto 23011 if(ga(k) .le. al1*vp(k))goto 23031 iy(k)=1 ixx=1 23031 continue 23011 continue continue if(ixx.eq.1) go to 10880 goto 22602 22971 continue goto 22601 22602 continue if(kxx .le. 0)goto 23051 jerr=-20000-ilm goto 22522 23051 continue if(jxx .le. 0)goto 23071 jerr=-10000-ilm goto 22522 23071 continue devi=0.0 do 23081 ic=1,nc if(nin.gt.0) a(1:nin,ic,ilm)=b(m(1:nin),ic) a0(ic,ilm)=b(0,ic) do 23091 i=1,no if(y(i,ic).le.0.0)goto 23091 devi=devi-w(i)*y(i,ic)*log(q(i,ic)/sxp(i)) 23091 continue continue 23081 continue continue kin(ilm)=nin alm(ilm)=al lmu=ilm dev(ilm)=(dev1-devi)/dev0 if(ilm.lt.mnl)goto 22521 if(flmin.ge.1.0)goto 22521 me=0 do 23101 j=1,nin if(a(j,1,ilm).ne.0.0) me=me+1 23101 continue continue if(me.gt.ne)goto 22522 if(dev(ilm).gt.devmax)goto 22522 if(dev(ilm)-dev(ilm-1).lt.sml)goto 22522 22521 continue 22522 continue g=log(q) do 23111 i=1,no g(i,:)=g(i,:)-sum(g(i,:))/nc 23111 continue continue deallocate(sxp,b,bs,r,q,mm,is,sc,ga,iy,gk,del,sxpl) return end subroutine psort7(v,a,ii,jj) implicit double precision(a-h,o-z) c c puts into a the permutation vector which sorts v into c increasing order. the array v is not modified. c only elements from ii to jj are considered. c arrays iu(k) and il(k) permit sorting up to 2**(k+1)-1 elements c c this is a modification of cacm algorithm #347 by r. c. singleton, c which is a modified hoare quicksort. c dimension a(jj),v(jj),iu(20),il(20) integer t,tt integer a double precision v m=1 i=ii j=jj 10 if (i.ge.j) go to 80 20 k=i ij=(j+i)/2 t=a(ij) vt=v(t) if (v(a(i)).le.vt) go to 30 a(ij)=a(i) a(i)=t t=a(ij) vt=v(t) 30 l=j if (v(a(j)).ge.vt) go to 50 a(ij)=a(j) a(j)=t t=a(ij) vt=v(t) if (v(a(i)).le.vt) go to 50 a(ij)=a(i) a(i)=t t=a(ij) vt=v(t) go to 50 40 a(l)=a(k) a(k)=tt 50 l=l-1 if (v(a(l)).gt.vt) go to 50 tt=a(l) vtt=v(tt) 60 k=k+1 if (v(a(k)).lt.vt) go to 60 if (k.le.l) go to 40 if (l-i.le.j-k) go to 70 il(m)=i iu(m)=l i=k m=m+1 go to 90 70 il(m)=k iu(m)=j j=l m=m+1 go to 90 80 m=m-1 if (m.eq.0) return i=il(m) j=iu(m) 90 if (j-i.gt.10) go to 20 if (i.eq.ii) go to 10 i=i-1 100 i=i+1 if (i.eq.j) go to 80 t=a(i+1) vt=v(t) if (v(a(i)).le.vt) go to 100 k=i 110 a(k+1)=a(k) k=k-1 if (vt.lt.v(a(k))) go to 110 a(k+1)=t go to 100 end glmnet/vignettes/0000755000176200001440000000000013574046201013552 5ustar liggesusersglmnet/vignettes/Coxnet.Rmd0000644000176200001440000000677113556622666015510 0ustar liggesusers--- title: "Coxnet: Regularized Cox Regression" author: "Noah Simon, Jerome Friedman, Trevor Hastie and Rob Tibshirani" date: '`r Sys.Date()`' bibliography: assets/glmnet_refs.bib link-citations: true output: pdf_document: fig_caption: yes toc: yes toc_depth: 3 vignette: > %\VignetteIndexEntry{Coxnet: Regularized Cox Regression} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} --- ## Introduction We will give a short tutorial on using coxnet. Coxnet is a function which fits the Cox Model regularized by an elastic net penalty. It is used for underdetermined (or nearly underdetermined systems) and chooses a small number of covariates to include in the model. Because the Cox Model is rarely used for actual prediction, we will rather focus on finding and interpretating an appropriate model. We give a simple example of how to format data and run the Cox Model in glmnet with cross validation. Further details may be found in @coxnet, @strongrules and @block. ## Example We first load our data and set up the response. In this case $x$ must be an $n$ by $p$ matrix of covariate values --- each row corresponds to a patient and each column a covariate. $y$ is an $n$ length vector of failure/censoring times, and status is an $n$ length vector with each entry, a $1$ or a $0$, indicating whether the corresponding entry in $y$ is indicative of a failure time or right censoring time ($1$ for failure, $0$ for censoring) ```{r} library("glmnet") library("survival") patient.data <- readRDS("assets/coxnet.RDS") ``` We then call our functions to fit with the lasso penalty ($\alpha=1$), and cross validate. We set maxit = 1000 (increasing the maximum number of iterations to $1000$) because our data is relatively high dimensional, so more iterations are needed for convergence. In practice, the function will spit out an error if convergence isn't reached by the maximum number of iterations. ```{r, warning = TRUE} cv.fit <- cv.glmnet(patient.data$x, Surv(patient.data$time, patient.data$status), family="cox", maxit = 1000) fit <- glmnet(patient.data$x, Surv(patient.data$time,patient.data$status), family = "cox", maxit = 1000) ``` The Surv function packages the survival data into the form expected by glmnet. Once fit, we can view the optimal $\lambda$ value and a cross validated error plot to help evaluate our model. ```{r} plot(cv.fit) cv.fit$lambda.min ``` The left vertical line in our plot shows us where the CV-error curve hits its minimum. The right vertical line shows us the most regularized model with CV-error within$1$standard deviation of the minimum. In this case, we see that the minimum was achieved by a fairly regularized model, however the right line indicates that the null model (no coefficients included) is within$1$sd of the minimum. This might lead us to believe that in actuality the covariates are not explaining any variability. For the time being we will concern ourselves with the minimum CV-error model. We can check which covariates our model chose to be active, and see the coefficients of those covariates. ```{r} Coefficients <- coef(fit, s = cv.fit$lambda.min) Active.Index <- which(Coefficients != 0) Active.Coefficients <- Coefficients[Active.Index] ``` `coef(fit, s = cv.fit\$lambda.min)` returns the $p$ length coefficient vector of the solution corresponding to $\lambda =$`cv.fit$lambda.min`. ```{r} Active.Index Active.Coefficients ``` We see that our optimal model chose 2 active covariates ($X80$ and $X394$) each with a small positive effect on hazard. ## References glmnet/vignettes/relax.Rmd0000644000176200001440000003516613560660761015354 0ustar liggesusers--- title: "Relaxed fits and other additions in `glmnet` 3.0" author: "Trevor Hastie, Balasubramanian Narasimhan and Rob Tibshirani" date: "October 15, 2019" bibliography: assets/glmnet_refs.bib link-citations: true output: pdf_document: fig_caption: yes toc: yes toc_depth: 3 vignette: > %\VignetteIndexEntry{Relaxed fits} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} --- ## Introduction In our vignette "glmnet" we give details for fitting lasso and elastic-net regularized models, for CV and various aspects of glmnet modeling. In this vignette, we highlight some of the new tools and features in the major revision glmnet 3.0. The main edition is the introduction of the *relaxed lasso*. The idea is to take a glmnet fitted object, and then for each lambda, refit the variables in the active set without any penalization. This gives the `relaxed` fit (note, there have been other definitions of a relaxed fit, but this is the one we prefer). This could of course be done for elastic net fits as well as lasso. However, if the number of variables gets too close to the sample size N, the relaxed path will be truncated. Furthermore, for binomial and other nonlinear GLMs convergence can be an issue with our current implementation if the number of variables is too large, and perversely if the relaxed fit is too strong. Suppose the `glmnet` fitted linear predictor at $\lambda$ is $\hat\eta_\lambda(x)$ and the relaxed version is $\tilde \eta_\lambda(x)$. We also allow for shrinkage between the two: $$\tilde \eta_{\lambda,\gamma}=(1-\gamma)\tilde \eta_\lambda(x)+\gamma\hat\eta_\lambda(x).$$ $\gamma\in[0,1]$ is an additional tuning parameter which can be selected by cross validation. The debiasing will potentially improve prediction performance, and CV will typically select a model with a smaller number of variables. This procedure is very competitive with forward-stepwise and best-subset regression, and has a considerable speed advantage when the number of variables is large. This is especially true for best-subset, but even so for forward stepwise. The latter has to plod through the variables one-at-a-time, while glmnet will just plunge in and find a good active set. Further details may be found in @glmnet, @coxnet, @strongrules, @block and @best_subset. ## Simple relaxed fit To get things going, we show the most basic use. We use the same data used in the `glmnet` vignette. ```{r} library(glmnet) data(QuickStartExample) fit=glmnet(x,y, relax=TRUE) print(fit) ``` There is an extra column `%Dev R` where the `R` stands for "relaxed", which is the percent deviance explained by the relaxed fit. This is always higher than its neighboring column, which is the same for the penalized fit (on the training data). The fit object is class `relaxed`, which inherits from class `glmnet`. One can plot it, with additional flexibility. ```{r} par(mfrow=c(1,3)) plot(fit) plot(fit,gamma=0.5) plot(fit,gamma=0) ``` So again, `gamma=1` is the traditional `glmnet` fit, while `gamma=0` is the unpenalized fit, and `gamma=0.5` is a mixture of the two (at the coefficient level, and hence also the linear predictors). We can also select `gamma` using `cv.glmnet`, which by default uses the 5 values `c(0, 0.25, 0.5, 0.75, 1)`. ```{r} cfit=cv.glmnet(x,y,relax=TRUE) plot(cfit) ``` The plot command has an `se.bands` option if you don't like the default shading of these bands. Just like before, you can make predictions from a CV object, and it uses the selected values for `lambda` and `gamma`. ```{r, eval=FALSE} predict(cvfit,newx) ``` A new feature in `glmnet` is a print method for `cv.glmnet` and a `cv.relaxed` object. ```{r} print(cfit) ``` ## More details on relaxed fitting Although `glmnet` has a `relax` option, you can created a relaxed version by post-processing a `glmnet` object. ```{r `relaxed`} fit=glmnet(x,y) fitr=relax.glmnet(fit,x=x,y=y) ``` This will rarely need to be done; one use case is if the original fit took a long time, and the user wanted to avoid refitting it. Note that in the call the arguments are named, since they are passed in via the `...` argument to `relax.glmnet`. Needless to say, *any* of the families fit by `glmnet` can also be fit with the `relaxed` option. As mentioned, a `relaxed` object is also a `glmnet` object. Apart from the class modification, it has an additional componet named `relaxed` which is itself a `glmnet` object, but with the relaxed coefficients. The default behavior of extractor functions like `predict` and `coef`, as well as `plot` will be to present results from the `glmnet` fit, unless a value of `gamma` is given different from the default value `gamma=1` (see the plots above). The `print` method gives additional info on the relaxed fit. Likewise, a `cv.relaxed` object inherits from class `cv.glmnet`. Here the `predict` method by default uses the optimal relaxed fit; if predictions from the CV-optimal *original* `glmnet` fit are desired, one can directly use `predict.cv.glmnet`. Similarly for the `print` command, which we illustrate here. ```{r} print(cfit) print.cv.glmnet(cfit) ``` ## Relaxed fits and glms `glmnet` itself is used to fit the relaxed fits, by using a single value of zero for `lambda`. However, for nonlinear models such as binomial, multinomial and poisson, there can be convergence issues. This is because `glmnet` does not do stepsize optimization, rather relying on the pathwise fit to stay in the "quadratic" zone of the log likelihood. We have an optional `path=TRUE` option for `relax.glmnet`, which actually fits a regurized path toward the `lambda=0` solution, and thus avoids the issue. The default is `path=FALSE` since this option adds to the computing time. ### Forward stepwise and relaxed fit One use case for a relaxed fit is as a faster version of forward stepwise regression. With a large number `p` of variables, forward-stepwise regression can be tedious. Lasso on the other hand, because of its convexity, can plunge in and identify good candidate sets of variables over 100 values of `lambda`, even though `p` could be in the 10s of thousands. In a case like this, one can have `cv.glmnet` do the selection. ```{r} fitr=cv.glmnet(x,y,gamma=0,relax=TRUE) plot(fitr) ``` Notice that we only allow `gamma=0`, so in this case we are not considering the blended fits. ## Progress bar We finally have a progress bar for `glmnet` and `cv.glmnet`. Ever run a job on a big dataset, and wonder how long it will take? Now you can use the `trace.it = TRUE` argument to these functions. ```{r, eval=FALSE} fit=glmnet(x,y,trace=TRUE) ``` ``##`` `` |================================== |65%`` Here we abbreviated the argument to `trace`. This display changes in place as the fit is produced. Also very helpful with `cv.glmnet` ```{r, eval=FALSE} fit=cv.glmnet(x,y,trace=TRUE) ``` ``##`` `` Training`` `` |=============================================| 100%`` `` Fold: 1/10`` `` |=============================================| 100%`` `` Fold: 2/10`` `` |=============================================| 100%`` `` Fold: 3/10`` `` |=============================================| 100%`` `` Fold: 4/10`` `` |=============================================| 100%`` `` Fold: 5/10`` `` |=============================================| 100%`` `` Fold: 6/10`` `` |============================= | 70%`` Tracing of the folds works a little differently when distributed computing is used. Here the `trace` argument should be used in each call to `glmnet` or `cv.glmnet`. One can set this option session wide via a call to `glmnet.control` with its new `itrace` argument: ```{r, eval=FALSE} glmnet.control(itrace=1) ``` To reset it, one makes a similar call and sets `itrace=0`. ## C index for Cox models We have a new performance measure for the Cox model: the Harrel *C index*. This is like the AUC measure of concordance for survival data, but only considers comparable pairs. Pure concordance would record the fraction of pairs for which the order of the death times agree with the order of the predicted risk. But with survival data, if an observation is right censored at a time *before* another observation's death time, they are not comparable. ```{r} data(CoxExample) ``` ```{r} cvfit=cv.glmnet(x,y,family="cox",type.measure="C") plot(cvfit) ``` ## Assessing models on test data Once we have fit a series of models using `glmnet`, we often assess their performance on a set of evaluation or test data. We usually go through the process of building a prediction matrix, and then deciding on the measure, and computing the values for a series of values for `lambda` and now `gamma`. Here we provide three functions for making these tasks easier. ### Performance measures The function `assess.glmnet` computes the same performance measures produced by `cv.glmnet`, but on a validation or test dataset. ```{r} data(BinomialExample) itrain=1:70 fit=glmnet(x[itrain,],y[itrain],family="binomial",nlambda=20) assess.glmnet(fit,newx=x[-itrain,],newy=y[-itrain]) ``` This produces a list with *all* the measures suitable for a binomial model, computed for the entire sequence of lambdas in the fit object. Here the function identifies the model family from the fit object. A second use case builds the prediction matrix first ```{r, eval=FALSE} pred=predict(fit,newx=x[-itrain,]) assess.glmnet(pred,newy=y[-itrain],family="binomial") ``` Here we have to provide the `family` as an argument; the results (not shown) are the same. Users can see the various measures suitable for each family via ```{r} glmnet.measures() ``` The assess function can also take the result of `cv.glmnet` as input. In this case the predictions are made at the optimal values for the parameter(s). ```{r} cfit=cv.glmnet(x[itrain,],y[itrain],family="binomial", nlambda = 30) assess.glmnet(cfit,newx=x[-itrain,],newy=y[-itrain]) ``` This used the default value of `s=lambda.1se`, just like `predict` would have done. Users can provide additional arguments that get passed on to predict: ```{r} assess.glmnet(cfit,newx=x[-itrain,],newy=y[-itrain], s="lambda.min") ``` One interesting use case is to get the results of CV using other measures, via the `keep` argument. In this case the `fit.preval` object is a matrix of prevalidated predictions made using the folds `foldid` ```{r} cfit=cv.glmnet(x,y,family="binomial",keep=TRUE, nlambda = 30) assess.glmnet(cfit$fit.preval,newy=y,family="binomial") ``` Users can verify that the first measure here `deviance` is identical to the component `cvm` on the `cfit` object. ### ROC curves for binomial data In the special case of binomial models, users often would like to see the ROC curve for validation or test data. Here the function `roc.glmnet` provides the goodies. Its first argument is as in `assess.glmnet`. Here we illustrate one use case, using the prevlidated CV fit as before. ```{r} cfit=cv.glmnet(x,y,family="binomial", type.measure="auc", keep=TRUE) rocs=roc.glmnet(cfit$fit.preval,newy=y) which=match(cfit$lambda.min,cfit$lambda) plot(rocs[[which]],type="l") nopr=sapply(rocs,lines,col="grey") lines(rocs[[which]],lwd=2,col="red") ``` In this case `roc.glmnet` returns a list of cross-validated ROC data, one for each model along the path. In the third line we identify the CV winner. Then we plot all the curves in grey, and the winner in red. ### Confusion matrices for classification For binomial and multinomial models, we often which to examine the classification performance on new data. The function `confusion.glmnet` will do that. ```{r} data(MultinomialExample) set.seed(101) itrain=sample(1:500,400,replace=FALSE) cfit=cv.glmnet(x[itrain,],y[itrain],family="multinomial") cnf=confusion.glmnet(cfit,newx=x[-itrain,],newy=y[-itrain]) print(cnf) ``` It produces a table of class `confusion.table` which inherits from calss `table`, and we also provide a print method. The first argument to `confusion.glmnet` should be either a `glmnet` object, or a `cv.glmnet` object, from which predictions can be made, or a matrix/array of predictions, such as the *kept* `fit.predval` object from `cv.glmnet`. In the second case we need to specify the `family`, otherwise *confusion* can exist between `binomial` and `multinomial` prediction matrices. Here we show a multinomial example ```{r} cfit=cv.glmnet(x,y,family="multinomial",type="class",keep=TRUE) cnf=confusion.glmnet(cfit$fit.preval,newy=y,family="multinomial") which=match(cfit$lambda.min,cfit$lambda) print(cnf[[which]]) ``` Since the `fit.preval` object has predictions for the whole path, the result of `confusion.glmnet` here is a list of confusion tables. We identify and print the one corresponding to the minimum classification error. ## Fitting big and/or sparse GLMs We include a function `bigGlm` for fitting a single GLM model (unpenalized), but allowing all the options of `glmnet`. In other words, coefficient upper and/or lower bounds and sparse `x` matrices. This is not too much more than fitting a model with a single value of `lambda=0` (with some protection from edge cases). There is also a `predict` and `print` method. ```{r} data(BinomialExample) fit=bigGlm(x,y,family="binomial",lower.limits=-1) print(fit) ``` ## Producing x from mixed variables, and missing data We have created a function `makeX` that makes it easy to create the model matrix `x` needed as input to `glmnet`. It takes as input a data frame, which can contain vectors, matrices and factors. Some of the features are * Factors are *one-hot* encoded to form indicator matrices * Missing values in the resultant matrix can be replaced by the column means * The `sparse` option returns a matrix in column-sparse format. This is useful if the data are large, and factors have many levels. * Two dataframes can be provided, `train` and `test`. This ensures the factor levels correspond, and also imputes missing data in the test data from means in the training data. We start with a simple case with some factors. ```{r} set.seed(101) X = matrix(rnorm(20),10,2) X3=sample(letters[1:3],10,replace=TRUE) X4=sample(LETTERS[1:3],10,replace=TRUE) df=data.frame(X,X3,X4) makeX(df) ``` Or if a sparse output was desired: ```{r} makeX(df,sparse=TRUE) ``` And now some missing values ```{r} Xn=X Xn[3,1]=NA;Xn[5,2]=NA X3n=X3; X3n[6]=NA X4n=X4 X4n[9]=NA dfn=data.frame(Xn,X3n,X4n) makeX(dfn) ``` which we can replace with column-mean imputations (and make sparse, if we like) ```{r} makeX(dfn,na.impute=TRUE,sparse=TRUE) ``` Finally if a test set is available as well ```{r} X = matrix(rnorm(10),5,2) X3=sample(letters[1:3],5,replace=TRUE) X4=sample(LETTERS[1:3],5,replace=TRUE) Xn=X Xn[3,1]=NA;Xn[5,2]=NA X3n=X3; X3n[1]=NA X4n=X4 X4n[2]=NA dftn=data.frame(Xn,X3n,X4n) makeX(dfn,dftn,na.impute=TRUE, sparse=TRUE) ``` ## References glmnet/vignettes/assets/0000755000176200001440000000000013555633674015073 5ustar liggesusersglmnet/vignettes/assets/coxnet.RDS0000644000176200001440000117540313552126664016751 0ustar liggesusers\wuB&" DJNDP"  {ｄ $M;{>;[Ny>~ͽ$|>rN2e)۔??]p/Lzp7L=`nԦ߭i,n检~'na͕~/Wcw>׏:ex<$+Kœk7h[w ~Au7k}?vK㥷z5k|#щ>/{W̫QA3e~*wmczѓ2~U{}Z{/Ԗ]u3Hz/fH@ғ0%)oTk׋- Yu*kĚ7e|WWO/>̲!yb,ҊEߑ܏KnMidsG|@FTqOҽO9!>w{#cum!tLi֓Ps߿p~wZ&=sʭ~.ߋ-ZI _>̽d|!htdܱ<:"Os_:C2VyO<~܅jtn=l~;.L+y=.~Y0P'?n%w}!u+K{=a/;ddf ]s) z#%za}c]CgDE *]7^Ջh|,=J<q9=I;:~zt\wӋ|W=?V"ν;oͿ4-ߑĵoyfY0{SҒ{K_KsO׭wM{>du Hz8;zk8IO~AӻrE>8C>\3?^9e,ݼ Y_-'vRGw?xJI?Ο$}嶖C_9}Ͼk$}m?y5u2M܋$sQXF_lsKF&2gK#?Aޛ+CSgW5V=RS~CGz]xϛ|[kEyӋ% ?]Ր{H}Oo˽_~W{2y+OIxn{ P6xKcva~i4wrܔ9nꂭ?-vnW3IOt2{8ˤs≑o<2vf>1r='ozQ퍯|i\];cr&9|ÿо Y=Ed~Grހ}zMOsqFL~ajgQ<:e߯x^Oovܕd[~Y=b˽^x)|us%ԗkf-I>Ss'^'mc%4wS\U87vu4w}1!o t0OzF?%)_<[?Ԏ?[{&?ϵ/%;F_-ܱZY5/prRIuùܒO?*?_ܯljqx kI~yTe..=niڃzCNm uiGLp/PnUlX_OUK>~m3y ݺ|3ݖ[%׋78ٴ}]uq4o䯙,w)nJn[n Vgȸ[o:>ŲWlsD&w釧܋h<1ȑ/CKe}/&pU!:/*w <7o=䤗9I;e­?T_xOr ;P)!C2ܺ1.orSN~% /]p:cq4N{܃nIyq wjlr;O^52^DeA#c rNgFD8ƽ_wi=uTߣuDrL:QFYO ni_p8?JksRh&/ɿ@7۝ϚN*ʡ/h=#k7gYpEJ;ԑ1viq_ss!kU%=v\ۇk5dm֭/XOٯeԗw ߣOo ~Gխ/I%?|#Gxr*:}yj>Gܹ'|]Re\=k|#ycsJ{?tA;N{ls ~R?rt^寫uƩ{ʈu KؔWOrl FΗk]}c~VX=Y<|K>Rg%1yi`֮iZùhyV{^<;pƵL8J6[WNG^wj+KZ}Kݗ)] iwhj똬h'(s[nt}rԲ|rGVul安'd}Ǝ+^{X}ۤ`jZAjrꄼGܲ=--ə{Oϝ7Vô,׾-{zv^M<+7j޹q.Σr/{ι͗S>Wgi$/篑Zι[.nhOlyfV}77QTuRwpEi wjj}BMc%,)((=*^wNJ=8EVVN-ΗU~K\k/3d(AcΏܧZoOԹͺ@};ݥ3ׇj|ãͮZu%WJ3]<x s?2/#o.rnd+lΝ{ '}Bm/ծaW'Is9h/qNm;}_6)#k[3/N4o{68Q;\_|֑|[-cLPgai6iϼZک3{ZwHC1w|u~ C-s"ԓnw}tL͟i7] tn4ȳ4E\vxtvq2BQ)眦n]\ahӦ^+s?J}.I𞬏=4]+ԛrn,m7z,SI w_zT~@^:Mz]K_5_7V$si eq@O_亢Γk қ~+[XoAؓg1p!9ߺ=:ɷa7H a%;nr 1^V7o?H3sl {/X[g/˦޲QN/ &4DOhǍW%ncΡwJ/=:|## o}+ؑ׿p 9dy[;}b0ߎ\ovUCv=jdWY'_cnj5m+v`n*<,|fWғQl*}^sn^̍C76DnRoܳ{ww5E]Tƹf}Z6p; 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Ο<`e,~p2L3Ow xlYߣyae!;euAw/eqg>^zÒ7iurɖKzl D%A/:<(xWGxziIX.\71WN7?Vt],auK9\, ?J#(n?fqLoD'jiz5ƜΣD{ue <[{51z)r4翈t`jػmkev;}TЗϥ1 (ii\JZj~J,ws+ ?Oqf7ȣ7zMH.dRg e& UۈkoK$x>%72/-μt)tMsgW|o{b9IpX絹ߢ172[/pVt>G\ᙬ( :| WHf786NO> u?ZOyml\q?9%#kg|duóYg_dfJ rȋqˣFw'ml> t s*y]=oj_5)o3 Q7K&}Nxe*<1qA]n]&̇~|?s5p/Xs=u B|NַB Ar[leNcn/_SaޅyrN8sVyu#iJOCۘ›ppglᗨٳGl%|qX  5ݠ>/9}GMr?ijG< ?cΟ!o\6Ic 錴n~<#Go1yf.Gࣚ_N7Zڅƃ'OϷYn~o|tXG [O?g1&ooFͧ<W㬧G!Ek% y: ߻fk>O!o% = Nh>X Uo{berVbABg|bMr)O9lσn?>6_07?Gx_O=m穟qϺbװw }}_/cܿ+~}OWؤL=?Cşşs2~ wN= `~>~Q|'}E>{z?Ɵg;?}SuOO޷ߩ4~i|=;ün{' )~_??}߾s߮\;~m73?|ͷ=^}޷; glmnet/vignettes/assets/glmnet_refs.bib0000644000176200001440000001054313555633674020061 0ustar liggesusers@article{glmnet, author = {Jerome Friedman and Trevor Hastie and Robert Tibshirani}, title = {Regularization Paths for Generalized Linear Models via Coordinate Descent}, journal = {Journal of Statistical Software, Articles}, volume = {33}, number = {1}, year = {2010}, keywords = {}, abstract = {We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.}, issn = {1548-7660}, pages = {1--22}, doi = {10.18637/jss.v033.i01}, url = {https://www.jstatsoft.org/v033/i01} } @article{coxnet, author = {Noah Simon and Jerome Friedman and Trevor Hastie and Robert Tibshirani}, title = {Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent}, journal = {Journal of Statistical Software, Articles}, volume = {39}, number = {5}, year = {2011}, keywords = {}, abstract = {We introduce a pathwise algorithm for the Cox proportional hazards model, regularized by convex combinations of l1 and l2 penalties (elastic net). Our algorithm fits via cyclical coordinate descent, and employs warm starts to find a solution along a regularization path. We demonstrate the efficacy of our algorithm on real and simulated data sets, and find considerable speedup between our algorithm and competing methods.}, issn = {1548-7660}, pages = {1--13}, doi = {10.18637/jss.v039.i05}, url = {https://www.jstatsoft.org/v039/i05} } @article{strongrules, author = {Robert Tibshirani and Jacob Bien and Jerome Friedman and Trevor Hastie and Noah Simon and Jonathan Taylor and Ryan Tibshirani}, title = {Strong rules for discarding predictors in lasso-type problems}, journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, volume = {74}, number = {2}, pages = {245-266}, keywords = {Convex optimization, Lasso, l1-regularization, Screening, Sparsity}, doi = {10.1111/j.1467-9868.2011.01004.x}, url = {https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9868.2011.01004.x}, eprint = {https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-9868.2011.01004.x}, abstract = {Summary.  We consider rules for discarding predictors in lasso regression and related problems, for computational efficiency. El Ghaoui and his colleagues have proposed ‘SAFE’ rules, based on univariate inner products between each predictor and the outcome, which guarantee that a coefficient will be 0 in the solution vector. This provides a reduction in the number of variables that need to be entered into the optimization. We propose strong rules that are very simple and yet screen out far more predictors than the SAFE rules. This great practical improvement comes at a price: the strong rules are not foolproof and can mistakenly discard active predictors, i.e. predictors that have non-zero coefficients in the solution. We therefore combine them with simple checks of the Karush–Kuhn–Tucker conditions to ensure that the exact solution to the convex problem is delivered. Of course, any (approximate) screening method can be combined with the Karush–Kuhn–Tucker conditions to ensure the exact solution; the strength of the strong rules lies in the fact that, in practice, they discard a very large number of the inactive predictors and almost never commit mistakes. We also derive conditions under which they are foolproof. Strong rules provide substantial savings in computational time for a variety of statistical optimization problems.}, year = {2012} } @misc{block, Author = {Noah Simon and Jerome Friedman and Trevor Hastie}, Title = {A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression}, Year = {2013}, Eprint = {arXiv:1311.6529}, } @misc{best_subset, Author = {Trevor Hastie and Robert Tibshirani and Ryan Tibshirani}, Title = {Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso}, Year = {2017}, Eprint = {arXiv:1707.08692}, }glmnet/vignettes/glmnet.Rmd0000644000176200001440000014705213557666310015527 0ustar liggesusers--- title: "An Introduction to `glmnet`" author: "Trevor Hastie and Junyang Qian" date: "September 13, 2016" bibliography: assets/glmnet_refs.bib link-citations: true output: pdf_document: fig_caption: yes toc: yes toc_depth: 3 vignette: > %\VignetteIndexEntry{An Introduction to glmnet} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} --- ## Introduction Glmnet is a package that fits a generalized linear model via penalized maximum likelihood. The regularization path is computed for the lasso or elasticnet penalty at a grid of values for the regularization parameter lambda. The algorithm is extremely fast, and can exploit sparsity in the input matrix `x`. It fits linear, logistic and multinomial, poisson, and Cox regression models. A variety of predictions can be made from the fitted models. It can also fit multi-response linear regression. The authors of glmnet are Jerome Friedman, Trevor Hastie, Rob Tibshirani and Noah Simon, and the R package is maintained by Trevor Hastie. The matlab version of glmnet is maintained by Junyang Qian. This vignette describes the usage of glmnet in R. There is an additional vignette for the new `relaxed` features in `glmnet`, along with some new capabilities. There is as well a vignette devoted to Cox models in `glmnet`. `glmnet` solves the following problem $$ \min_{\beta_0,\beta} \frac{1}{N} \sum_{i=1}^{N} w_i l(y_i,\beta_0+\beta^T x_i) + \lambda\left[(1-\alpha)||\beta||_2^2/2 + \alpha ||\beta||_1\right], $$ over a grid of values of $\lambda$ covering the entire range. Here $l(y,\eta)$ is the negative log-likelihood contribution for observation $i$; e.g. for the Gaussian case it is $\frac{1}{2}(y-\eta)^2$. The _elastic-net_ penalty is controlled by $\alpha$, and bridges the gap between lasso ($\alpha=1$, the default) and ridge ($\alpha=0$). The tuning parameter $\lambda$ controls the overall strength of the penalty. It is known that the ridge penalty shrinks the coefficients of correlated predictors towards each other while the lasso tends to pick one of them and discard the others. The elastic-net penalty mixes these two; if predictors are correlated in groups, an $\alpha=0.5$ tends to select the groups in or out together. This is a higher level parameter, and users might pick a value upfront, else experiment with a few different values. One use of $\alpha$ is for numerical stability; for example, the elastic net with $\alpha = 1 - \epsilon$ for some small $\epsilon > 0$ performs much like the lasso, but removes any degeneracies and wild behavior caused by extreme correlations. The `glmnet` algorithms use cyclical coordinate descent, which successively optimizes the objective function over each parameter with others fixed, and cycles repeatedly until convergence. The package also makes use of the strong rules for efficient restriction of the active set. Due to highly efficient updates and techniques such as warm starts and active-set convergence, our algorithms can compute the solution path very fast. The code can handle sparse input-matrix formats, as well as range constraints on coefficients. The core of `glmnet` is a set of fortran subroutines, which make for very fast execution. The package also includes methods for prediction and plotting, and a function that performs K-fold cross-validation. The theory and algorithms in this implementation are described in @glmnet, @coxnet, @strongrules and @block . ## Installation Like many other R packages, the simplest way to obtain `glmnet` is to install it directly from CRAN. Type the following command in R console: ```{r, eval=FALSE} install.packages("glmnet", repos = "https://cran.us.r-project.org") ``` Users may change the `repos` options depending on their locations and preferences. Other options such as the directories where to install the packages can be altered in the command. For more details, see `help(install.packages)`. Here the R package has been downloaded and installed to the default directories. Alternatively, users can download the package source from [CRAN](https://cran.r-project.org/package=glmnet) and type Unix commands to install it to the desired location. ## Quick Start The purpose of this section is to give users a general sense of the package, including the components, what they do and some basic usage. We will briefly go over the main functions, see the basic operations and have a look at the outputs. Users may have a better idea after this section what functions are available, which one to choose, or at least where to seek help. More details are given in later sections. First, we load the `glmnet` package: ```{r} library(glmnet) ``` The default model used in the package is the Guassian linear model or "least squares", which we will demonstrate in this section. We load a set of data created beforehand for illustration. Users can either load their own data or use those saved in the workspace. ```{r} data(QuickStartExample) ``` The command loads an input matrix `x` and a response vector `y` from this saved R data archive. We fit the model using the most basic call to `glmnet`. ```{r} fit = glmnet(x, y) ``` "fit" is an object of class `glmnet` that contains all the relevant information of the fitted model for further use. We do not encourage users to extract the components directly. Instead, various methods are provided for the object such as `plot`, `print`, `coef` and `predict` that enable us to execute those tasks more elegantly. We can visualize the coefficients by executing the `plot` function: ```{r} plot(fit) ``` Each curve corresponds to a variable. It shows the path of its coefficient against the $\ell_1$-norm of the whole coefficient vector at as $\lambda$ varies. The axis above indicates the number of nonzero coefficients at the current $\lambda$, which is the effective degrees of freedom (_df_) for the lasso. Users may also wish to annotate the curves; this can be done by setting `label = TRUE` in the plot command. A summary of the `glmnet` path at each step is displayed if we just enter the object name or use the `print` function: ```{r height = 4} print(fit) ``` It shows from left to right the number of nonzero coefficients (`Df`), the percent (of null) deviance explained (`%dev`) and the value of $\lambda$ (`Lambda`). Although by default `glmnet` calls for 100 values of `lambda` the program stops early if `%dev% does not change sufficently from one lambda to the next (typically near the end of the path.) We can obtain the actual coefficients at one or more $\lambda$'s within the range of the sequence: ```{r} coef(fit,s=0.1) ``` (why `s` and not `lambda`? In case later we want to allow one to specify the model size in other ways.) Users can also make predictions at specific $\lambda$'s with new input data: ```{r} set.seed(29) nx = matrix(rnorm(10*20),10,20) predict(fit,newx=nx,s=c(0.1,0.05)) ``` The function `glmnet` returns a sequence of models for the users to choose from. In many cases, users may prefer the software to select one of them. Cross-validation is perhaps the simplest and most widely used method for that task. `cv.glmnet` is the main function to do cross-validation here, along with various supporting methods such as plotting and prediction. We still act on the sample data loaded before. ```{r} cvfit = cv.glmnet(x, y) ``` `cv.glmnet` returns a `cv.glmnet` object, which is "cvfit" here, a list with all the ingredients of the cross-validation fit. As for `glmnet`, we do not encourage users to extract the components directly except for viewing the selected values of $\lambda$. The package provides well-designed functions for potential tasks. We can plot the object. ```{r} plot(cvfit) ``` It includes the cross-validation curve (red dotted line), and upper and lower standard deviation curves along the $\lambda$ sequence (error bars). Two selected $\lambda$'s are indicated by the vertical dotted lines (see below). We can view the selected $\lambda$'s and the corresponding coefficients. For example, ```{r} cvfit$lambda.min ``` `lambda.min` is the value of $\lambda$ that gives minimum mean cross-validated error. The other $\lambda$ saved is `lambda.1se`, which gives the most regularized model such that error is within one standard error of the minimum. To use that, we only need to replace `lambda.min` with `lambda.1se` above. ```{r} coef(cvfit, s = "lambda.min") ``` Note that the coefficients are represented in the sparse matrix format. The reason is that the solutions along the regularization path are often sparse, and hence it is more efficient in time and space to use a sparse format. If you prefer non-sparse format, pipe the output through `as.matrix()`. Predictions can be made based on the fitted `cv.glmnet` object. Let's see a toy example. ```{r} predict(cvfit, newx = x[1:5,], s = "lambda.min") ``` `newx` is for the new input matrix and `s`, as before, is the value(s) of $\lambda$ at which predictions are made. That is the end of `glmnet` 101. With the tools introduced so far, users are able to fit the entire elastic net family, including ridge regression, using squared-error loss. In the package, there are many more options that give users a great deal of flexibility. To learn more, move on to later sections. ## Linear Regression Linear regression here refers to two families of models. One is `gaussian`, the Gaussian family, and the other is `mgaussian`, the multiresponse Gaussian family. We first discuss the ordinary Gaussian and the multiresponse one after that. ### Gaussian Family `gaussian ` is the default family option in the function `glmnet`. Suppose we have observations $x_i \in \mathbb{R}^p$ and the responses $y_i \in \mathbb{R}, i = 1, \ldots, N$. The objective function for the Gaussian family is $$ \min_{(\beta_0, \beta) \in \mathbb{R}^{p+1}}\frac{1}{2N} \sum_{i=1}^N (y_i -\beta_0-x_i^T \beta)^2+\lambda \left[ (1-\alpha)||\beta||_2^2/2 + \alpha||\beta||_1\right], $$ where $\lambda \geq 0$ is a complexity parameter and $0 \leq \alpha \leq 1$ is a compromise between ridge ($\alpha = 0$) and lasso ($\alpha = 1$). Coordinate descent is applied to solve the problem. Specifically, suppose we have current estimates $\tilde{\beta_0}$ and $\tilde{\beta}_\ell$ $\forall j\in 1,]\ldots,p$. By computing the gradient at $\beta_j = \tilde{\beta}_j$ and simple calculus, the update is $$ \tilde{\beta}_j \leftarrow \frac{S(\frac{1}{N}\sum_{i=1}^N x_{ij}(y_i-\tilde{y}_i^{(j)}),\lambda \alpha)}{1+\lambda(1-\alpha)}, $$ where $\tilde{y}_i^{(j)} = \tilde{\beta}_0 + \sum_{\ell \neq j} x_{i\ell} \tilde{\beta}_\ell$, and $S(z, \gamma)$ is the soft-thresholding operator with value $\text{sign}(z)(|z|-\gamma)_+$. This formula above applies when the `x` variables are standardized to have unit variance (the default); it is slightly more complicated when they are not. Note that for "family=gaussian", `glmnet` standardizes $y$ to have unit variance before computing its lambda sequence (and then unstandardizes the resulting coefficients); if you wish to reproduce/compare results with other software, best to supply a standardized $y$ first (Using the "1/N" variance formula). `glmnet` provides various options for users to customize the fit. We introduce some commonly used options here and they can be specified in the `glmnet` function. * `alpha` is for the elastic-net mixing parameter $\alpha$, with range $\alpha \in [0,1]$. $\alpha = 1$ is the lasso (default) and $\alpha = 0$ is the ridge. * `weights` is for the observation weights. Default is 1 for each observation. (Note: `glmnet` rescales the weights to sum to N, the sample size.) * `nlambda` is the number of $\lambda$ values in the sequence. Default is 100. * `lambda` can be provided, but is typically not and the program constructs a sequence. When automatically generated, the $\lambda$ sequence is determined by `lambda.max` and `lambda.min.ratio`. The latter is the ratio of smallest value of the generated $\lambda$ sequence (say `lambda.min`) to `lambda.max`. The program then generated `nlambda` values linear on the log scale from `lambda.max` down to `lambda.min`. `lambda.max` is not given, but easily computed from the input $x$ and $y$; it is the smallest value for `lambda` such that all the coefficients are zero. For `alpha=0` (ridge) `lambda.max` would be $\infty$; hence for this case we pick a value corresponding to a small value for `alpha` close to zero.) * `standardize` is a logical flag for `x` variable standardization, prior to fitting the model sequence. The coefficients are always returned on the original scale. Default is `standardize=TRUE`. For more information, type `help(glmnet)` or simply `?glmnet`. As an example, we set $\alpha = 0.2$ (more like a ridge regression), and give double weights to the latter half of the observations. To avoid too long a display here, we set `nlambda` to 20. In practice, however, the number of values of $\lambda$ is recommended to be 100 (default) or more. In most cases, it does not come with extra cost because of the warm-starts used in the algorithm, and for nonlinear models leads to better convergence properties. ```{r} fit = glmnet(x, y, alpha = 0.2, weights = c(rep(1,50),rep(2,50)), nlambda = 20) ``` We can then print the `glmnet` object. ```{r} print(fit) ``` This displays the call that produced the object `fit` and a three-column matrix with columns `Df` (the number of nonzero coefficients), `%dev` (the percent deviance explained) and `Lambda` (the corresponding value of $\lambda$). (Note that the `digits` option can used to specify significant digits in the printout.) Here the actual number of $\lambda$'s here is less than specified in the call. The reason lies in the stopping criteria of the algorithm. According to the default internal settings, the computations stop if either the fractional change in deviance down the path is less than $10^{-5}$ or the fraction of explained deviance reaches $0.999$. From the last few lines , we see the fraction of deviance does not change much and therefore the computation ends when meeting the stopping criteria. We can change such internal parameters. For details, see the Appendix section or type `help(glmnet.control)`. We can plot the fitted object as in the previous section. There are more options in the `plot` function. Users can decide what is on the X-axis. `xvar` allows three measures: "norm" for the $\ell_1$-norm of the coefficients (default), "lambda" for the log-lambda value and "dev" for %deviance explained. Users can also label the curves with variable sequence numbers simply by setting `label = TRUE`. Let's plot "fit" against the log-lambda value and with each curve labeled. ```{r} plot(fit, xvar = "lambda", label = TRUE) ``` Now when we plot against %deviance we get a very different picture. This is percent deviance explained on the training data. What we see here is that toward the end of the path this value are not changing much, but the coefficients are "blowing up" a bit. This lets us focus attention on the parts of the fit that matter. This will especially be true for other models, such as logistic regression. ```{r} plot(fit, xvar = "dev", label = TRUE) ``` We can extract the coefficients and make predictions at certain values of $\lambda$. Two commonly used options are: * `s` specifies the value(s) of $\lambda$ at which extraction is made. * `exact` indicates whether the exact values of coefficients are desired or not. That is, if `exact = TRUE`, and predictions are to be made at values of `s` not included in the original fit, these values of `s` are merged with `object$lambda`, and the model is refit before predictions are made. If `exact=FALSE` (default), then the predict function uses linear interpolation to make predictions for values of `s` that do not coincide with lambdas used in the fitting algorithm. A simple example is: ```{r} fit = glmnet(x, y) any(fit$lambda == 0.5) coef.apprx = coef(fit, s = 0.5, exact = FALSE) coef.exact = coef(fit, s = 0.5, exact = TRUE, x=x, y=y) cbind2(coef.exact, coef.apprx) ``` The left column is for `exact = TRUE` and the right for `FALSE`. We see from the above that 0.5 is not in the sequence and that hence there are some difference, though not much. Linear interpolation is mostly enough if there are no special requirements. Notice that with `exact=TRUE` we have to supply by named argument any data that was used in creating the original fit, in this case `x` and `y`. Users can make predictions from the fitted object. In addition to the options in `coef`, the primary argument is `newx`, a matrix of new values for `x`. The `type` option allows users to choose the type of prediction: * "link" gives the fitted values * "response" the sames as "link" for "gaussian" family. * "coefficients" computes the coefficients at values of `s` * "nonzero" retuns a list of the indices of the nonzero coefficients for each value of `s`. For example, ```{r} predict(fit, newx = x[1:5,], type = "response", s = 0.05) ``` gives the fitted values for the first 5 observations at $\lambda = 0.05$. If multiple values of `s` are supplied, a matrix of predictions is produced. Users can customize K-fold cross-validation. In addition to all the `glmnet` parameters, `cv.glmnet` has its special parameters including `nfolds` (the number of folds), `foldid` (user-supplied folds), `type.measure`(the loss used for cross-validation): * "deviance" or "mse" uses squared loss * "mae" uses mean absolute error As an example, ```{r} cvfit = cv.glmnet(x, y, type.measure = "mse", nfolds = 20) ``` does 20-fold cross-validation, based on mean squared error criterion (default though). Parallel computing is also supported by `cv.glmnet`. To make it work, users must register parallel beforehand. We give a simple example of comparison here. Unfortunately, the package `doMC` is not available on Windows platforms (it is on others), so we cannot run the code here, but we make it looks as if we have. ```{r, eval=FALSE} require(doMC) registerDoMC(cores=2) X = matrix(rnorm(1e4 * 200), 1e4, 200) Y = rnorm(1e4) ``` ```{r, eval=FALSE} system.time(cv.glmnet(X, Y)) ``` ```{r, echo=FALSE} structure(c(2.44, 0.08, 2.518, 0, 0), class = "proc_time", .Names = c("user.self", "sys.self", "elapsed", "user.child", "sys.child")) ``` ```{r, eval=FALSE} system.time(cv.glmnet(X, Y, parallel = TRUE)) ``` ```{r, echo=FALSE} structure(c(0.508999999999999, 0.057, 1.56699999999999, 1.941, 0.1), class = "proc_time", .Names = c("user.self", "sys.self", "elapsed", "user.child", "sys.child")) ``` As suggested from the above, parallel computing can significantly speed up the computation process especially for large-scale problems. Functions `coef` and `predict` on cv.glmnet object are similar to those for a `glmnet` object, except that two special strings are also supported by `s` (the values of $\lambda$ requested): * "lambda.1se": the largest $\lambda$ at which the MSE is within one standard error of the minimal MSE. * "lambda.min": the $\lambda$ at which the minimal MSE is achieved. ```{r} cvfit$lambda.min coef(cvfit, s = "lambda.min") predict(cvfit, newx = x[1:5,], s = "lambda.min") ``` Users can control the folds used. Here we use the same folds so we can also select a value for $\alpha$. ```{r} foldid=sample(1:10,size=length(y),replace=TRUE) cv1=cv.glmnet(x,y,foldid=foldid,alpha=1) cv.5=cv.glmnet(x,y,foldid=foldid,alpha=.5) cv0=cv.glmnet(x,y,foldid=foldid,alpha=0) ``` There are no built-in plot functions to put them all on the same plot, so we are on our own here: ```{r} par(mfrow=c(2,2)) plot(cv1);plot(cv.5);plot(cv0) plot(log(cv1$lambda),cv1$cvm,pch=19,col="red",xlab="log(Lambda)",ylab=cv1$name) points(log(cv.5$lambda),cv.5$cvm,pch=19,col="grey") points(log(cv0$lambda),cv0$cvm,pch=19,col="blue") legend("topleft",legend=c("alpha= 1","alpha= .5","alpha 0"),pch=19,col=c("red","grey","blue")) ``` We see that lasso (`alpha=1`) does about the best here. We also see that the range of lambdas used differs with alpha. #### Coefficient upper and lower bounds These are recently added features that enhance the scope of the models. Suppose we want to fit our model, but limit the coefficients to be bigger than -0.7 and less than 0.5. This is easily achieved via the `upper.limits` and `lower.limits` arguments: ```{r} tfit=glmnet(x,y,lower=-.7,upper=.5) plot(tfit) ``` These are rather arbitrary limits; often we want the coefficients to be positive, so we can set only `lower.limit` to be 0. (Note, the lower limit must be no bigger than zero, and the upper limit no smaller than zero.) These bounds can be a vector, with different values for each coefficient. If given as a scalar, the same number gets recycled for all. #### Penalty factors This argument allows users to apply separate penalty factors to each coefficient. Its default is 1 for each parameter, but other values can be specified. In particular, any variable with `penalty.factor` equal to zero is not penalized at all! Let $v_j$ denote the penalty factor for $j$ th variable. The penalty term becomes $$ \lambda \sum_{j=1}^p \boldsymbol{v_j} P_\alpha(\beta_j) = \lambda \sum_{j=1}^p \boldsymbol{v_j} \left[ (1-\alpha)\frac{1}{2} \beta_j^2 + \alpha |\beta_j| \right]. $$ Note the penalty factors are internally rescaled to sum to nvars. This is very useful when people have prior knowledge or preference over the variables. In many cases, some variables may be so important that one wants to keep them all the time, which can be achieved by setting corresponding penalty factors to 0: ```{r} p.fac = rep(1, 20) p.fac[c(5, 10, 15)] = 0 pfit = glmnet(x, y, penalty.factor = p.fac) plot(pfit, label = TRUE) ``` We see from the labels that the three variables with 0 penalty factors always stay in the model, while the others follow typical regularization paths and shrunken to 0 eventually. Some other useful arguments. `exclude` allows one to block certain variables from being the model at all. Of course, one could simply subset these out of `x`, but sometimes `exclude` is more useful, since it returns a full vector of coefficients, just with the excluded ones set to zero. There is also an `intercept` argument which defaults to `TRUE`; if `FALSE` the intercept is forced to be zero. #### Customizing plots Sometimes, especially when the number of variables is small, we want to add variable labels to a plot. Since `glmnet` is intended primarily for wide data, this is not supprted in `plot.glmnet`. However, it is easy to do, as the following little toy example shows. We first generate some data, with 10 variables, and for lack of imagination and ease we give them simple character names. We then fit a glmnet model, and make the standard plot. ```{r} set.seed(101) x=matrix(rnorm(1000),100,10) y=rnorm(100) vn=paste("var",1:10) fit=glmnet(x,y) plot(fit) ``` We wish to label the curves with the variable names. Here s a simple way to do this, using the `axis` command in R (and a little research into how to customize it). We need to have the positions of the coefficients at the end of the path, and we need to make some space using the `par` command, so that our labels will fit in. This requires knowing how long your labels are, but here they are all quite short. ```{r} par(mar=c(4.5,4.5,1,4)) plot(fit) vnat=coef(fit) vnat=vnat[-1,ncol(vnat)] # remove the intercept, and get the coefficients at the end of the path axis(4, at=vnat,line=-.5,label=vn,las=1,tick=FALSE, cex.axis=0.5) ``` We have done nothing here to avoid overwriting of labels, in the event that they are close together. This would be a bit more work, but perhaps best left alone, anyway. ### Multiresponse Gaussian Family The multiresponse Gaussian family is obtained using `family = "mgaussian"` option in `glmnet`. It is very similar to the single-response case above. This is useful when there are a number of (correlated) responses - the so-called "multi-task learning" problem. Here the sharing involves which variables are selected, since when a variable is selected, a coefficient is fit for each response. Most of the options are the same, so we focus here on the differences with the single response model. Obviously, as the name suggests, $y$ is not a vector, but a matrix of quantitative responses in this section. The coefficients at each value of lambda are also a matrix as a result. Here we solve the following problem: $$ \min_{(\beta_0, \beta) \in \mathbb{R}^{(p+1)\times K}}\frac{1}{2N} \sum_{i=1}^N ||y_i -\beta_0-\beta^T x_i||^2_F+\lambda \left[ (1-\alpha)||\beta||_F^2/2 + \alpha\sum_{j=1}^p||\beta_j||_2\right]. $$ Here $\beta_j$ is the jth row of the $p\times K$ coefficient matrix $\beta$, and we replace the absolute penalty on each single coefficient by a group-lasso penalty on each coefficient K-vector $\beta_j$ for a single predictor $x_j$. We use a set of data generated beforehand for illustration. ```{r} data(MultiGaussianExample) ``` We fit the data, with an object "mfit" returned. ```{r} mfit = glmnet(x, y, family = "mgaussian") ``` For multiresponse Gaussian, the options in `glmnet` are almost the same as the single-response case, such as `alpha`, `weights`, `nlambda`, `standardize`. A exception to be noticed is that `standardize.response` is only for `mgaussian` family. The default value is `FALSE`. If `standardize.response = TRUE`, it standardizes the response variables. To visualize the coefficients, we use the `plot` function. ```{r} plot(mfit, xvar = "lambda", label = TRUE, type.coef = "2norm") ``` Note that we set `type.coef = "2norm"`. Under this setting, a single curve is plotted per variable, with value equal to the $\ell_2$ norm. The default setting is `type.coef = "coef"`, where a coefficient plot is created for each response (multiple figures). `xvar` and `label` are two other options besides ordinary graphical parameters. They are the same as the single-response case. We can extract the coefficients at requested values of $\lambda$ by using the function `coef` and make predictions by `predict`. The usage is similar and we only provide an example of `predict` here. ```{r} predict(mfit, newx = x[1:5,], s = c(0.1, 0.01)) ``` The prediction result is saved in a three-dimensional array with the first two dimensions being the prediction matrix for each response variable and the third indicating the response variables. We can also do k-fold cross-validation. The options are almost the same as the ordinary Gaussian family and we do not expand here. ```{r} cvmfit = cv.glmnet(x, y, family = "mgaussian") ``` We plot the resulting `cv.glmnet` object "cvmfit". ```{r} plot(cvmfit) ``` To show explicitly the selected optimal values of $\lambda$, type ```{r} cvmfit$lambda.min cvmfit$lambda.1se ``` As before, the first one is the value at which the minimal mean squared error is achieved and the second is for the most regularized model whose mean squared error is within one standard error of the minimal. Prediction for `cv.glmnet` object works almost the same as for `glmnet` object. We omit the details here. ## Logistic Regression Logistic regression is another widely-used model when the response is categorical. If there are two possible outcomes, we use the binomial distribution, else we use the multinomial. ### Binomial Models For the binomial model, suppose the response variable takes value in $\mathcal{G}=\{1,2\}$. Denote $y_i = I(g_i=1)$. We model $$\mbox{Pr}(G=2|X=x)=\frac{e^{\beta_0+\beta^Tx}}{1+e^{\beta_0+\beta^Tx}},$$ which can be written in the following form $$\log\frac{\mbox{Pr}(G=2|X=x)}{\mbox{Pr}(G=1|X=x)}=\beta_0+\beta^Tx,$$ the so-called "logistic" or log-odds transformation. The objective function for the penalized logistic regression uses the negative binomial log-likelihood, and is $$ \min_{(\beta_0, \beta) \in \mathbb{R}^{p+1}} -\left[\frac{1}{N} \sum_{i=1}^N y_i \cdot (\beta_0 + x_i^T \beta) - \log (1+e^{(\beta_0+x_i^T \beta)})\right] + \lambda \big[ (1-\alpha)||\beta||_2^2/2 + \alpha||\beta||_1\big]. $$ Logistic regression is often plagued with degeneracies when $p > N$ and exhibits wild behavior even when $N$ is close to $p$; the elastic-net penalty alleviates these issues, and regularizes and selects variables as well. Our algorithm uses a quadratic approximation to the log-likelihood, and then coordinate descent on the resulting penalized weighted least-squares problem. These constitute an outer and inner loop. For illustration purpose, we load pre-generated input matrix `x` and the response vector `y` from the data file. ```{r} data(BinomialExample) ``` The input matrix $x$ is the same as other families. For binomial logistic regression, the response variable $y$ should be either a factor with two levels, or a two-column matrix of counts or proportions. Other optional arguments of `glmnet` for binomial regression are almost same as those for Gaussian family. Don't forget to set `family` option to "binomial". ```{r} fit = glmnet(x, y, family = "binomial") ``` Like before, we can print and plot the fitted object, extract the coefficients at specific $\lambda$'s and also make predictions. For plotting, the optional arguments such as `xvar` and `label` are similar to the Gaussian. We plot against the deviance explained and show the labels. ```{r} plot(fit, xvar = "dev", label = TRUE) ``` Prediction is a little different for logistic from Gaussian, mainly in the option `type`. "link" and "response" are never equivalent and "class" is only available for logistic regression. In summary, * "link" gives the linear predictors * "response" gives the fitted probabilities * "class" produces the class label corresponding to the maximum probability. * "coefficients" computes the coefficients at values of `s` * "nonzero" retuns a list of the indices of the nonzero coefficients for each value of `s`. For "binomial" models, results ("link", "response", "coefficients", "nonzero") are returned only for the class corresponding to the second level of the factor response. In the following example, we make prediction of the class labels at $\lambda = 0.05, 0.01$. ```{r} predict(fit, newx = x[1:5,], type = "class", s = c(0.05, 0.01)) ``` For logistic regression, `cv.glmnet` has similar arguments and usage as Gaussian. `nfolds`, `weights`, `lambda`, `parallel` are all available to users. There are some differences in `type.measure`: "deviance" and "mse" do not both mean squared loss and "class" is enabled. Hence, * "mse" uses squared loss. * "deviance" uses actual deviance. * "mae" uses mean absolute error. * "class" gives misclassification error. * "auc" (for two-class logistic regression ONLY) gives area under the ROC curve. For example, ```{r} cvfit = cv.glmnet(x, y, family = "binomial", type.measure = "class") ``` It uses misclassification error as the criterion for 10-fold cross-validation. We plot the object and show the optimal values of $\lambda$. ```{r} plot(cvfit) ``` ```{r} cvfit$lambda.min cvfit$lambda.1se ``` `coef` and `predict` are simliar to the Gaussian case and we omit the details. We review by some examples. ```{r} coef(cvfit, s = "lambda.min") ``` As mentioned previously, the results returned here are only for the second level of the factor response. ```{r} predict(cvfit, newx = x[1:10,], s = "lambda.min", type = "class") ``` Like other GLMs, glmnet allows for an "offset". This is a fixed vector of N numbers that is added into the linear predictor. For example, you may have fitted some other logistic regression using other variables (and data), and now you want to see if the present variables can add anything. So you use the predicted logit from the other model as an offset in. ### Multinomial Models For the multinomial model, suppose the response variable has $K$ levels ${\cal G}=\{1,2,\ldots,K\}$. Here we model $$\mbox{Pr}(G=k|X=x)=\frac{e^{\beta_{0k}+\beta_k^Tx}}{\sum_{\ell=1}^Ke^{\beta_{0\ell}+\beta_\ell^Tx}}.$$ Let ${Y}$ be the $N \times K$ indicator response matrix, with elements $y_{i\ell} = I(g_i=\ell)$. Then the elastic-net penalized negative log-likelihood function becomes $$ \ell(\{\beta_{0k},\beta_{k}\}_1^K) = -\left[\frac{1}{N} \sum_{i=1}^N \Big(\sum_{k=1}^Ky_{il} (\beta_{0k} + x_i^T \beta_k)- \log \big(\sum_{k=1}^K e^{\beta_{0k}+x_i^T \beta_k}\big)\Big)\right] +\lambda \left[ (1-\alpha)||\beta||_F^2/2 + \alpha\sum_{j=1}^p||\beta_j||_q\right]. $$ Here we really abuse notation! $\beta$ is a $p\times K$ matrix of coefficients. $\beta_k$ refers to the kth column (for outcome category k), and $\beta_j$ the jth row (vector of K coefficients for variable j). The last penalty term is $||\beta_j||_q$, we have two options for q: $q\in \{1,2\}$. When q=1, this is a lasso penalty on each of the parameters. When q=2, this is a grouped-lasso penalty on all the K coefficients for a particular variables, which makes them all be zero or nonzero together. The standard Newton algorithm can be tedious here. Instead, we use a so-called partial Newton algorithm by making a partial quadratic approximation to the log-likelihood, allowing only $(\beta_{0k}, \beta_k)$ to vary for a single class at a time. For each value of $\lambda$, we first cycle over all classes indexed by $k$, computing each time a partial quadratic approximation about the parameters of the current class. Then the inner procedure is almost the same as for the binomial case. This is the case for lasso (q=1). When q=2, we use a different approach, which we wont dwell on here. For the multinomial case, the usage is similar to logistic regression, and we mainly illustrate by examples and address any differences. We load a set of generated data. ```{r} data(MultinomialExample) ``` The optional arguments in `glmnet` for multinomial logistic regression are mostly similar to binomial regression except for a few cases. The response variable can be a `nc >= 2` level factor, or a `nc`-column matrix of counts or proportions. Internally glmnet will make the rows of this matrix sum to 1, and absorb the total mass into the weight for that observation. `offset` should be a `nobs x nc` matrix if there is one. A special option for multinomial regression is `type.multinomial`, which allows the usage of a grouped lasso penalty if `type.multinomial = "grouped"`. This will ensure that the multinomial coefficients for a variable are all in or out together, just like for the multi-response Gaussian. ```{r} fit = glmnet(x, y, family = "multinomial", type.multinomial = "grouped") ``` We plot the resulting object "fit". ```{r} plot(fit, xvar = "lambda", label = TRUE, type.coef = "2norm") ``` The options are `xvar`, `label` and `type.coef`, in addition to other ordinary graphical parameters. `xvar` and `label` are the same as other families while `type.coef` is only for multinomial regression and multiresponse Gaussian model. It can produce a figure of coefficients for each response variable if `type.coef = "coef"` or a figure showing the $\ell_2$-norm in one figure if `type.coef = "2norm"` We can also do cross-validation and plot the returned object. ```{r} cvfit=cv.glmnet(x, y, family="multinomial", type.multinomial = "grouped", parallel = TRUE) plot(cvfit) ``` Note that although `type.multinomial` is not a typical argument in `cv.glmnet`, in fact any argument that can be passed to `glmnet` is valid in the argument list of `cv.glmnet`. We also use parallel computing to accelerate the calculation. Users may wish to predict at the optimally selected $\lambda$: ```{r} predict(cvfit, newx = x[1:10,], s = "lambda.min", type = "class") ``` ## Poisson Models Poisson regression is used to model count data under the assumption of Poisson error, or otherwise non-negative data where the mean and variance are proportional. Like the Gaussian and binomial model, the Poisson is a member of the exponential family of distributions. We usually model its positive mean on the log scale: $\log \mu(x) = \beta_0+\beta' x$. The log-likelihood for observations $\{x_i,y_i\}_1^N$ is given my $$ l(\beta|X, Y) = \sum_{i=1}^N \left(y_i (\beta_0+\beta' x_i) - e^{\beta_0+\beta^Tx_i}\right). $$ As before, we optimize the penalized log-lielihood: $$ \min_{\beta_0,\beta} -\frac1N l(\beta|X, Y) + \lambda \left((1-\alpha) \sum_{i=1}^N \beta_i^2/2) +\alpha \sum_{i=1}^N |\beta_i|\right). $$ Glmnet uses an outer Newton loop, and an inner weighted least-squares loop (as in logistic regression) to optimize this criterion. First, we load a pre-generated set of Poisson data. ```{r} data(PoissonExample) ``` We apply the function `glmnet` with the `"poisson"` option. ```{r} fit = glmnet(x, y, family = "poisson") ``` The optional input arguments of `glmnet` for `"poisson"` family are similar to those for others. `offset` is a useful argument particularly in Poisson models. When dealing with rate data in Poisson models, the counts collected are often based on different exposures, such as length of time observed, area and years. A poisson rate $\mu(x)$ is relative to a unit exposure time, so if an observation $y_i$ was exposed for $E_i$ units of time, then the expected count would be $E_i\mu(x)$, and the log mean would be $\log(E_i)+\log(\mu(x))$. In a case like this, we would supply an *offset* $\log(E_i)$ for each observation. Hence `offset` is a vector of length `nobs` that is included in the linear predictor. Other families can also use options, typically for different reasons. (Warning: if `offset` is supplied in `glmnet`, offsets must also also be supplied to `predict` to make reasonable predictions.) Again, we plot the coefficients to have a first sense of the result. ```{r} plot(fit) ``` Like before, we can extract the coefficients and make predictions at certain $\lambda$'s by using `coef` and `predict` respectively. The optional input arguments are similar to those for other families. In function `predict`, the option `type`, which is the type of prediction required, has its own specialties for Poisson family. That is, * "link" (default) gives the linear predictors like others * "response" gives the fitted mean * "coefficients" computes the coefficients at the requested values for `s`, which can also be realized by `coef` function * "nonzero" returns a a list of the indices of the nonzero coefficients for each value of `s`. For example, we can do as follows. ```{r} coef(fit, s = 1) predict(fit, newx = x[1:5,], type = "response", s = c(0.1,1)) ``` We may also use cross-validation to find the optimal $\lambda$'s and thus make inferences. ```{r} cvfit = cv.glmnet(x, y, family = "poisson") ``` Options are almost the same as the Gaussian family except that for `type.measure`, * "deviance" (default) gives the deviance * "mse" stands for mean squared error * "mae" is for mean absolute error. We can plot the `cv.glmnet` object. ```{r} plot(cvfit) ``` We can also show the optimal $\lambda$'s and the corresponding coefficients. ```{r} opt.lam = c(cvfit$lambda.min, cvfit$lambda.1se) coef(cvfit, s = opt.lam) ``` The `predict` method is similar and we do not repeat it here. ## Cox Models The Cox proportional hazards model is commonly used for the study of the relationship beteween predictor variables and survival time. In the usual survival analysis framework, we have data of the form $(y_1, x_1, \delta_1), \ldots, (y_n, x_n, \delta_n)$ where $y_i$, the observed time, is a time of failure if $\delta_i$ is 1 or right-censoring if $\delta_i$ is 0. We also let $t_1 < t_2 < \ldots < t_m$ be the increasing list of unique failure times, and $j(i)$ denote the index of the observation failing at time $t_i$. The Cox model assumes a semi-parametric form for the hazard $$ h_i(t) = h_0(t) e^{x_i^T \beta}, $$ where $h_i(t)$ is the hazard for patient $i$ at time $t$, $h_0(t)$ is a shared baseline hazard, and $\beta$ is a fixed, length $p$ vector. In the classic setting $n \geq p$, inference is made via the partial likelihood $$ L(\beta) = \prod_{i=1}^m \frac{e^{x_{j(i)}^T \beta}}{\sum_{j \in R_i} e^{x_j^T \beta}}, $$ where $R_i$ is the set of indices $j$ with $y_j \geq t_i$ (those at risk at time $t_i$). Note there is no intercept in the Cox mode (its built into the baseline hazard, and like it, would cancel in the partial likelihood.) We penalize the negative log of the partial likelihood, just like the other models, with an elastic-net penalty. We use a pre-generated set of sample data and response. Users can load their own data and follow a similar procedure. In this case $x$ must be an $n\times p$ matrix of covariate values - each row corresponds to a patient and each column a covariate. $y$ is an $n \times 2$ matrix, with a column "time" of failure/censoring times, and "status" a 0/1 indicator, with 1 meaning the time is a failure time, and zero a censoring time. ```{r} data(CoxExample) y[1:5,] ``` The `Surv` function in the package `survival` can create such a matrix. Note, however, that the `coxph` and related linear models can handle interval and other forms of censoring, while glmnet can only handle right censoring in its present form. We apply the `glmnet` function to compute the solution path under default settings. ```{r} fit = glmnet(x, y, family = "cox") ``` All the standard options are available such as `alpha`, `weights`, `nlambda` and `standardize`. Their usage is similar as in the Gaussian case and we omit the details here. Users can also refer to the help file `help(glmnet)`. We can plot the coefficients. ```{r} plot(fit) ``` As before, we can extract the coefficients at certain values of $\lambda$. ```{r} coef(fit, s = 0.05) ``` Since the Cox Model is not commonly used for prediction, we do not give an illustrative example on prediction. If needed, users can refer to the help file by typing `help(predict.glmnet)`. Also, the function `cv.glmnet` can be used to compute $k$-fold cross-validation for the Cox model. The usage is similar to that for other families except for two main differences. One is that `type.measure` only supports "deviance"(also default), which gives the partial-likelihood. The other is in the option `grouped`. `grouped = TRUE` obtains the CV partial likelihood for the Kth fold by subtraction; by subtracting the log partial likelihood evaluated on the full dataset from that evaluated on the on the (K-1)/K dataset. This makes more efficient use of risk sets. With `grouped=FALSE` the log partial likelihood is computed only on the Kth fold, which is only reasonable if each fold has a large number of observations. ```{r} cvfit = cv.glmnet(x, y, family = "cox") ``` Once fit, we can view the optimal $\lambda$ value and a cross validated error plot to help evaluate our model. ```{r} plot(cvfit) ``` As previously, the left vertical line in our plot shows us where the CV-error curve hits its minimum. The right vertical line shows us the most regularized model with CV-error within 1 standard deviation of the minimum. We also extract such optimal $\lambda$'s. ```{r} cvfit$lambda.min cvfit$lambda.1se ``` We can check the active covariates in our model and see their coefficients. ```{r} coef.min = coef(cvfit, s = "lambda.min") active.min = which(coef.min != 0) index.min = coef.min[active.min] ``` ```{r} index.min coef.min ``` ## Sparse Matrices Our package supports sparse input matrices, which allow efficient storage and operations of large matrices but with only a few nonzero entries. It is available for all families except for the `cox` family. The usage of sparse matrices (inherit from class `"sparseMatrix"` as in package `Matrix`) in `glmnet ` is the same as if a regular matrix is provided. We load a set of sample data created beforehand. ```{r} data(SparseExample) ``` It loads `x`, a 100*20 sparse input matrix and `y`, the response vector. ```{r} class(x) ``` Users can create a sparse matrix with the function `sparseMatrix` by providing the locations and values of the nonzero entries. Alternatively, `Matrix` function can also be used to contruct a sparse matrix by setting `sparse = TRUE`, but this defeats the purpose somewhat. We can fit the model the same way as before. ```{r} fit = glmnet(x, y) ``` We also do the cross-validation and plot the resulting object. ```{r} cvfit = cv.glmnet(x, y) plot(cvfit) ``` The usage of other functions are similar and we do not expand here. Note that sparse matrices can also be used for `newx`, the new input matrix in the `predict` function. For example, ```{r} i = sample(1:5, size = 25, replace = TRUE) j = sample(1:20, size = 25, replace = TRUE) x = rnorm(25) nx = sparseMatrix(i = i, j = j, x = x, dims = c(5, 20)) predict(cvfit, newx = nx, s = "lambda.min") ``` ## Appendix 0: Convergence Criteria Glmnet uses a convergence criterion that focuses not on coefficient change but rather the impact of the change on the fitted values, and hence the loss part of the objective. The net result is a weighted norm of the coefficient change vector. For gaussian models it uses the following. Suppose observation $i$ has weight $w_i$. Let $v_j$ be the (weighted) sum-of-squares for variable $x_j$: $$v_j=\sum_{i=1}^Nw_ix_{ij}^2.$$ If there is an intercept in the model, these $x_j$ will be centered by the weighted mean, and hence this would be a weighted variance. After $\hat\beta_j^o$ has been updated to $\hat\beta_j^n$, we compute $\Delta_j=v_j(\hat\beta_j^o-\hat\beta_j^n)^2$. After a complete cycle of coordinate descent, we look at $\Delta_{max}=\max_j\Delta_j$. Why this measure? We can write $$\Delta_j=\frac1N\sum_{i=1}^N w_j(x_{ij}\hat\beta_j^o-x_{ij}\hat\beta_j^n)^2,$$ which measures the weighted sum of squares of changes in fitted values for this term. This measures the impact of the change in this coefficient on the fit. If the largest such change is negligible, we stop. For logistic regression, and other non-Gaussian models it is similar for the inner loop. Only now the weights for each observation are more complex. For example, for logisitic regression the weights are those that arise from the current Newton step, namely $w_i^*=w_i\hat p_i(1-\hat p_i)$. Here $\hat p_i$ are the fitted probabilities as we entered the current inner loop. The intuition is the same --- it measures the impact of the coefficient change on the current weighted least squares loss, or quadratic approximation to the log-likelihood loss. What about outer-loop convergence? We use the same measure, except now $\hat\beta^o$ is the coefficient vector before we entered this inner loop, and $\hat\beta^n$ the converged solution for this inner loop. Hence if this Newton step had no impact, we declare outer-loop convergence. ## Appendix 1: Internal Parameters Our package has a set of internal parameters which control some aspects of the computation of the path. The *factory default* settings are expected to serve in most cases, and users do not need to make changes unless there are special requirements. There are several parameters that users can change: `fdev` - minimum fractional change in deviance for stopping path; factory default = 1.0e-5 `devmax` - maximum fraction of explained deviance for stopping path; factory default = 0.999 * `eps` - minimum value of lambda.min.ratio (see glmnet); factory default= 1.0e-6 * `big` - large floating point number; factory default = 9.9e35. Inf in definition of upper.limit is set to big * `mnlam` - minimum number of path points (lambda values) allowed; factory default = 5 * `pmin` - minimum null probability for any class; factory default = 1.0e-5 * `exmx` - maximum allowed exponent; factory default = 250.0 * `prec` - convergence threshold for multi-response bounds adjustment solution; factory default = 1.0e-10 * `mxit` - maximum iterations for multiresponse bounds adjustment solution; factory default = 100 * `factory` - If `TRUE`, reset all the parameters to the factory default; default is `FALSE` We illustrate the usage by an example. Note that any changes made hold for the duration of the R session, or unless they are changed by the user with a subsequent call to `glmnet.control`. ```{r} data(QuickStartExample) fit = glmnet(x, y) print(fit) ``` We can change the minimum fractional change in deviance for stopping path and compare the results. ```{r} glmnet.control(fdev = 0) fit = glmnet(x, y) print(fit) ``` We set `fdev = 0` to continue all along the path, even without much change. The length of the sequence becomes 100, which is the default of `nlambda`. Users can also reset to the default settings. ```{r} glmnet.control(factory = TRUE) ``` The current settings are obtained as follows. ```{r} glmnet.control() ``` ## Appendix 2: Comparison with Other Packages Some people may want to use `glmnet` to solve the Lasso or elastic-net problem at a single $\lambda$. We compare here the solution by `glmnet` with other packages (such as CVX), and also as an illustration of parameter settings in this situation. __Warning__: Though such problems can be solved by `glmnet`, it is __not recommended__ and is not the spirit of the package. `glmnet` fits the __entire__ solution path for Lasso or elastic-net problems efficiently with various techniques such as warm start. Those advantages will disappear if the $\lambda$ sequence is forced to be only one value. Nevertheless, we still illustrate with a typical example in linear model in the following for the purpose of comparison. Given $X, Y$ and $\lambda_0 > 0$, we want to find $\beta$ such that $$ \min_{\beta} ||Y - X\beta||_2^2 + \lambda_0 ||\beta||_1, $$ where, say, $\lambda_0 = 8$. We first solve using `glmnet`. Notice that there is no intercept term in the objective function, and the columns of $X$ are not necessarily standardized. Corresponding parameters have to be set to make it work correctly. In addition, there is a $1/(2n)$ factor before the quadratic term by default, we need to adjust $\lambda$ accordingly. For the purpose of comparison, the `thresh` option is specified to be 1e-20. However, this is not necessary in many practical applications. ```{r, echo=FALSE} data(QuickStartExample) ``` ```{r,eval=FALSE} fit = glmnet(x, y, intercept = F, standardize = F, lambda = 8/(2*dim(x)[1]), thresh = 1e-20) ``` We then extract the coefficients (with no intercept). ```{r,eval=FALSE} beta_glmnet = as.matrix(predict(fit, type = "coefficients")[-1,]) ``` In linear model as here this approach worked because we were using squared error loss, but with any nonlinear family, it will probably fail. The reason is we are not using step length optimization, and so rely on very good warm starts to put us in the quadratic region of the loss function. Alternatively, a more stable and __strongly recommended__ way to perform this task is to first fit the entire Lasso or elastic-net path without specifying `lambda`, but then provide the requested $\lambda_0$ to `predict` function to extract the corresponding coefficients. In fact, if $\lambda_0$ is not in the $\lambda$ sequence generated by `glmnet`, the path will be refitted along a new $\lambda$ sequence that includes the requested value $\lambda_0$ and the old sequence, and the coefficients will be returned at $\lambda_0$ based on the new fit. Remember to set `exact = TRUE` in `predict` function to get the exact solution. Otherwise, it will be approximated by linear interpolation. ```{r} fit = glmnet(x, y, intercept = F, standardize = F, thresh = 1e-20) beta_glmnet = as.matrix(predict(fit, s = 8/(2*dim(x)[1]), type = "coefficients", exact = TRUE, x=x, y=y)[-1,]) ``` We also use CVX, a general convex optimization solver, to solve this specific Lasso problem. Users could also call CVX from R using the `CVXfromR` package and solve the problem as follows. ```{r, eval=FALSE} library(CVXfromR) setup.dir = "change/this/to/your/cvx/directory" n = dim(x)[1]; p = dim(x)[2] cvxcode = paste("variables beta(p)", "minimize(square_pos(norm(y - x * beta, 2)) + lambda * norm(beta, 1))", sep = ";") Lasso = CallCVX(cvxcode, const.var = list(p = p, x = x, y = y, lambda = 8), opt.var.names = "beta", setup.dir = setup.dir, matlab.call = "change/this/to/path/to/matlab") beta_CVX = Lasso$beta ``` For convenience here, the results were saved in `CVXResult.RData`, and we simply load in the results. ```{r} data(CVXResults) ``` In addition, we use `lars` to solve the same problem. ```{r,message=FALSE} require(lars) ``` ```{r} fit_lars = lars(x, y, type = "lasso", intercept = F, normalize = F) beta_lars = predict(fit_lars, s = 8/2, type = "coefficients", mode = "lambda")$coefficients ``` The results are listed below up to 6 decimal digits (due to convergence thresholds). ```{r} cmp = round(cbind(beta_glmnet, beta_lars, beta_CVX), digits = 6) colnames(cmp) = c("beta_glmnet", "beta_lars", "beta_CVX") cmp ``` ## References glmnet/R/0000755000176200001440000000000013574046201011743 5ustar liggesusersglmnet/R/jerr.elnet.R0000644000176200001440000000133413534022702014133 0ustar liggesusersjerr.elnet=function(n,maxit,pmax){ if(n>0){#fatal error if(n<7777)msg="Memory allocation error; contact package maintainer" else if(n==7777)msg="All used predictors have zero variance" else if(n==10000)msg="All penalty factors are <= 0" else msg="Unknown error" list(n=n,fatal=TRUE,msg=msg) } else if(n<0){# non-fatal error if(n>-10000)msg=paste("Convergence for ",-n,"th lambda value not reached after maxit=",maxit," iterations; solutions for larger lambdas returned",sep="") if(n < -10000)msg=paste("Number of nonzero coefficients along the path exceeds pmax=",pmax, " at ",-n-10000,"th lambda value; solutions for larger lambdas returned",sep="") list(n=n,fatal=FALSE,msg=msg) } } glmnet/R/plot.cv.glmnet.R0000644000176200001440000000504713556622666014765 0ustar liggesusers#' plot the cross-validation curve produced by cv.glmnet #' #' Plots the cross-validation curve, and upper and lower standard deviation #' curves, as a function of the \code{lambda} values used. If the object has #' class \code{"cv.relaxed"} a different plot is produced, showing both #' \code{lambda} and \code{gamma} #' #' A plot is produced, and nothing is returned. #' #' @aliases plot.cv.glmnet #' @param x fitted \code{"cv.glmnet"} object #' @param sign.lambda Either plot against \code{log(lambda)} (default) or its #' negative if \code{sign.lambda=-1}. #' @param \dots Other graphical parameters to plot #' @author Jerome Friedman, Trevor Hastie and Rob Tibshirani\cr Maintainer: #' Trevor Hastie #' @seealso \code{glmnet} and \code{cv.glmnet}. #' @references Friedman, J., Hastie, T. and Tibshirani, R. (2008) #' \emph{Regularization Paths for Generalized Linear Models via Coordinate #' Descent} #' @keywords models regression #' @examples #' #' set.seed(1010) #' n = 1000 #' p = 100 #' nzc = trunc(p/10) #' x = matrix(rnorm(n * p), n, p) #' beta = rnorm(nzc) #' fx = (x[, seq(nzc)] %*% beta) #' eps = rnorm(n) * 5 #' y = drop(fx + eps) #' px = exp(fx) #' px = px/(1 + px) #' ly = rbinom(n = length(px), prob = px, size = 1) #' cvob1 = cv.glmnet(x, y) #' plot(cvob1) #' title("Gaussian Family", line = 2.5) #' cvob1r = cv.glmnet(x, y, relax = TRUE) #' plot(cvob1r) #' frame() #' set.seed(1011) #' par(mfrow = c(2, 2), mar = c(4.5, 4.5, 4, 1)) #' cvob2 = cv.glmnet(x, ly, family = "binomial") #' plot(cvob2) #' title("Binomial Family", line = 2.5) #' ## set.seed(1011) #' ## cvob3 = cv.glmnet(x, ly, family = "binomial", type = "class") #' ## plot(cvob3) #' ## title("Binomial Family", line = 2.5) #' #' @method plot cv.glmnet #' @export plot.cv.glmnet=function(x,sign.lambda=1,...){ cvobj=x xlab = expression(Log(lambda)) # xlab="log(Lambda)" if(sign.lambda<0)xlab=paste("-",xlab,sep="") plot.args=list(x=sign.lambda*log(cvobj$lambda),y=cvobj$cvm,ylim=range(cvobj$cvup,cvobj$cvlo),xlab=xlab,ylab=cvobj$name,type="n") new.args=list(...) if(length(new.args))plot.args[names(new.args)]=new.args do.call("plot",plot.args) error.bars(sign.lambda*log(cvobj$lambda),cvobj$cvup,cvobj$cvlo,width=0.01, col="darkgrey") # col="antiquewhite2") points(sign.lambda*log(cvobj$lambda),cvobj$cvm,pch=20, col="red") axis(side=3,at=sign.lambda*log(cvobj$lambda),labels=paste(cvobj$nz),tick=FALSE,line=0) abline(v=sign.lambda*log(cvobj$lambda.min),lty=3) abline(v=sign.lambda*log(cvobj$lambda.1se),lty=3) invisible() } glmnet/R/buildPredmat.array.R0000644000176200001440000000363313552673674015644 0ustar liggesusers#' @method buildPredmat array #' @export buildPredmat.array=function(outlist, lambda, x, offset, foldid, alignment,...){ if (!is.null(offset)) is.offset = TRUE else is.offset = FALSE nc=dim(outlist[[1]]$a0)[1] nobs=nrow(x) predmat = array(NA, c(nobs, nc, length(lambda))) nfolds = max(foldid) nlams = double(nfolds) nlambda = length(lambda) for (i in seq(nfolds)) { which = foldid == i fitobj = outlist[[i]] if (is.offset) off_sub = offset[which, , drop = FALSE] preds = switch(alignment, fraction = predict(fitobj, x[which, , drop = FALSE], newoffset = off_sub,...), lambda = predict(fitobj, x[which, , drop = FALSE], s = lambda, newoffset = off_sub,...)) nlami = min(dim(preds)[3], nlambda) predmat[which, , seq(nlami)] = preds[, , seq(nlami)] if (nlami < nlambda) predmat[which, , seq(from = nlami, to = nlambda)] = preds[,, nlami] } ### fix up dimnames rn=rownames(x) sn=paste("s",seq(0,length=nlambda),sep="") cn=dimnames(preds)[[2]] dimnames(predmat)=list(rn,cn,sn) predmat } #' @method buildPredmat multnetlist #' @export buildPredmat.multnetlist=function(outlist, lambda, x, offset, foldid, alignment,...){ mat=buildPredmat.array(outlist, lambda, x, offset, foldid, alignment,...) attr(mat,"classnames")=outlist[[1]]$classnames mat } #' @method buildPredmat mrelnetlist #' @export buildPredmat.mrelnetlist=function(outlist, lambda, x, offset, foldid, alignment,...){ buildPredmat.array(outlist, lambda, x, offset, foldid, alignment,...) } #' @method buildPredmat lognetlist #' @export buildPredmat.lognetlist=function(outlist, lambda, x, offset, foldid, alignment,...){ mat=buildPredmat.default(outlist, lambda, x, offset, foldid, alignment,...) attr(mat,"classnames")=outlist[[1]]$classnames mat } glmnet/R/getcoef.R0000644000176200001440000000252513534022702013502 0ustar liggesusersgetcoef=function(fit,nvars,nx,vnames){ lmu=fit$lmu if(lmu<1){ ## changed this to a warning message, and return an empty model warning("an empty model has been returned; probably a convergence issue") coefob=list(a0=fit$a0,beta=zeromat(nvars,as.integer(1),vnames,"s0"),df=0,dim=c(nvars,1),lambda=Inf) return(coefob) } nin=fit$nin[seq(lmu)] ninmax=max(nin) lam=fit$alm[seq(lmu)] stepnames=paste("s",seq(lmu)-1,sep="") dd=c(nvars,lmu) if(ninmax>0){ ca=matrix(fit$ca[seq(nx*lmu)],nx,lmu)[seq(ninmax),,drop=FALSE] df=apply(abs(ca)>0,2,sum) ja=fit$ia[seq(ninmax)] ####confusing but too hard to change ###glmnet builds a list of ever active variables which is nondecreasing ###Since ca was initialized to zero, no harm is done in passing a square matrix ###to new(); then when we do a drop0 it makes it really sparse oja=order(ja) ja=rep(ja[oja],lmu) ia=cumsum(c(1,rep(ninmax,lmu))) beta=drop0(new("dgCMatrix",Dim=dd,Dimnames=list(vnames,stepnames),x=as.vector(ca[oja,]),p=as.integer(ia-1),i=as.integer(ja-1))) }else { beta = zeromat(nvars,lmu,vnames,stepnames) df=rep(0,lmu) } a0=fit$a0 if(!is.null(a0)){#for Cox model a0=a0[seq(lmu)] names(a0)=stepnames } list(a0=a0,beta=beta,df=df,dim=dd,lambda=lam) } glmnet/R/predict.glmnet.R0000644000176200001440000001507213555633674015031 0ustar liggesusers#' make predictions from a "glmnet" object. #' #' Similar to other predict methods, this functions predicts fitted values, #' logits, coefficients and more from a fitted \code{"glmnet"} object. #' #' The shape of the objects returned are different for \code{"multinomial"} #' objects. This function actually calls \code{NextMethod()}, and the #' appropriate predict method is invoked for each of the three model types. #' \code{coef(...)} is equivalent to \code{predict(type="coefficients",...)} #' #' @aliases coef.glmnet coef.relaxed predict.glmnet predict.relaxed #' predict.elnet predict.lognet predict.multnet predict.mrelnet predict.fishnet #' predict.coxnet #' @param object Fitted \code{"glmnet"} model object or a \code{"relaxed"} #' model (which inherits from class "glmnet"). #' @param newx Matrix of new values for \code{x} at which predictions are to be #' made. Must be a matrix; can be sparse as in \code{Matrix} package. This #' argument is not used for \code{type=c("coefficients","nonzero")} #' @param s Value(s) of the penalty parameter \code{lambda} at which #' predictions are required. Default is the entire sequence used to create the #' model. #' @param type Type of prediction required. Type \code{"link"} gives the linear #' predictors for \code{"binomial"}, \code{"multinomial"}, \code{"poisson"} or #' \code{"cox"} models; for \code{"gaussian"} models it gives the fitted #' values. Type \code{"response"} gives the fitted probabilities for #' \code{"binomial"} or \code{"multinomial"}, fitted mean for \code{"poisson"} #' and the fitted relative-risk for \code{"cox"}; for \code{"gaussian"} type #' \code{"response"} is equivalent to type \code{"link"}. Type #' \code{"coefficients"} computes the coefficients at the requested values for #' \code{s}. Note that for \code{"binomial"} models, results are returned only #' for the class corresponding to the second level of the factor response. #' Type \code{"class"} applies only to \code{"binomial"} or #' \code{"multinomial"} models, and produces the class label corresponding to #' the maximum probability. Type \code{"nonzero"} returns a list of the indices #' of the nonzero coefficients for each value of \code{s}. #' @param exact This argument is relevant only when predictions are made at #' values of \code{s} (lambda) \emph{different} from those used in the fitting #' of the original model. Not available for \code{"relaxed"} objects. If #' \code{exact=FALSE} (default), then the predict function uses linear #' interpolation to make predictions for values of \code{s} (lambda) that do #' not coincide with those used in the fitting algorithm. While this is often a #' good approximation, it can sometimes be a bit coarse. With #' \code{exact=TRUE}, these different values of \code{s} are merged (and #' sorted) with \code{object$lambda}, and the model is refit before predictions #' are made. In this case, it is required to supply the original data \code{x=} #' and \code{y=} as additional named arguments to \code{predict()} or #' \code{coef()}. The workhorse \code{predict.glmnet()} needs to \code{update} #' the model, and so needs the data used to create it. The same is true of #' \code{weights}, \code{offset}, \code{penalty.factor}, \code{lower.limits}, #' \code{upper.limits} if these were used in the original call. Failure to do #' so will result in an error. #' @param newoffset If an offset is used in the fit, then one must be supplied #' for making predictions (except for \code{type="coefficients"} or #' \code{type="nonzero"}) #' @param \dots This is the mechanism for passing arguments like \code{x=} when #' \code{exact=TRUE}; see\code{exact} argument. #' @return The object returned depends on type. #' @author Jerome Friedman, Trevor Hastie and Rob Tibshirani\cr Maintainer: #' Trevor Hastie #' @seealso \code{glmnet}, and \code{print}, and \code{coef} methods, and #' \code{cv.glmnet}. #' @references Friedman, J., Hastie, T. and Tibshirani, R. (2008) #' \emph{Regularization Paths for Generalized Linear Models via Coordinate #' Descent}, \url{https://web.stanford.edu/~hastie/Papers/glmnet.pdf}\cr #' \emph{Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010}\cr #' \url{https://www.jstatsoft.org/v33/i01/}\cr Simon, N., Friedman, J., Hastie, #' T., Tibshirani, R. (2011) \emph{Regularization Paths for Cox's Proportional #' Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. #' 39(5) 1-13}\cr \url{https://www.jstatsoft.org/v39/i05/} #' @keywords models regression #' #' @examples #' x=matrix(rnorm(100*20),100,20) #' y=rnorm(100) #' g2=sample(1:2,100,replace=TRUE) #' g4=sample(1:4,100,replace=TRUE) #' fit1=glmnet(x,y) #' predict(fit1,newx=x[1:5,],s=c(0.01,0.005)) #' predict(fit1,type="coef") #' fit2=glmnet(x,g2,family="binomial") #' predict(fit2,type="response",newx=x[2:5,]) #' predict(fit2,type="nonzero") #' fit3=glmnet(x,g4,family="multinomial") #' predict(fit3,newx=x[1:3,],type="response",s=0.01) #' @method predict glmnet #' @export #' @export predict.glmnet predict.glmnet=function(object,newx,s=NULL,type=c("link","response","coefficients","nonzero","class"),exact=FALSE,newoffset,...){ type=match.arg(type) if(missing(newx)){ if(!match(type,c("coefficients","nonzero"),FALSE))stop("You need to supply a value for 'newx'") } if(exact&&(!is.null(s))){ ###we augment the lambda sequence with the new values, if they are different,and refit the model using update lambda=object$lambda which=match(s,lambda,FALSE) if(!all(which>0)){ lambda=unique(rev(sort(c(s,lambda)))) check_dots(object,...)# This fails if you don't supply the crucial arguments object=update(object,lambda=lambda,...) } } a0=t(as.matrix(object$a0)) rownames(a0)="(Intercept)" nbeta=methods::rbind2(a0,object$beta)#was rbind2 if(!is.null(s)){ vnames=dimnames(nbeta)[[1]] dimnames(nbeta)=list(NULL,NULL) lambda=object$lambda lamlist=lambda.interp(lambda,s) nbeta=nbeta[,lamlist$left,drop=FALSE]%*%Diagonal(x=lamlist$frac) +nbeta[,lamlist$right,drop=FALSE]%*%Diagonal(x=1-lamlist$frac) dimnames(nbeta)=list(vnames,paste(seq(along=s))) } if(type=="coefficients")return(nbeta) if(type=="nonzero")return(nonzeroCoef(nbeta[-1,,drop=FALSE],bystep=TRUE)) ###Check on newx if(inherits(newx, "sparseMatrix"))newx=as(newx,"dgCMatrix") nfit=as.matrix(cbind2(1,newx)%*%nbeta) if(object$offset){ if(missing(newoffset))stop("No newoffset provided for prediction, yet offset used in fit of glmnet",call.=FALSE) if(is.matrix(newoffset)&&inherits(object,"lognet")&&dim(newoffset)[[2]]==2)newoffset=newoffset[,2] nfit=nfit+array(newoffset,dim=dim(nfit)) } nfit } glmnet/R/coef.glmnet.R0000644000176200001440000000036113557672013014276 0ustar liggesusers#' Extract coefficients from a glmnet object #' #' @method coef glmnet #' @rdname predict.glmnet #' @export #' @export coef.glmnet coef.glmnet=function(object,s=NULL,exact=FALSE,...) predict(object,s=s,type="coefficients",exact=exact,...) glmnet/R/coxnet.deviance.R0000644000176200001440000000467113553702453015160 0ustar liggesusers#' compute deviance for cox model output #' #' Given a fit or coefficients, compute the deciance (-2 log partial likelihood) for #' right-censored survival data #' #' \code{coxnet.deviance} computes the deviance for a single prediction, or a matrix of predictions #' #' @aliases coxnet.deviance #' @param pred matrix of predictions #' @param y a survival response matrix, as produced by \code{Surv} #' @param x optional \code{x} matrix, if \code{pred} is \code{NULL} #' @param offset optional offset #' @param weights optional observation weights #' @param beta optional coefficient vector/matrix, supplied if \code{pred=NULL} #' @return a single or vector of deviances #' @author Trevor Hastie\cr Maintainer: Trevor Hastie #' @seealso \code{coxgrad} #' @keywords Cox model #' #' @export coxnet.deviance coxnet.deviance <- function (pred = NULL, y, x = 0, offset = NULL, weights = NULL, beta = NULL) { storage.mode(x) = "double" y = response.coxnet(y) ty = y$time tevent = y$event ty = ty + (1 - tevent) * 100 * .Machine$double.eps nobs = as.integer(length(ty)) nvars = as.integer(ncol(x)) nvec=1 if (is.null(weights)) weights = rep(1, nobs) else { weights=nobs*weights/sum(weights) weights = as.double(weights) } ### Compute saturated loglikelihood wd=weights[tevent==1] tyd=ty[tevent==1] if(any(duplicated(tyd))){ wd=tapply(wd,tyd,sum) } wd=wd[wd>0] lsat=-sum(wd*log(wd)) #### if (is.null(offset)) offset = rep(0, nobs) else offset=as.double(offset) if (is.null(beta)) { beta = double(0) nvars = as.integer(0) } else { beta = as.matrix(beta) nvec = ncol(beta) } if(!is.null(pred)){ # trick to get a set of deviances based on predictions" x=as.matrix(pred) nvec=ncol(x) storage.mode(x)="double" beta=diag(nvec) nvars=as.integer(nvec) storage.mode(beta)="double" } nvec=as.integer(nvec) fit = .Fortran("loglike", nobs, nvars, x, ty, tevent, offset, weights, nvec, beta, flog = double(nvec), jerr = integer(1), PACKAGE = "glmnet") if (fit$jerr != 0) { errmsg = jerr(fit$jerr, maxit = 0, pmax = 0, family = "cox") if (errmsg$fatal) stop(errmsg$msg, call. = FALSE) else warning(errmsg$msg, call. = FALSE) } 2 *(lsat-fit$flog) } glmnet/R/response.coxnet.R0000644000176200001440000000063713534022702015225 0ustar liggesusersresponse.coxnet=function(y){ if(!is.matrix(y)||!all(match(c("time","status"),dimnames(y)[[2]],0)))stop("Cox model requires a matrix with columns 'time' (>0) and 'status' (binary) as a response; a 'Surv' object suffices",call.=FALSE) ty=as.double(y[,"time"]) tevent=as.double(y[,"status"]) if(any(ty<=0))stop("negative event times encountered; not permitted for Cox family") list(time=ty,event=tevent) } glmnet/R/coefnorm.R0000644000176200001440000000031513534022702013671 0ustar liggesuserscoefnorm=function(coeflist,q=1){ ###coeflist comes from mrelnet or multnet nmat=coeflist[[1]]*0 nclass=length(coeflist) for(i in seq(nclass)){ nmat=nmat+abs(coeflist[[i]])^q } nmat^(1/q) } glmnet/R/predict.mrelnet.R0000644000176200001440000000054513552673674015211 0ustar liggesusers#' @method predict mrelnet #' @export predict.mrelnet=function(object, newx, s = NULL, type = c("link", "response", "coefficients", "nonzero"), exact = FALSE, newoffset, ...) { type=match.arg(type) if(type=="response")type="link" object$grouped=TRUE predict.multnet(object,newx,s,type,exact,newoffset,...) } glmnet/R/getOptcv.glmnet.R0000644000176200001440000000065413534022702015147 0ustar liggesusersgetOptcv.glmnet <- function (lambda, cvm, cvsd,cvname) { if(match(cvname,c("AUC","C-index"),0))cvm=-cvm cvmin = min(cvm, na.rm = TRUE) idmin = cvm <= cvmin lambda.min = max(lambda[idmin], na.rm = TRUE) idmin = match(lambda.min, lambda) semin = (cvm + cvsd)[idmin] idmin = cvm <= semin lambda.1se = max(lambda[idmin], na.rm = TRUE) list(lambda.min = lambda.min, lambda.1se = lambda.1se) } glmnet/R/cv.mrelnet.R0000644000176200001440000000113413566632235014153 0ustar liggesuserscv.mrelnet <- function(predmat,y,type.measure,weights,foldid,grouped){ ndim = dim(y) nc = ndim[2] nobs = ndim[1] N = nobs - apply(is.na(predmat[, 1,,drop=FALSE ]), 2, sum)# dimensions could be lost if third dim=1 bigY = array(y, dim(predmat)) cvraw = switch(type.measure, mse = apply((bigY - predmat)^2,c(1, 3), sum), deviance = apply((bigY - predmat)^2, c(1,3), sum), mae = apply(abs(bigY - predmat), c(1, 3), sum) ) list(cvraw=cvraw,weights=weights,N=N,type.measure=type.measure,grouped=grouped) } glmnet/R/print.cv.glmnet.R0000644000176200001440000000367013553730725015134 0ustar liggesusers#' print a cross-validated glmnet object #' #' Print a summary of the results of cross-validation for a glmnet model. #' #' A summary of the cross-validated fit is produced, slightly different for a #' 'cv.relaxed' object than for a 'cv.glmnet' object. Note that a 'cv.relaxed' #' object inherits from class 'cv.glmnet', so by directly invoking #' \code{print.cv.glmnet(object)} will print the summary as if #' \code{relax=TRUE} had not been used. #' #' @aliases print.cv.glmnet print.cv.relaxed #' @param x fitted 'cv.glmnet' object #' @param digits significant digits in printout #' @param \dots additional print arguments #' @author Jerome Friedman, Trevor Hastie and Rob Tibshirani\cr Maintainer: #' Trevor Hastie #' @seealso \code{glmnet}, \code{predict} and \code{coef} methods. #' @references Friedman, J., Hastie, T. and Tibshirani, R. (2008) #' \emph{Regularization Paths for Generalized Linear Models via Coordinate #' Descent}\cr \url{https://arxiv.org/abs/1707.08692}\cr Hastie, T., #' Tibshirani, Robert, Tibshirani, Ryan (2019) \emph{Extended Comparisons of #' Best Subset Selection, Forward Stepwise Selection, and the Lasso} #' @keywords models regression #' @examples #' #' x = matrix(rnorm(100 * 20), 100, 20) #' y = rnorm(100) #' fit1 = cv.glmnet(x, y) #' print(fit1) #' fit1r = cv.glmnet(x, y, relax = TRUE) #' print(fit1r) #' ## print.cv.glmnet(fit1r) ## CHECK WITH TREVOR #' @method print cv.glmnet #' @export #' @export print.cv.glmnet print.cv.glmnet <- function(x, digits = max(3, getOption("digits") - 3), ...) { cat("\nCall: ", deparse(x$call), "\n\n") optlams=c(x$lambda.min,x$lambda.1se) which=match(optlams,x$lambda) mat=with(x,cbind(optlams,cvm[which],cvsd[which],nzero[which])) dimnames(mat)=list(c("min","1se"),c("Lambda","Measure","SE","Nonzero")) cat("Measure:", x$name,"\n\n") mat=data.frame(mat,check.names=FALSE) class(mat)=c("anova",class(mat)) print(mat,digits=digits) } glmnet/R/predict.cv.relaxed.R0000644000176200001440000000161513552673674015575 0ustar liggesusers#' @method predict cv.relaxed #' @param gamma Value (single) of 'gamma' at which predictions are to be made #' @rdname predict.cv.glmnet #' @export predict.cv.relaxed <- function (object, newx, s = c("lambda.1se", "lambda.min"), gamma=c("gamma.1se","gamma.min"),...) { relaxed=object$relaxed if (is.numeric(s)) lambda = s else if (is.character(s)) { s = match.arg(s) lambda = relaxed[[s]] if(missing(gamma)){ gamma=switch(s, lambda.1se=relaxed[["gamma.1se"]], lambda.min=relaxed[["gamma.min"]] ) } } else stop("Invalid form for s") if (is.character(gamma)) { gamma = match.arg(gamma) gamma = relaxed[[gamma]] } if(!is.numeric(gamma))stop("Invalid form for gamma") predict(object$glmnet.fit, newx, s = lambda,gamma=gamma, ...) } glmnet/R/Cindex.R0000644000176200001440000000502613552673674013323 0ustar liggesusers#' compute C index for a Cox model #' #' Computes Harrel's C index for predictions from a \code{"coxnet"} object. #' #' Computes the concordance index, taking into account censoring. #' #' @param pred Predictions from a \code{"coxnet"} object #' @param y a survival response object - a matrix with two columns "time" and #' "status"; see documentation for "glmnet" #' @param weights optional observation weights #' @author Rob Tibshirani\cr Maintainer: Trevor Hastie #' @seealso \code{cv.glmnet} #' @references Harrel Jr, F. E. and Lee, K. L. and Mark, D. B. (1996) #' \emph{Tutorial in biostatistics: multivariable prognostic models: issues in #' developing models, evaluating assumptions and adequacy, and measuring and #' reducing error}, Statistics in Medicine, 15, pages 361--387. #' @keywords Cox models cross-validation #' @examples #' #' set.seed(10101) #' N = 1000 #' p = 30 #' nzc = p/3 #' x = matrix(rnorm(N * p), N, p) #' beta = rnorm(nzc) #' fx = x[, seq(nzc)] %*% beta/3 #' hx = exp(fx) #' ty = rexp(N, hx) #' tcens = rbinom(n = N, prob = 0.3, size = 1) # censoring indicator #' y = cbind(time = ty, status = 1 - tcens) # y=Surv(ty,1-tcens) with library(survival) #' fit = glmnet(x, y, family = "cox") #' pred = predict(fit, newx = x) #' Cindex(pred, y) #' cv.glmnet(x, y, family = "cox", type.measure = "C") #' #' @export Cindex Cindex=function(pred,y,weights=rep(1,nrow(y))){ ### Written by Rob Tibshirani; edited by Trevor Hastie ### This function gives identical results to the summary function from coxph, ### and the concordance.index function in survcomp (with their default settings), ### and no ties in yhat. ### Works with ties in y. But does not agree with latter when yhat has ties. ### There are conflicting definitions for c-index in this case ### ### Formula used is ### Concordance = (num. all concordant pairs + num. tied pairs/2)/(num. total pairs including ties). ### with weights, weights used are average wts for each pair (??) status=y[,"status"] ty=y[,"time"] risksets=which(status==1) weights=length(weights)*weights/sum(weights) fun=function(riskset,ty,pred,weights){ total=concordant=0 i=riskset rest=which(ty>ty[i]) if(length(rest)>0){ ww=(weights[rest]+weights[i])/2 total=sum(ww) concordant = 1.0*sum(ww*(pred[rest] #' @seealso \code{glmnet} #' @keywords models #' @examples #' #' set.seed(101) #' ### Single data frame #' X = matrix(rnorm(20), 10, 2) #' X3 = sample(letters[1:3], 10, replace = TRUE) #' X4 = sample(LETTERS[1:3], 10, replace = TRUE) #' df = data.frame(X, X3, X4) #' makeX(df) #' makeX(df, sparse = TRUE) #' #' ### Single data freame with missing values #' Xn = X #' Xn[3, 1] = NA #' Xn[5, 2] = NA #' X3n = X3 #' X3n[6] = NA #' X4n = X4 #' X4n[9] = NA #' dfn = data.frame(Xn, X3n, X4n) #' #' makeX(dfn) #' makeX(dfn, sparse = TRUE) #' makeX(dfn, na.impute = TRUE) #' makeX(dfn, na.impute = TRUE, sparse = TRUE) #' #' ### Test data as well #' X = matrix(rnorm(10), 5, 2) #' X3 = sample(letters[1:3], 5, replace = TRUE) #' X4 = sample(LETTERS[1:3], 5, replace = TRUE) #' dft = data.frame(X, X3, X4) #' #' makeX(df, dft) #' makeX(df, dft, sparse = TRUE) #' #' ### Missing data in test as well #' Xn = X #' Xn[3, 1] = NA #' Xn[5, 2] = NA #' X3n = X3 #' X3n[1] = NA #' X4n = X4 #' X4n[2] = NA #' dftn = data.frame(Xn, X3n, X4n) #' #' makeX(dfn, dftn) #' makeX(dfn, dftn, sparse = TRUE) #' makeX(dfn, dftn, na.impute = TRUE) #' makeX(dfn, dftn, sparse = TRUE, na.impute = TRUE) #' #' @export makeX makeX=function(train,test=NULL, na.impute=FALSE,sparse=FALSE,...){ df=train istest=!is.null(test) ntr=nrow(train) if(istest){ nte=nrow(test) df=rbind(df,test) } x=prepareX(df,sparse=sparse) if(na.impute){ xbar=colMeans(x[seq(ntr),],na.rm=TRUE) x=na.replace(x,xbar) } if(istest){ xt=x[seq(ntr+1,ntr+nte),] x=x[seq(ntr),] } if(na.impute)attr(x,"means")=xbar if(istest)list(x=x,xtest=xt) else x } #' @importFrom stats contrasts model.matrix na.pass #' @importFrom Matrix sparse.model.matrix #' @export prepareX prepareX <- function(df,sparse=FALSE,...){ if(!inherits(df,"data.frame"))stop("first argument must be of class `data.frame`") whichfac=sapply(df,inherits,"factor") oldna=options()$na.action cna=as.character(substitute(na.action)) options(na.action=na.pass) on.exit(options(na.action=oldna)) if(any(whichfac)) ctr=lapply(df[,whichfac], contrasts,contrast=FALSE) else ctr=NULL if(sparse){ m=sparse.model.matrix(~.-1,data=df,contrasts.arg=ctr,...) m=na_sparse_fix(m,names(df)) # sparse.model.matrix is faulty } else m = model.matrix(~.-1,data=df,contrasts.arg=ctr,...) if(any(whichfac))attr(m,"contrasts")=NULL attr(m,"assign")=NULL m } #' @export na_sparse_fix na_sparse_fix=function(x,dfnames){ a=attributes(x) ac=a$contrasts as=a$assign if(is.null(ac))return(x) acn=names(ac) whichn=match(acn,dfnames) for(i in whichn){ xi=x[,as==i] rowtot=rowSums(xi) if(sum(rowtot) #' @seealso \code{makeX} and \code{glmnet} #' @keywords models #' @examples #' #' set.seed(101) #' ### Single data frame #' X = matrix(rnorm(20), 10, 2) #' X[3, 1] = NA #' X[5, 2] = NA #' X3 = sample(letters[1:3], 10, replace = TRUE) #' X3[6] = NA #' X4 = sample(LETTERS[1:3], 10, replace = TRUE) #' X4[9] = NA #' dfn = data.frame(X, X3, X4) #' #' x = makeX(dfn) #' m = rowSums(x, na.rm = TRUE) #' na.replace(x, m) #' #' x = makeX(dfn, sparse = TRUE) #' na.replace(x, m) #' #' @export na.replace na.replace=function(x,m=rowSums(x,na.rm=TRUE)){ if(inherits(x,"sparseMatrix")){ x=as(x,"CsparseMatrix") ccount=diff(x@p) cindex=rep(1:length(ccount),ccount) nas=is.na(x@x) if(any(nas))x@x[nas]=m[cindex[nas]] } else{ d=dim(x) cols=rep(1:d[2],rep(d[1],d[2])) nas=is.na(x) if(any(nas))x[nas]=m[cols[nas]] } x } glmnet/R/coef.relaxed.R0000644000176200001440000000025513557667631014450 0ustar liggesusers#' @method coef relaxed #' @export #' @export coef.relaxed coef.relaxed=function(object,s=NULL, gamma=1,...) predict(object, s=s, gamma= gamma, type="coefficients",...) glmnet/R/mrelnet.R0000644000176200001440000000406513534022702013535 0ustar liggesusersmrelnet=function(x,is.sparse,ix,jx,y,weights,offset,alpha,nobs,nvars,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,jsd,intr,vnames,maxit){ maxit=as.integer(maxit) weights=as.double(weights) ### compute the null deviance y=as.matrix(y) nr=ncol(y) if(nr>1){ responseNames=dimnames(y)[[2]] if(is.null(responseNames))responseNames=paste("y",seq(nr),sep="") } storage.mode(y)="double" if(is.null(offset)){ is.offset=FALSE} else{ offset=as.matrix(offset) storage.mode(offset)="double" if(!all.equal(dim(offset),dim(y)))stop("Offset must match dimension of y") y=y-offset is.offset=TRUE } nulldev=0 for(i in seq(nr)){ ybar=weighted.mean(y[,i],weights) nulldev=nulldev+sum(weights* (y[,i]-ybar)^2) } storage.mode(nr)="integer" fit=if(is.sparse).Fortran("multspelnet", parm=alpha,nobs,nvars,nr,x,ix,jx,y,weights,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,jsd,intr,maxit, lmu=integer(1), a0=double(nlam*nr), ca=double(nx*nlam*nr), ia=integer(nx), nin=integer(nlam), rsq=double(nlam), alm=double(nlam), nlp=integer(1), jerr=integer(1),PACKAGE="glmnet" ) else .Fortran("multelnet", parm=alpha,nobs,nvars,nr,as.double(x),y,weights,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,jsd,intr,maxit, lmu=integer(1), a0=double(nlam*nr), ca=double(nx*nlam*nr), ia=integer(nx), nin=integer(nlam), rsq=double(nlam), alm=double(nlam), nlp=integer(1), jerr=integer(1),PACKAGE="glmnet" ) if(fit$jerr!=0){ errmsg=jerr(fit$jerr,maxit,pmax=nx,family="mrelnet") if(errmsg$fatal)stop(errmsg$msg,call.=FALSE) else warning(errmsg$msg,call.=FALSE) } if(nr>1) outlist=getcoef.multinomial(fit,nvars,nx,vnames,nr,responseNames,center.intercept=FALSE) else outlist=getcoef(fit,nvars,nx,vnames) dev=fit$rsq[seq(fit$lmu)] outlist=c(outlist,list(dev.ratio=dev,nulldev=nulldev,npasses=fit$nlp,jerr=fit$jerr,offset=is.offset)) class(outlist)="mrelnet" outlist } glmnet/R/jerr.fishnet.R0000644000176200001440000000046113534022702014464 0ustar liggesusersjerr.fishnet=function(n,maxit,pmax){ outlist=jerr.elnet(n,maxit,pmax) if(outlist$msg!="Unknown error")return(outlist) if(n==8888)msg="Negative response values - should be counts" else if(n==9999)msg="No positive observation weights" else msg="Unknown error" list(n=n,fatal=TRUE,msg=msg) } glmnet/R/plot.cv.relaxed.R0000644000176200001440000000335313552673674015122 0ustar liggesusers#' method plot cv.relaxed #' @importFrom shape colorlegend #' @param se.bands Should shading be produced to show standard-error bands; #' default is \code{TRUE} #' @rdname plot.cv.glmnet #' @export plot.cv.relaxed <- function (x, se.bands=TRUE,...) { xr=x$relaxed oldpar = par(mar = c(4, 4, 3, 4)) on.exit(par(oldpar)) statlist=xr$statlist gamma=xr$gamma ngamma=length(gamma) ylim=range(unlist(lapply(statlist,"[[","cvm"))) if(se.bands){ cvup=lapply(statlist,"[[","cvup") cvlo=lapply(statlist,"[[","cvlo") ylim=range(ylim,unlist(cvup),unlist(cvlo)) } xlim=log(range(unlist(lapply(statlist,"[[","lambda")))+0.00001) cvcolors = rainbow(ngamma, start = .1, end = 1) with(statlist[[ngamma]],plot(log(lambda), cvm, type = "n", xlab = expression(Log(lambda)), ylab = x$name,ylim=ylim,xlim=xlim)) if(se.bands){ for (i in seq(ngamma)) with(statlist[[i]],polygon(c(log(lambda),rev(log(lambda))),c(cvup,rev(cvlo)), col="floralwhite",border="antiquewhite")) } for (i in seq(ngamma)) with(statlist[[i]],lines(log(lambda), cvm, lwd = 2, col = cvcolors[i])) mins=log(c(xr$lambda.min,xr$lambda.1se)) abline(v=mins,lty=3) dof= statlist[[1]]$nzero lambda=statlist[[1]]$lambda axis(side = 3, at = log(lambda), labels = paste(dof), tick = FALSE, line = 0) # dof=c(x$nzero.min,x$nzero.1se) # axis(3, at = mins, labels = format(dof), tick=FALSE, line = 0,cex.axis=.9) colorlegend(posy = c(0.2, 0.8), posx = c(0.93, 0.945)-.03, col = rainbow(ngamma, start = 0.1, end = 1), zlim = c(0, 1), zval = gamma , main = expression(gamma),digit=2) invisible() } glmnet/R/pb.R0000644000176200001440000000022413573755070012477 0ustar liggesusers## Create and store the pb createPB <- function(...) { pb <- utils::txtProgressBar(...) .Call("storePB", pb, PACKAGE = "glmnet") pb } glmnet/R/roc.glmnet.R0000644000176200001440000000265313557670702014156 0ustar liggesusers#' Compute the roc curve(s) for a binimial glmnet model. #' #' @rdname assess.glmnet #' @export roc.glmnet roc.glmnet=function(object,newx=NULL,newy,...){ ### object must be either a glmnet or cv.glmnet object, or else a matrix or a vector of predictions of a glmnet model oclass=grep("glmnet",class(object)) if(length(oclass)){# either a glmnet or cv.glmnet if(family(object)!="binomial")stop("roc available only for binomial family") predmat=predict(object,newx=newx,...) } else{ predmat=object if(is.vector(predmat))predmat=as.matrix(predmat) } roc.raw=function(prediction,y){ op=order(prediction,decreasing=TRUE) preds=prediction[op] y=y[op] noty=1-y if(any(duplicated(preds))){ y=rev(tapply(y,preds,sum)) noty=rev(tapply(noty,preds,sum)) } data.frame( FPR=cumsum(noty)/sum(noty),TPR=cumsum(y)/sum(y)) } ### newy should be binary, but could be a two-class matrix! nc = dim(newy) if (is.null(nc)) { newy = as.factor(newy) ntab = table(newy) nc = length(ntab) newy = diag(nc)[as.numeric(newy), ] } else { nc = nc[2] } if(nc!=2)stop("newy should be binary, or a factor with two levels") newy=newy[,2] n=nrow(predmat) if(n!=length(newy))stop("mismatch in size of two arguments") out=apply(predmat,2,roc.raw,y=newy) if(length(out)==1)out=out[[1]] out } glmnet/R/na.mean.R0000644000176200001440000000030113534022702013371 0ustar liggesusersna.mean=function(x){ ### replace missing entries with mean for that column. namean=function(x){ nas=is.na(x) if(any(nas)) x[nas]=mean(x,na.rm=TRUE) x } apply(x,2,namean) } glmnet/R/error.bars.R0000644000176200001440000000041013534022702014134 0ustar liggesuserserror.bars <- function(x, upper, lower, width = 0.02, ...) { xlim <- range(x) barw <- diff(xlim) * width segments(x, upper, x, lower, ...) segments(x - barw, upper, x + barw, upper, ...) segments(x - barw, lower, x + barw, lower, ...) range(upper, lower) } glmnet/R/cv.lognet.R0000644000176200001440000000424613534022702013767 0ustar liggesuserscv.lognet <- function(predmat,y,type.measure,weights,foldid,grouped){ prob_min = 1e-05 prob_max = 1 - prob_min nc = dim(y) if (is.null(nc)) { y = as.factor(y) ntab = table(y) nc = as.integer(length(ntab)) y = diag(nc)[as.numeric(y), ] } N = nrow(y) nfolds = max(foldid) if ((N/nfolds < 10) && type.measure == "auc") { warning("Too few (< 10) observations per fold for type.measure='auc' in cv.lognet; changed to type.measure='deviance'. Alternatively, use smaller value for nfolds", call. = FALSE) type.measure = cvtype("deviance", "lognet") } predmat=1/(1+exp(-predmat)) nlambda=ncol(predmat) nlams=rep(nlambda,nfolds) if (type.measure == "auc") { cvraw = matrix(NA, nfolds, nlambda) good = matrix(0, nfolds, nlambda) for (i in seq(nfolds)) { good[i, seq(nlams[i])] = 1 which = foldid == i for (j in seq(nlams[i])) { cvraw[i, j] = auc.mat(y[which, ], predmat[which, j], weights[which]) } } N = apply(good, 2, sum) weights = tapply(weights, foldid, sum) grouped=FALSE } else { ywt = apply(y, 1, sum) y = y/ywt weights = weights * ywt N = nrow(y) - apply(is.na(predmat), 2, sum) cvraw = switch(type.measure, mse = (y[, 1] - (1 - predmat))^2 + (y[, 2] - predmat)^2, mae = abs(y[, 1] - (1 - predmat)) + abs(y[, 2] - predmat), deviance = { predmat = pmin(pmax(predmat, prob_min), prob_max) lp = y[, 1] * log(1 - predmat) + y[, 2] * log(predmat) ly = log(y) ly[y == 0] = 0 ly = drop((y * ly) %*% c(1, 1)) 2 * (ly - lp) }, class = y[, 1] * (predmat > 0.5) + y[, 2] * (predmat <= 0.5) ) } list(cvraw=cvraw,weights=weights,N=N,type.measure=type.measure,grouped=grouped) } glmnet/R/confusion.glmnet.R0000644000176200001440000000402113566625272015367 0ustar liggesusers#' Compute a confusion matrix (or many) for a binomial or multinomial glmnet model #' #' @rdname assess.glmnet #' @export confusion.glmnet confusion.glmnet=function(object,newx=NULL,newy,family=c("binomial","multinomial"),...){ ### object must be either a glmnet or cv.glmnet object fit with family binomial or multinomial ### or else a matrix/array of predictions of a glmnet model of these families ### (the last dimension can be 1) ### (not on the mean scale, but natural parameter scale) oclass=grep("glmnet",class(object)) if(length(oclass)){# either a glmnet or cv.glmnet if(!match(family(object),c("binomial","multinomial"),FALSE)) stop("confusion available only for binomial or multinomial family") classmat=predict(object,newx=newx,type="class",...) } else{ fam=match.arg(family) predmat=object if(fam=="binomial"){ classnames=attr(predmat,"classnames")#if it was created by cv with keep if(is.null(classnames))classnames=c("1","2") if(is.vector(predmat))predmat=as.matrix(predmat) cnum=ifelse(predmat>0,2,1) classmat=classnames[cnum] if(is.matrix(predmat))classmat=array(classmat,dim(predmat),dimnames(predmat)) } else{ classmat=apply(predmat, 3, glmnet_softmax) } } ### newy can be a matrix or a factor nc = dim(newy) if (is.null(nc)) { newy = as.factor(newy) } else { nc = nc[2] yc=newy%*%(1:nc) cn=colnames(newy) if(!is.null(cn)) newy=factor(yc,labels=cn) else newy=factor(yc) } ctable=function(...){ tab=table(...) class(tab)=c("confusion.table",class(tab)) tab } if(ncol(classmat)>1) ## convert to a dataframe, to prevent apply simplifying lapply(data.frame(classmat,stringsAsFactors=FALSE,check.names=FALSE), function(x,y)ctable(Predicted=x,True=y),y=newy) else(ctable(Predicted=classmat,True=newy)) } glmnet/R/predict.coxnet.R0000644000176200001440000000260313552673674015040 0ustar liggesusers#' @method predict coxnet #' @export predict.coxnet=function(object,newx,s=NULL,type=c("link","response","coefficients","nonzero"),exact=FALSE,newoffset,...){ type=match.arg(type) ###coxnet has no intercept, so we treat it separately if(missing(newx)){ if(!match(type,c("coefficients","nonzero"),FALSE))stop("You need to supply a value for 'newx'") } if(exact&&(!is.null(s))){ lambda=object$lambda which=match(s,lambda,FALSE) if(!all(which>0)){ lambda=unique(rev(sort(c(s,lambda)))) check_dots(object,...)# This fails if you don't supply the crucial arguments object=update(object,lambda=lambda,...) } } nbeta=object$beta if(!is.null(s)){ vnames=dimnames(nbeta)[[1]] dimnames(nbeta)=list(NULL,NULL) lambda=object$lambda lamlist=lambda.interp(lambda,s) nbeta=nbeta[,lamlist$left,drop=FALSE]%*%Diagonal(x=lamlist$frac) +nbeta[,lamlist$right,drop=FALSE]%*%Diagonal(x=1-lamlist$frac) dimnames(nbeta)=list(vnames,paste(seq(along=s))) } if(type=="coefficients")return(nbeta) if(type=="nonzero")return(nonzeroCoef(nbeta,bystep=TRUE)) nfit=as.matrix(newx%*%nbeta) if(object$offset){ if(missing(newoffset))stop("No newoffset provided for prediction, yet offset used in fit of glmnet",call.=FALSE) nfit=nfit+array(newoffset,dim=dim(nfit)) } switch(type, response=exp(nfit), link=nfit ) } glmnet/R/plot.relaxed.R0000644000176200001440000000241113552673674014505 0ustar liggesusers#' @method plot relaxed #' @param gamma Value of the mixing parameter for a "relaxed" fit #' @rdname plot.glmnet #' @export plot.relaxed <- function(x,xvar = c("lambda", "dev"), label = FALSE, gamma=1, ...) { xvar=match.arg(xvar) if(any(wh<-gamma<0)){ warning("negative gamma values ignored") gamma=gamma[!wh] } if(any(wh<-gamma>1)){ warning("gamma values larger than 1 ignored") gamma=gamma[!wh] } if(!length(gamma))stop("no valid values of gamma") for(i in seq(along=length(gamma))){ bfit=blend.relaxed(x,gamma=gamma[i],extend=FALSE) plot(bfit,xvar=xvar,label=label,...) } } #' @method print relaxed #' @export print.relaxed <- function (x, digits = max(3, getOption("digits") - 3), ...) { cat("\nCall: ", deparse(x$call), "\n") cat("Relaxed\n\n") which=match(x$relaxed$lambda,x$lambda,0) rdev=rep(NA,length(x$lambda)) rdev[which]=signif(x$relaxed$dev.ratio, digits) out=data.frame(Df = x$df, `%Dev` = signif(x$dev.ratio, digits), `%Dev R`=rdev, Lambda = signif(x$lambda, digits),check.names=FALSE,row.names=seq(along=rdev)) class(out)=c("anova",class(out)) print(out) } glmnet/R/cv.fishnet.R0000644000176200001440000000102513534022702014127 0ustar liggesuserscv.fishnet <-function(predmat,y,type.measure,weights,foldid,grouped){ devi = function(y, eta) { deveta = y * eta - exp(eta) devy = y * log(y) - y devy[y == 0] = 0 2 * (devy - deveta) } N = length(y) - apply(is.na(predmat), 2, sum) cvraw = switch(type.measure, mse = (y - exp(predmat))^2, mae = abs(y - exp(predmat)), deviance = devi(y, predmat) ) list(cvraw=cvraw,weights=weights,N=N,type.measure=type.measure,grouped=grouped) } glmnet/R/bigGlm.R0000644000176200001440000000616513561056202013275 0ustar liggesusers#' fit a glm with all the options in \code{glmnet} #' #' Fit a generalized linear model as in \code{glmnet} but unpenalized. This #' allows all the features of \code{glmnet} such as sparse x, bounds on #' coefficients, offsets, and so on. #' #' This is essentially the same as fitting a "glmnet" model with a single value #' \code{lambda=0}, but it avoids some edge cases. CAVEAT: If the user tries a #' problem with N smaller than or close to p for some models, it is likely to #' fail (and maybe not gracefully!) If so, use the \code{path=TRUE} argument. #' #' @param x input matrix #' @param ... Most other arguments to glmnet that make sense #' @param path Since \code{glmnet} does not do stepsize optimization, the Newton #' algorithm can get stuck and not converge, especially with unpenalized fits. With \code{path=TRUE}, #' the fit computed with pathwise lasso regularization. The current implementation does this twice: #' the first time to get the lambda sequence, and the second time with a zero attached to the end). #' Default is \code{path=FALSE}. #' @return It returns an object of class "bigGlm" that inherits from class #' "glmnet". That means it can be predicted from, coefficients extracted via #' \code{coef}. It has its own print method. #' @author Trevor Hastie\cr Maintainer: Trevor Hastie #' \email{hastie@@stanford.edu} #' @seealso \code{print}, \code{predict}, and \code{coef} methods. #' @keywords models regression #' @examples #' #' # Gaussian #' x = matrix(rnorm(100 * 20), 100, 20) #' y = rnorm(100) #' fit1 = bigGlm(x, y) #' print(fit1) #' #' fit2=bigGlm(x,y>0,family="binomial") #' print(fit2) #' fit2p=bigGlm(x,y>0,family="binomial",path=TRUE) #' print(fit2p) #' #' @export bigGlm bigGlm=function (x,..., path=FALSE){ ### fit a model using x, y and the other glmnet specs in ... unpenalized thiscall = match.call(glmnet) ### Next few lines simply in case x is a vector, in which case glmnet complains bx=cbind(x,1) d=dim(bx) n=d[1] p=d[2]-1 bx[1,p+1]=0 # so its not a constant arglist = list(...) arglist$x = bx arglist$exclude=p+1 if(!path){ arglist$lambda=0 fit = do.call("glmnet", arglist) if (is.list(fit$beta)) fit$beta = lapply(fit$beta, function(x, p) x[1:p, , drop = FALSE], p) else fit$beta = fit$beta[1:p, , drop = FALSE] } else { fit = do.call("glmnet", arglist)# get the lambda sequence lam0=c(fit$lambda,0) arglist$lambda=lam0 fit = do.call("glmnet", arglist) nlam=length(fit$lambda)#may have stopped early if (is.list(fit$beta)){ fit$a0=fit$a0[,nlam,drop=FALSE] fit$beta = lapply(fit$beta, function(x, p,nlam) x[1:p,nlam , drop = FALSE], p,nlam) dfmat=fit$dfmat if(!is.null(dfmat))fit$dfmat=dfmat[,nlam,drop=FALSE] } else { fit$a0=fit$a0[nlam] fit$beta = fit$beta[1:p,nlam , drop = FALSE] } fit$df=p fit$lambda=0 fit$dim=c(fit$dim[1],1) fit$dev.ratio=fit$dev.ratio[nlam] } fit$call = thiscall class(fit) = c("bigGlm", class(fit)) fit } glmnet/R/cvstats.R0000644000176200001440000000127413534022702013555 0ustar liggesuserscvstats=function(cvstuff,foldid,nfolds,lambda,nz,grouped,...){ if (grouped){ nlams=rep(dim(cvstuff$cvraw)[2],nfolds) ## All the same - this should go cvstuff= cvcompute(cvstuff, foldid, nlams) } cvm=with(cvstuff,apply(cvraw, 2, weighted.mean, w = weights, na.rm = TRUE)) cvsd=with(cvstuff, sqrt(apply(scale(cvraw, cvm, FALSE)^2, 2, weighted.mean, w = weights, na.rm = TRUE)/(N - 1))) nas=is.na(cvsd) if(any(nas)){ lambda=lambda[!nas] cvm=cvm[!nas] cvsd=cvsd[!nas] nz=nz[!nas] } list(lambda = lambda, cvm = cvm, cvsd = cvsd, cvup = cvm + cvsd, cvlo = cvm - cvsd, nzero = nz) } glmnet/R/predict.multnet.R0000644000176200001440000000571613574023574015230 0ustar liggesusers#' @method predict multnet #' @export predict.multnet <- function (object, newx, s = NULL, type = c("link", "response", "coefficients", "class", "nonzero"), exact = FALSE, newoffset, ...) { type = match.arg(type) ###multnet is very different, so we treat it separately if(missing(newx)){ if(!match(type,c("coefficients","nonzero"),FALSE))stop("You need to supply a value for 'newx'") } if(exact&&(!is.null(s))){ lambda=object$lambda which=match(s,lambda,FALSE) if(!all(which>0)){ lambda=unique(rev(sort(c(s,lambda)))) check_dots(object,...)# This fails if you don't supply the crucial arguments object=update(object,lambda=lambda,...) } } a0 = object$a0 rownames(a0) = rep("(Intercept)", nrow(a0)) nbeta = object$beta klam = dim(a0) nclass = klam[[1]] nlambda = length(s) if (!is.null(s)) { lambda = object$lambda lamlist = lambda.interp(lambda, s) for (i in seq(nclass)) { kbeta = methods::rbind2(a0[i, , drop = FALSE], nbeta[[i]])#was rbind2 vnames = dimnames(kbeta)[[1]] dimnames(kbeta) = list(NULL, NULL) kbeta = kbeta[, lamlist$left, drop = FALSE] %*% Diagonal(x=lamlist$frac) + kbeta[, lamlist$right, drop = FALSE] %*% Diagonal(x=1 - lamlist$frac) dimnames(kbeta) = list(vnames, paste(seq(along = s))) nbeta[[i]] = kbeta } } else { for (i in seq(nclass)) nbeta[[i]] = methods::rbind2(a0[i, ], nbeta[[i]])#was rbind2 nlambda=length(object$lambda) } if (type == "coefficients") return(nbeta) if (type == "nonzero") # if(object$grouped)return(nonzeroCoef(object$beta[[1]],bystep=TRUE)) if(object$grouped)return(nonzeroCoef(nbeta[[1]],bystep=TRUE)) else return(lapply(nbeta, function(x) nonzeroCoef(x[-1, , drop = FALSE], bystep = TRUE))) dd = dim(newx) if (inherits(newx, "sparseMatrix")) newx = as(newx, "dgCMatrix") npred = dd[[1]] dn = list(names(nbeta), dimnames(nbeta[[1]])[[2]], dimnames(newx)[[1]]) dp = array(0, c(nclass, nlambda, npred), dimnames = dn) for (i in seq(nclass)) { fitk = cbind2(1, newx) %*% (nbeta[[i]]) dp[i, , ] = dp[i, , ] + t(as.matrix(fitk)) } if (object$offset) { if (missing(newoffset)) stop("No newoffset provided for prediction, yet offset used in fit of glmnet", call. = FALSE) if (!is.matrix(newoffset) || dim(newoffset)[[2]] != nclass) stop(paste("Dimension of newoffset should be", npred, "x", nclass), call. = FALSE) toff = t(newoffset) for (i in seq(nlambda)) { dp[, i, ] = dp[, i, ] + toff } } switch(type, response = { pp = exp(dp) psum = apply(pp, c(2, 3), sum) aperm(pp/rep(psum, rep(nclass, nlambda * npred)), c(3, 1, 2)) }, link = aperm(dp, c(3, 1, 2)), class = { dp = aperm(dp, c(3, 1, 2)) apply(dp, 3, glmnet_softmax) }) } glmnet/R/fix.lam.R0000644000176200001440000000015413534022702013420 0ustar liggesusersfix.lam=function(lam){ if(length(lam)>2){ llam=log(lam) lam[1]=exp(2*llam[2]-llam[3]) } lam } glmnet/R/zeromat.R0000644000176200001440000000040713534022702013544 0ustar liggesuserszeromat=function(nvars,lmu,vnames,stepnames){ ca=rep(0,lmu) ia=seq(lmu+1) ja=rep(1,lmu) dd=c(nvars,lmu) new("dgCMatrix", Dim = dd, Dimnames = list(vnames,stepnames), x = as.vector(ca), p = as.integer(ia - 1), i = as.integer(ja - 1)) } glmnet/R/glmnet.control.R0000644000176200001440000000736513552673674015066 0ustar liggesusers#' internal glmnet parameters #' #' View and/or change the factory default parameters in glmnet #' #' If called with no arguments, \code{glmnet.control()} returns a list with the #' current settings of these parameters. Any arguments included in the call #' sets those parameters to the new values, and then silently returns. The #' values set are persistent for the duration of the R session. #' #' @param fdev minimum fractional change in deviance for stopping path; factory #' default = 1.0e-5 #' @param devmax maximum fraction of explained deviance for stopping path; #' factory default = 0.999 #' @param eps minimum value of lambda.min.ratio (see glmnet); factory default= #' 1.0e-6 #' @param big large floating point number; factory default = 9.9e35. Inf in #' definition of upper.limit is set to big #' @param mnlam minimum number of path points (lambda values) allowed; factory #' default = 5 #' @param pmin minimum probability for any class. factory default = 1.0e-9. #' Note that this implies a pmax of 1-pmin. #' @param exmx maximum allowed exponent. factory default = 250.0 #' @param prec convergence threshold for multi response bounds adjustment #' solution. factory default = 1.0e-10 #' @param mxit maximum iterations for multiresponse bounds adjustment solution. #' factory default = 100 #' @param itrace If 1 then progress bar is displayed when running \code{glmnet} #' and \code{cv.glmnet}. factory default = 0 #' @param factory If \code{TRUE}, reset all the parameters to the factory #' default; default is \code{FALSE} #' @return A list with named elements as in the argument list #' @author Jerome Friedman, Trevor Hastie\cr Maintainer: Trevor Hastie #' \email{hastie@@stanford.edu} #' @seealso \code{glmnet} #' @keywords models regression #' @examples #' #' glmnet.control(fdev = 0) #continue along path even though not much changes #' glmnet.control() # view current settings #' glmnet.control(factory = TRUE) # reset all the parameters to their default #' #' @export glmnet.control glmnet.control <- function (fdev = 1e-05, devmax = 0.999, eps = 1e-06, big = 9.9e+35, mnlam = 5, pmin = 1e-09, exmx = 250, prec = 1e-10, mxit = 100, itrace = 0, factory = FALSE) { inquiry=!nargs() if (factory) invisible(glmnet.control(fdev = 1e-05, devmax = 0.999, eps = 1e-06, big = 9.9e+35, mnlam = 5, pmin = 1e-09, exmx = 250, prec = 1e-10, mxit = 100, itrace = 0)) else { if (!missing(fdev)) .Fortran("chg_fract_dev", as.double(fdev), PACKAGE = "glmnet") if (!missing(devmax)) .Fortran("chg_dev_max", as.double(devmax), PACKAGE = "glmnet") if (!missing(eps)) .Fortran("chg_min_flmin", as.double(eps), PACKAGE = "glmnet") if (!missing(big)) .Fortran("chg_big", as.double(big), PACKAGE = "glmnet") if (!missing(mnlam)) .Fortran("chg_min_lambdas", as.integer(mnlam), PACKAGE = "glmnet") if (!missing(pmin)) .Fortran("chg_min_null_prob", as.double(pmin), PACKAGE = "glmnet") if (!missing(exmx)) .Fortran("chg_max_exp", as.double(exmx), PACKAGE = "glmnet") if (!missing(prec) | !missing(mxit)) .Fortran("chg_bnorm", as.double(prec), as.integer(mxit), PACKAGE = "glmnet") if (!missing(itrace)) .Fortran("chg_itrace", as.integer(itrace), PACKAGE = "glmnet") value=c(.Fortran("get_int_parms", fdev = double(1), eps = double(1), big = double(1), mnlam = integer(1), devmax = double(1), pmin = double(1), exmx = double(1), itrace = integer(1), PACKAGE = "glmnet"), .Fortran("get_bnorm", prec = double(1), mxit = integer(1), PACKAGE = "glmnet")) if(inquiry)value else invisible(value) } } glmnet/R/lognet.R0000644000176200001440000001017313534022702013354 0ustar liggesuserslognet=function(x,is.sparse,ix,jx,y,weights,offset,alpha,nobs,nvars,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,vnames,maxit,kopt,family){ nc=dim(y) maxit=as.integer(maxit) if(is.null(nc)){ ## Need to construct a y matrix, and include the weights y=as.factor(y) ntab=table(y) minclass=min(ntab) if(minclass<=1)stop("one multinomial or binomial class has 1 or 0 observations; not allowed") if(minclass<8)warning("one multinomial or binomial class has fewer than 8 observations; dangerous ground") classnames=names(ntab) nc=as.integer(length(ntab)) y=diag(nc)[as.numeric(y),] } else{ noo=nc[1] if(noo!=nobs)stop("x and y have different number of rows in call to glmnet",call.=FALSE) nc=as.integer(nc[2]) classnames=colnames(y) } #Check for size limitations maxvars=.Machine$integer.max/(nlam*nc) if(nx>maxvars)stop(paste("Integer overflow; num_classes*num_lambda*pmax should not exceed .Machine$integer.max. Reduce pmax to be below", trunc(maxvars))) if(!missing(weights)) y=y*weights ### check if any rows of y sum to zero, and if so deal with them weights=drop(y%*%rep(1,nc)) o=weights>0 if(!all(o)){ #subset the data y=y[o,] if(is.sparse){ # we have to subset this beast with care x=sparseMatrix(i=jx,p=ix-1,x=x,dims=c(nobs,nvars))[o,,drop=FALSE] ix=as.integer(x@p+1) jx=as.integer(x@i+1) x=as.double(x@x) } else x=x[o,,drop=FALSE] nobs=sum(o) }else o=NULL if(family=="binomial"){ if(nc>2)stop("More than two classes; use multinomial family instead in call to glmnet",call.=FALSE) nc=as.integer(1) # for calling lognet y=y[,c(2,1)]#fortran lognet models the first column; we prefer the second (for 0/1 data) } storage.mode(y)="double" if(is.null(offset)){ offset=y*0 #keeps the shape of y is.offset=FALSE} else{ offset=as.matrix(offset) if(!is.null(o))offset=offset[o,,drop=FALSE]# we might have zero weights do=dim(offset) if(do[[1]]!=nobs)stop("offset should have the same number of values as observations in binomial/multinomial call to glmnet",call.=FALSE) if((do[[2]]==1)&(nc==1))offset=cbind(offset,-offset) if((family=="multinomial")&(do[[2]]!=nc))stop("offset should have same shape as y in multinomial call to glmnet",call.=FALSE) storage.mode(offset)="double" is.offset=TRUE } fit=if(is.sparse) .Fortran("splognet", parm=alpha,nobs,nvars,nc,x,ix,jx,y,offset,jd,vp,cl,ne=ne,nx,nlam,flmin,ulam,thresh,isd,intr,maxit,kopt, lmu=integer(1), a0=double(nlam*nc), ca=double(nx*nlam*nc), ia=integer(nx), nin=integer(nlam), nulldev=double(1), dev=double(nlam), alm=double(nlam), nlp=integer(1), jerr=integer(1),PACKAGE="glmnet" ) else .Fortran("lognet", parm=alpha,nobs,nvars,nc,as.double(x),y,offset,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,maxit,kopt, lmu=integer(1), a0=double(nlam*nc), ca=double(nx*nlam*nc), ia=integer(nx), nin=integer(nlam), nulldev=double(1), dev=double(nlam), alm=double(nlam), nlp=integer(1), jerr=integer(1),PACKAGE="glmnet" ) if(fit$jerr!=0){ errmsg=jerr(fit$jerr,maxit,pmax=nx,family) if(errmsg$fatal)stop(errmsg$msg,call.=FALSE) else warning(errmsg$msg,call.=FALSE) } if(family=="binomial"){ outlist=getcoef(fit,nvars,nx,vnames) # outlist$a0=-outlist$a0#sign flips make 2 target class # outlist$beta=-outlist$beta#sign flips make 2 target class } else outlist=getcoef.multinomial(fit,nvars,nx,vnames,nc,classnames) dev=fit$dev[seq(fit$lmu)] outlist=c(outlist,list(dev.ratio=dev,nulldev=fit$nulldev,npasses=fit$nlp,jerr=fit$jerr,offset=is.offset,classnames=classnames)) if(family=="multinomial"){ if(kopt==2)grouped=TRUE else grouped =FALSE outlist$grouped=grouped } class(outlist)=switch(family, "binomial"="lognet", "multinomial"="multnet" ) outlist } glmnet/R/plot.multnet.R0000644000176200001440000000157513552673674014563 0ustar liggesusers#' @method plot multnet #' @rdname plot.glmnet #' @export plot.multnet=function(x, xvar=c("norm","lambda","dev"),label=FALSE,type.coef=c("coef","2norm"),...){ xvar=match.arg(xvar) type.coef=match.arg(type.coef) beta=x$beta if(xvar=="norm"){ cnorm1=function(beta){ which=nonzeroCoef(beta) beta=as.matrix(beta[which,]) apply(abs(beta),2,sum) } norm=apply(sapply(x$beta,cnorm1),1,sum) } else norm = NULL dfmat=x$dfmat if(type.coef=="coef"){ ncl=nrow(dfmat) clnames=rownames(dfmat) for( i in seq(ncl)){ plotCoef(beta[[i]],norm,x$lambda,dfmat[i,],x$dev.ratio,label=label,xvar=xvar,ylab=paste("Coefficients: Response",clnames[i]),...) } } else { dfseq=round(apply(dfmat,2,mean),1) plotCoef(coefnorm(beta,2),norm,x$lambda,dfseq,x$dev.ratio,label=label,xvar=xvar,ylab="Coefficient 2Norms",...) } } glmnet/R/cv.coxnet.R0000644000176200001440000000231713534022702013774 0ustar liggesuserscv.coxnet <- function(predmat,y,type.measure,weights,foldid,grouped){ ## Note, all the work got done in buildPredmat.coxnetlist for deviance; a special case devtrue=type.measure=="deviance" nfolds = max(foldid) if ((length(weights)/nfolds < 10) && !grouped) { warning("Option grouped=TRUE enforced for cv.coxnet, since < 10 observations per fold", call. = FALSE) } if(devtrue){ cvraw=attr(predmat,"cvraw") status = y[, "status"] N = nfolds - apply(is.na(cvraw), 2, sum) weights = as.vector(tapply(weights * status, foldid, sum)) cvraw = cvraw/weights } else { nlambda=ncol(predmat) nlams=rep(nlambda,nfolds) cvraw = matrix(NA, nfolds, nlambda) good = matrix(0, nfolds, nlambda) for (i in seq(nfolds)) { good[i, seq(nlams[i])] = 1 which = foldid == i for (j in seq(nlams[i])) { cvraw[i, j] = Cindex(predmat[which,j],y[which, ], weights[which]) } } N = apply(good, 2, sum) weights = tapply(weights, foldid, sum) } list(cvraw=cvraw,weights=weights,N=N,type.measure=type.measure,grouped=FALSE) } glmnet/R/blend.relaxed.R0000644000176200001440000000221213534022702014566 0ustar liggesusersblend.relaxed=function(fit,gamma,extend=TRUE,epsilon=0.00001){ ### ###gamma must be a single value if(gamma==1){ class(fit)=class(fit)[-1] #drop the relaxed status fit$relaxed=NULL return(fit) } gamma=max(gamma,epsilon) ### We do the above so that even of gamma=0, and extend=TRUE, we fill in the coefficients ### First see which if any of glmnet coefficients are missing on relaxed which=match(fit$lambda,fit$relaxed$lambda,0) if(extend)which[which==0]=max(which) beta=fit$beta a0=fit$a0 fitr=fit$relaxed betar=fitr$beta a0r=fitr$a0 islistbeta=is.list(beta) if(!islistbeta){ fitr$beta=gamma*beta[,which>0] +(1-gamma)*betar[,which] fitr$a0=gamma*a0[which>0]+(1-gamma)*a0r[which] } else{ ngroups=length(beta) for(i in 1:ngroups) beta[[i]]=gamma*beta[[i]][,which>0]+(1-gamma)*betar[[i]][,which] fitr$beta=beta fitr$a0=gamma*a0[,which>0]+(1-gamma)*a0r[,which] } fitr$dev.ratio=gamma*fit$dev.ratio[which>0]+(1-gamma)*fitr$dev.ratio[which] fitr$df=fit$df[which>0] fitr$lambda=fit$lambda[which>0] fitr } glmnet/R/getcoef.multinomial.R0000644000176200001440000000242313534022702016030 0ustar liggesusersgetcoef.multinomial=function(fit,nvars,nx,vnames,nc,classnames,center.intercept=TRUE){ lmu=fit$lmu nin=fit$nin[seq(lmu)] ninmax=max(nin) lam=fit$alm[seq(lmu)] stepnames=paste("s",seq(lmu)-1,sep="") beta.list=as.list(seq(nc)) names(beta.list)=classnames a0=matrix(fit$a0[seq(lmu*nc)],nc,lmu,dimnames=list(classnames,stepnames)) if(center.intercept){ a0=scale(a0,TRUE,FALSE) attr(a0,"scaled:center")=NULL } dfmat=a0 dd=c(nvars,lmu) if(ninmax>0){ ca=array(fit$ca[seq(nx*lmu*nc)],c(nx,nc,lmu))[seq(ninmax),,,drop=FALSE] ja=fit$ia[seq(ninmax)]#confusing but too hard to change oja=order(ja) ja=rep(ja[oja],lmu) ia=cumsum(c(1,rep(ninmax,lmu))) df=apply(abs(ca)>0,c(1,3),any) df=apply(df,2,sum) for(k in seq(nc)){ cak=ca[oja,k, ,drop=FALSE] dfmat[k,]=apply(abs(cak)>0,3,sum) beta=new("dgCMatrix",Dim=dd,Dimnames=list(vnames,stepnames),x=as.vector(cak),p=as.integer(ia-1),i=as.integer(ja-1)) beta.list[[k]]=drop0(beta) } } else{ for (k in seq(nc)) { dfmat[k, ] = rep(0,lmu) beta.list[[k]] = zeromat(nvars,lmu,vnames,stepnames) } } list(a0=a0,beta=beta.list,dfmat=dfmat,df=df,dim=dd,lambda=lam) } glmnet/R/jerr.R0000644000176200001440000000115613534022702013027 0ustar liggesusersjerr=function(n,maxit,pmax,family){ if(n==0) list(n=0,fatal=FALSE,msg="") else { errlist=switch(family, "gaussian"=jerr.elnet(n,maxit,pmax), "binomial"=jerr.lognet(n,maxit,pmax), "multinomial"=jerr.lognet(n,maxit,pmax), "poisson"=jerr.fishnet(n,maxit,pmax), "cox"=jerr.coxnet(n,maxit,pmax), "mrelnet"=jerr.mrelnet(n,maxit,pmax) ) names(errlist)=c("n","fatal","msg") errlist$msg=paste("from glmnet Fortran code (error code ",n, "); ",errlist$msg,sep="") errlist } } glmnet/R/elnet.R0000644000176200001440000000347313534022702013200 0ustar liggesuserselnet=function(x,is.sparse,ix,jx,y,weights,offset,type.gaussian=c("covariance","naive"),alpha,nobs,nvars,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,vnames,maxit){ maxit=as.integer(maxit) weights=as.double(weights) type.gaussian=match.arg(type.gaussian) ka=as.integer(switch(type.gaussian, covariance=1, naive=2, )) storage.mode(y)="double" if(is.null(offset)){ is.offset=FALSE} else{ storage.mode(offset)="double" is.offset=TRUE y=y-offset } ### compute the null deviance ybar=weighted.mean(y,weights) nulldev=sum(weights* (y-ybar)^2) if(nulldev==0)stop("y is constant; gaussian glmnet fails at standardization step") fit=if(is.sparse).Fortran("spelnet", ka,parm=alpha,nobs,nvars,x,ix,jx,y,weights,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,maxit, lmu=integer(1), a0=double(nlam), ca=double(nx*nlam), ia=integer(nx), nin=integer(nlam), rsq=double(nlam), alm=double(nlam), nlp=integer(1), jerr=integer(1),PACKAGE="glmnet" ) else .Fortran("elnet", ka,parm=alpha,nobs,nvars,as.double(x),y,weights,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,maxit, lmu=integer(1), a0=double(nlam), ca=double(nx*nlam), ia=integer(nx), nin=integer(nlam), rsq=double(nlam), alm=double(nlam), nlp=integer(1), jerr=integer(1),PACKAGE="glmnet" ) if(fit$jerr!=0){ errmsg=jerr(fit$jerr,maxit,pmax=nx,family="gaussian") if(errmsg$fatal)stop(errmsg$msg,call.=FALSE) else warning(errmsg$msg,call.=FALSE) } outlist=getcoef(fit,nvars,nx,vnames) dev=fit$rsq[seq(fit$lmu)] outlist=c(outlist,list(dev.ratio=dev,nulldev=nulldev,npasses=fit$nlp,jerr=fit$jerr,offset=is.offset)) class(outlist)="elnet" outlist } glmnet/R/predict.fishnet.R0000644000176200001440000000043213552673674015176 0ustar liggesusers#' @method predict fishnet #' @export predict.fishnet=function(object,newx,s=NULL,type=c("link","response","coefficients","nonzero"),exact=FALSE,newoffset,...){ type=match.arg(type) nfit=NextMethod("predict") switch(type, response=exp(nfit), nfit ) } glmnet/R/predict.cv.glmnet.R0000644000176200001440000000551313555633674015437 0ustar liggesusers## CHECK WITH TREVOR: why alias here for predict.cv.relaxed as well as in plot.cv.relaxed? ## #' make predictions from a "cv.glmnet" object. #' #' This function makes predictions from a cross-validated glmnet model, using #' the stored \code{"glmnet.fit"} object, and the optimal value chosen for #' \code{lambda} (and \code{gamma} for a 'relaxed' fit. #' #' This function makes it easier to use the results of cross-validation to make #' a prediction. #' #' @aliases coef.cv.glmnet coef.cv.relaxed predict.cv.glmnet #' @param object Fitted \code{"cv.glmnet"} or \code{"cv.relaxed"} object. #' @param newx Matrix of new values for \code{x} at which predictions are to be #' made. Must be a matrix; can be sparse as in \code{Matrix} package. See #' documentation for \code{predict.glmnet}. #' @param s Value(s) of the penalty parameter \code{lambda} at which #' predictions are required. Default is the value \code{s="lambda.1se"} stored #' on the CV \code{object}. Alternatively \code{s="lambda.min"} can be used. If #' \code{s} is numeric, it is taken as the value(s) of \code{lambda} to be #' used. (For historical reasons we use the symbol 's' rather than 'lambda' to #' reference this parameter) #' @param \dots Not used. Other arguments to predict. #' @return The object returned depends on the \dots{} argument which is passed #' on to the \code{predict} method for \code{glmnet} objects. #' @author Jerome Friedman, Trevor Hastie and Rob Tibshirani\cr Maintainer: #' Trevor Hastie #' @seealso \code{glmnet}, and \code{print}, and \code{coef} methods, and #' \code{cv.glmnet}. #' @references Friedman, J., Hastie, T. and Tibshirani, R. (2008) #' \emph{Regularization Paths for Generalized Linear Models via Coordinate #' Descent, Journal of Statistical Software, Vol. 33, Issue 1, Feb 2010}\cr #' \url{https://www.jstatsoft.org/v33/i01/} #' \url{https://arxiv.org/abs/1707.08692}\cr Hastie, T., Tibshirani, Robert, #' Tibshirani, Ryan (2019) \emph{Extended Comparisons of Best Subset Selection, #' Forward Stepwise Selection, and the Lasso} #' @keywords models regression #' @examples #' #' x = matrix(rnorm(100 * 20), 100, 20) #' y = rnorm(100) #' cv.fit = cv.glmnet(x, y) #' predict(cv.fit, newx = x[1:5, ]) #' coef(cv.fit) #' coef(cv.fit, s = "lambda.min") #' predict(cv.fit, newx = x[1:5, ], s = c(0.001, 0.002)) #' cv.fitr = cv.glmnet(x, y, relax = TRUE) #' predict(cv.fit, newx = x[1:5, ]) #' coef(cv.fit) #' coef(cv.fit, s = "lambda.min", gamma = "gamma.min") #' predict(cv.fit, newx = x[1:5, ], s = c(0.001, 0.002), gamma = "gamma.min") #' #' @method predict cv.glmnet #' @export predict.cv.glmnet=function(object,newx,s=c("lambda.1se","lambda.min"),...){ if(is.numeric(s))lambda=s else if(is.character(s)){ s=match.arg(s) lambda=object[[s]] } else stop("Invalid form for s") predict(object$glmnet.fit,newx,s=lambda,...) } glmnet/R/glmnet.measures.R0000644000176200001440000000231513552673674015220 0ustar liggesusers#' Display the names of the measures used in CV for different "glmnet" families #' #' Produces a list of names of measures #' #' Try it and see. A very simple function to provide information #' #' @param family If a "glmnet" family is supplied, a list of the names of #' measures available for that family are produced. Default is "all", in which #' case the names of measures for all families are produced. #' @author Trevor Hastie\cr Maintainer: Trevor Hastie #' \email{hastie@@stanford.edu} #' @seealso \code{cv.glmnet} and \code{assess.glmnet}. #' @keywords models #' @export glmnet.measures glmnet.measures=function(family=c("all","gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian")){ family=match.arg(family) type.measures = c("mse", "deviance", "class", "auc", "mae", "C") family.ch = list( gaussian = c(1, 5), binomial = c(2, 3, 4, 1, 5), poisson = c(2, 1, 5), cox = c(2, 6), multinomial = c(2, 3, 1, 5), mgaussian = c(1, 5)) if(family=="all")lapply(family.ch,function(x)type.measures[x]) else type.measures[family.ch[[family]]] } glmnet/R/lambda.interp.R0000644000176200001440000000221013574045726014614 0ustar liggesuserslambda.interp=function(lambda,s){ ### lambda is the index sequence that is produced by the model ### s is the new vector at which evaluations are required. ### the value is a vector of left and right indices, and a vector of fractions. ### the new values are interpolated bewteen the two using the fraction ### Note: lambda decreases. you take: ### sfrac*left+(1-sfrac*right) if(length(lambda)==1){# degenerate case of only one lambda nums=length(s) left=rep(1,nums) right=left sfrac=rep(1,nums) } else{ ## s[s > max(lambda)] = max(lambda) ## s[s < min(lambda)] = min(lambda) k=length(lambda) sfrac <- (lambda[1]-s)/(lambda[1] - lambda[k]) lambda <- (lambda[1] - lambda)/(lambda[1] - lambda[k]) sfrac[sfrac < min(lambda)] <- min(lambda) sfrac[sfrac > max(lambda)] <- max(lambda) coord <- approx(lambda, seq(lambda), sfrac)$y left <- floor(coord) right <- ceiling(coord) sfrac=(sfrac-lambda[right])/(lambda[left] - lambda[right]) sfrac[left==right]=1 sfrac[abs(lambda[left]-lambda[right])<.Machine$double.eps]=1 } list(left=left,right=right,frac=sfrac) } glmnet/R/getOptcv.relaxed.R0000644000176200001440000000205313534022702015300 0ustar liggesusersgetOptcv.relaxed <- function (statlist,cvname,gamma) { cvm=lapply(statlist,"[[","cvm") nlams=sapply(cvm,length) cvm=unlist(cvm) lambdas=unlist(lapply(statlist,"[[","lambda")) cvsd=unlist(lapply(statlist,"[[","cvsd")) nzero=unlist(lapply(statlist,"[[","nzero")) gammas=rep(gamma,nlams) names(lambdas)=NULL names(gammas)=NULL if(match(cvname,c("AUC","C-index"),0))cvm=-cvm cvmin = min(cvm, na.rm = TRUE) idmin = cvm <= cvmin which=order(lambdas[idmin],gammas[idmin],decreasing=TRUE)[1] lambda.min = lambdas[idmin][which] gamma.min= gammas[idmin][which] nzero.min=nzero[idmin][which] idmin=seq(along=lambdas)[idmin][which] semin = (cvm + cvsd)[idmin] idmin = cvm <= semin which=order(lambdas[idmin],gammas[idmin],decreasing=TRUE)[1] lambda.1se = lambdas[idmin][which] gamma.1se= gammas[idmin][which] nzero.1se=nzero[idmin][which] list(lambda.min = lambda.min, lambda.1se = lambda.1se, gamma.min=gamma.min, gamma.1se=gamma.1se,nzero.min=nzero.min,nzero.1se=nzero.1se) } glmnet/R/family.glmnet.R0000644000176200001440000000072713552673674014662 0ustar liggesusers#' @method family glmnet #' @export family.glmnet=function(object,...){ families=c(elnet = "gaussian", lognet = "binomial", fishnet = "poisson", multnet = "multinomial", coxnet = "cox", mrelnet = "mgaussian") cl=class(object)[1] families[cl] } #' @method family relaxed #' @export family.relaxed=function(object,...)family(object$relaxed) #' @method family cv.glmnet #' @export family.cv.glmnet=function(object,...)family(object$glmnet.fit) glmnet/R/jerr.lognet.R0000644000176200001440000000100013534022702014302 0ustar liggesusersjerr.lognet=function(n,maxit,pmax){ outlist=jerr.elnet(n,maxit,pmax) if(n< -20000)outlist$msg=paste("Max(p(1-p),1.0e-6 at ",-n-20000,"th value of lambda; solutions for larger values of lambda returned") if(outlist$msg!="Unknown error")return(outlist) if((8000 1.0 - 1.0e-5") else msg="Unknown error" list(n=n,fatal=TRUE,msg=msg) } glmnet/R/cv.relaxed.raw.R0000644000176200001440000001024013550721067014712 0ustar liggesuserscv.relaxed.raw <- function (x, y, weights, offset, lambda, type.measure, nfolds, foldid, alignment,grouped, keep, parallel, trace.it, glmnet.call, cv.call, gamma, ...) { ###This next function may be redundant relaxglmnet=function(x,...){ fit=glmnet(x,...) relax.glmnet(fit,x,...,check.args=FALSE) } gamma=sort(checkgamma.relax(gamma)) ### The next bit is to make sure we always include lasso in CV, as well as the gamma series lengamma=length(gamma) if(max(gamma)<1.0){ gamma=c(gamma,1.0) } glmnet.call$relax=TRUE if (trace.it) cat("Training\n") glmnet.object = relaxglmnet(x=x, y=y, weights = weights, offset = offset, lambda = lambda, ...) glmnet.object$call = glmnet.call subclass=class(glmnet.object)[[2]]# it is of class c("relaxed","subtype","glmnet") type.measure=cvtype(type.measure,subclass) is.offset = glmnet.object$offset ###Next line is commented out so each call generates its own lambda sequence # lambda=glmnet.object$lambda if (inherits(glmnet.object, "multnet") && !glmnet.object$grouped) { nz = predict(glmnet.object, type = "nonzero") nz = sapply(nz, function(x) sapply(x, length)) nz = ceiling(apply(nz, 1, median)) } else nz = sapply(predict(glmnet.object, type = "nonzero"), length) outlist = as.list(seq(nfolds)) N=nrow(x) if (parallel) { # if (parallel && require(foreach)) { outlist = foreach(i = seq(nfolds), .packages = c("glmnet")) %dopar% { which = foldid == i # if (is.matrix(y)) if (length(dim(y))>1) y_sub = y[!which, ] else y_sub = y[!which] if (is.offset) offset_sub = as.matrix(offset)[!which, ] else offset_sub = NULL relaxglmnet(x=x[!which, , drop = FALSE], y=y_sub, lambda = lambda, offset = offset_sub, weights = weights[!which], ...) } } else { for (i in seq(nfolds)) { if (trace.it) cat(sprintf("Fold: %d/%d\n", i, nfolds)) which = foldid == i if (is.matrix(y)) y_sub = y[!which, ] else y_sub = y[!which] if (is.offset) offset_sub = as.matrix(offset)[!which, ] else offset_sub = NULL outlist[[i]] = relaxglmnet(x=x[!which, , drop = FALSE], y=y_sub, lambda = lambda, offset = offset_sub, weights = weights[!which], ...) } } lambda = glmnet.object$lambda class(outlist)=paste0(subclass,"list") predmatlist=as.list(gamma) names(predmatlist)=paste("g",gamma,sep=":") outstuff=cvstufflist=predmatlist ### Even though the following is innefficient, It makes the code more modular fun = paste("cv", subclass, sep = ".") for(i in seq(along=gamma)){ predmatlist[[i]]=buildPredmat(outlist, lambda=lambda,x=x,offset=offset,foldid=foldid,alignment=alignment, y=y,weights=weights,grouped=grouped, gamma=gamma[i] ) cvstufflist[[i]]=do.call(fun, list(predmatlist[[i]],y,type.measure,weights,foldid,grouped)) } grouped=all(sapply(cvstufflist,"[[","grouped")) if ((N/nfolds < 3) && grouped) { warning("Option grouped=FALSE enforced in cv.relaxed, since < 3 observations per fold", call. = FALSE) grouped = FALSE } for(i in seq(along=gamma)) outstuff[[i]]=cvstats(cvstufflist[[i]],foldid,nfolds,lambda,nz,grouped) cvn = cvstufflist[[1]]$type.measure cvname=names(cvn);names(cvname)=cvn# to be compatible with earlier version; silly, I know out=outstuff[[length(gamma)]] # lasso stats out = c(out,list(call=cv.call,name = cvname, glmnet.fit = glmnet.object)) if(lengamma #' @references Friedman, J., Hastie, T. and Tibshirani, R. (2008) #' \emph{Regularization Paths for Generalized Linear Models via Coordinate #' Descent}, \url{https://web.stanford.edu/~hastie/Papers/glmnet.pdf}\cr #' \emph{Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010}\cr #' \url{https://www.jstatsoft.org/v33/i01/}\cr Simon, N., Friedman, J., Hastie, #' T., Tibshirani, R. (2011) \emph{Regularization Paths for Cox's Proportional #' Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. #' 39(5) 1-13}\cr \url{https://www.jstatsoft.org/v39/i05/}\cr Tibshirani, #' Robert., Bien, J., Friedman, J.,Hastie, T.,Simon, N.,Taylor, J. and #' Tibshirani, Ryan. (2012) \emph{Strong Rules for Discarding Predictors in #' Lasso-type Problems, JRSSB, vol 74},\cr #' \url{https://statweb.stanford.edu/~tibs/ftp/strong.pdf}\cr \emph{Stanford #' Statistics Technical Report}\cr \emph{Glmnet Vignette} #' \url{https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html} #' @keywords models regression package #' @examples #' #' x = matrix(rnorm(100 * 20), 100, 20) #' y = rnorm(100) #' g2 = sample(1:2, 100, replace = TRUE) #' g4 = sample(1:4, 100, replace = TRUE) #' fit1 = glmnet(x, y) #' predict(fit1, newx = x[1:5, ], s = c(0.01, 0.005)) #' predict(fit1, type = "coef") #' plot(fit1, xvar = "lambda") #' fit2 = glmnet(x, g2, family = "binomial") #' predict(fit2, type = "response", newx = x[2:5, ]) #' predict(fit2, type = "nonzero") #' fit3 = glmnet(x, g4, family = "multinomial") #' predict(fit3, newx = x[1:3, ], type = "response", s = 0.01) #' NULL glmnet/R/jerr.mrelnet.R0000644000176200001440000000033313534022702014470 0ustar liggesusersjerr.mrelnet=function(n,maxit,pmax){ if(n==90000){ msg=paste("Newton stepping for bounded multivariate response did not converge") list(n=n,fatal=FALSE,msg=msg) } else jerr.elnet(n,maxit,pmax) } glmnet/R/cv.glmnet.R0000644000176200001440000003107013555633674014003 0ustar liggesusers#' Cross-validation for glmnet #' #' Does k-fold cross-validation for glmnet, produces a plot, and returns a #' value for \code{lambda} (and \code{gamma} if \code{relax=TRUE}) #' #' The function runs \code{glmnet} \code{nfolds}+1 times; the first to get the #' \code{lambda} sequence, and then the remainder to compute the fit with each #' of the folds omitted. The error is accumulated, and the average error and #' standard deviation over the folds is computed. Note that \code{cv.glmnet} #' does NOT search for values for \code{alpha}. A specific value should be #' supplied, else \code{alpha=1} is assumed by default. If users would like to #' cross-validate \code{alpha} as well, they should call \code{cv.glmnet} with #' a pre-computed vector \code{foldid}, and then use this same fold vector in #' separate calls to \code{cv.glmnet} with different values of \code{alpha}. #' Note also that the results of \code{cv.glmnet} are random, since the folds #' are selected at random. Users can reduce this randomness by running #' \code{cv.glmnet} many times, and averaging the error curves. #' #' If \code{relax=TRUE} then the values of \code{gamma} are used to mix the #' fits. If \eqn{\eta} is the fit for lasso/elastic net, and \eqn{\eta_R} is #' the relaxed fit (with unpenalized coefficients), then a relaxed fit mixed by #' \eqn{\gamma} is \deqn{\eta(\gamma)=(1-\gamma)\eta_R+\gamma\eta}. There is #' practically no extra cost for having a lot of values for \code{gamma}. #' However, 5 seems sufficient for most purposes. CV then selects both #' \code{gamma} and \code{lambda}. #' #' @param x \code{x} matrix as in \code{glmnet}. #' @param y response \code{y} as in \code{glmnet}. #' @param weights Observation weights; defaults to 1 per observation #' @param offset Offset vector (matrix) as in \code{glmnet} #' @param lambda Optional user-supplied lambda sequence; default is #' \code{NULL}, and \code{glmnet} chooses its own sequence #' @param type.measure loss to use for cross-validation. Currently five #' options, not all available for all models. The default is #' \code{type.measure="deviance"}, which uses squared-error for gaussian models #' (a.k.a \code{type.measure="mse"} there), deviance for logistic and poisson #' regression, and partial-likelihood for the Cox model. #' \code{type.measure="class"} applies to binomial and multinomial logistic #' regression only, and gives misclassification error. #' \code{type.measure="auc"} is for two-class logistic regression only, and #' gives area under the ROC curve. \code{type.measure="mse"} or #' \code{type.measure="mae"} (mean absolute error) can be used by all models #' except the \code{"cox"}; they measure the deviation from the fitted mean to #' the response. #' \code{type.measure="C"} is Harrel's concordance measure, only available for \code{cox} models. #' @param nfolds number of folds - default is 10. Although \code{nfolds} can be #' as large as the sample size (leave-one-out CV), it is not recommended for #' large datasets. Smallest value allowable is \code{nfolds=3} #' @param foldid an optional vector of values between 1 and \code{nfold} #' identifying what fold each observation is in. If supplied, \code{nfold} can #' be missing. #' @param alignment This is an experimental argument, designed to fix the #' problems users were having with CV, with possible values \code{"lambda"} #' (the default) else \code{"fraction"}. With \code{"lambda"} the \code{lambda} #' values from the master fit (on all the data) are used to line up the #' predictions from each of the folds. In some cases this can give strange #' values, since the effective \code{lambda} values in each fold could be quite #' different. With \code{"fraction"} we line up the predictions in each fold #' according to the fraction of progress along the regularization. If in the #' call a \code{lambda} argument is also provided, \code{alignment="fraction"} #' is ignored (with a warning). #' @param grouped This is an experimental argument, with default \code{TRUE}, #' and can be ignored by most users. For all models except the \code{"cox"}, #' this refers to computing \code{nfolds} separate statistics, and then using #' their mean and estimated standard error to describe the CV curve. If #' \code{grouped=FALSE}, an error matrix is built up at the observation level #' from the predictions from the \code{nfold} fits, and then summarized (does #' not apply to \code{type.measure="auc"}). For the \code{"cox"} family, #' \code{grouped=TRUE} obtains the CV partial likelihood for the Kth fold by #' \emph{subtraction}; by subtracting the log partial likelihood evaluated on #' the full dataset from that evaluated on the on the (K-1)/K dataset. This #' makes more efficient use of risk sets. With \code{grouped=FALSE} the log #' partial likelihood is computed only on the Kth fold #' @param keep If \code{keep=TRUE}, a \emph{prevalidated} array is returned #' containing fitted values for each observation and each value of #' \code{lambda}. This means these fits are computed with this observation and #' the rest of its fold omitted. The \code{folid} vector is also returned. #' Default is keep=FALSE. If \code{relax=TRUE}, then a list of such arrays is #' returned, one for each value of 'gamma'. Note: if the value 'gamma=1' is #' omitted, this case is included in the list since it corresponds to the #' original 'glmnet' fit. #' @param parallel If \code{TRUE}, use parallel \code{foreach} to fit each #' fold. Must register parallel before hand, such as \code{doMC} or others. #' See the example below. #' @param gamma The values of the parameter for mixing the relaxed fit with the #' regularized fit, between 0 and 1; default is \code{gamma = c(0, 0.25, 0.5, #' 0.75, 1)} #' @param relax If \code{TRUE}, then CV is done with respect to the mixing #' parameter \code{gamma} as well as \code{lambda}. Default is #' \code{relax=FALSE} #' @param trace.it If \code{trace.it=1}, then progress bars are displayed; #' useful for big models that take a long time to fit. Limited tracing if #' \code{parallel=TRUE} #' @param \dots Other arguments that can be passed to \code{glmnet} #' @return an object of class \code{"cv.glmnet"} is returned, which is a list #' with the ingredients of the cross-validation fit. If the object was created #' with \code{relax=TRUE} then this class has a prefix class of #' \code{"cv.relaxed"}. \item{lambda}{the values of \code{lambda} used in the #' fits.} \item{cvm}{The mean cross-validated error - a vector of length #' \code{length(lambda)}.} \item{cvsd}{estimate of standard error of #' \code{cvm}.} \item{cvup}{upper curve = \code{cvm+cvsd}.} \item{cvlo}{lower #' curve = \code{cvm-cvsd}.} \item{nzero}{number of non-zero coefficients at #' each \code{lambda}.} \item{name}{a text string indicating type of measure #' (for plotting purposes).} \item{glmnet.fit}{a fitted glmnet object for the #' full data.} \item{lambda.min}{value of \code{lambda} that gives minimum #' \code{cvm}.} \item{lambda.1se}{largest value of \code{lambda} such that #' error is within 1 standard error of the minimum.} \item{fit.preval}{if #' \code{keep=TRUE}, this is the array of prevalidated fits. Some entries can #' be \code{NA}, if that and subsequent values of \code{lambda} are not reached #' for that fold} \item{foldid}{if \code{keep=TRUE}, the fold assignments used} #' \item{relaxed}{if \code{relax=TRUE}, this additional item has the CV info #' for each of the mixed fits. In particular it also selects \code{lambda, #' gamma} pairs corresponding to the 1SE rule, as well as the minimum error.} #' @author Jerome Friedman, Trevor Hastie and Rob Tibshirani\cr Noah Simon #' helped develop the 'coxnet' function.\cr Jeffrey Wong and B. Narasimhan #' helped with the parallel option\cr Maintainer: Trevor Hastie #' \email{hastie@@stanford.edu} #' @seealso \code{glmnet} and \code{plot}, \code{predict}, and \code{coef} #' methods for \code{"cv.glmnet"} and \code{"cv.relaxed"} objects. #' @references Friedman, J., Hastie, T. and Tibshirani, R. (2008) #' \emph{Regularization Paths for Generalized Linear Models via Coordinate #' Descent}, \url{https://web.stanford.edu/~hastie/Papers/glmnet.pdf}\cr #' \emph{Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010}\cr #' \url{https://www.jstatsoft.org/v33/i01/}\cr Simon, N., Friedman, J., Hastie, #' T., Tibshirani, R. (2011) \emph{Regularization Paths for Cox's Proportional #' Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. #' 39(5) 1-13}\cr \url{https://www.jstatsoft.org/v39/i05/} #' @keywords models regression #' @examples #' #' set.seed(1010) #' n = 1000 #' p = 100 #' nzc = trunc(p/10) #' x = matrix(rnorm(n * p), n, p) #' beta = rnorm(nzc) #' fx = x[, seq(nzc)] %*% beta #' eps = rnorm(n) * 5 #' y = drop(fx + eps) #' px = exp(fx) #' px = px/(1 + px) #' ly = rbinom(n = length(px), prob = px, size = 1) #' set.seed(1011) #' cvob1 = cv.glmnet(x, y) #' plot(cvob1) #' coef(cvob1) #' predict(cvob1, newx = x[1:5, ], s = "lambda.min") #' title("Gaussian Family", line = 2.5) #' set.seed(1011) #' cvob1a = cv.glmnet(x, y, type.measure = "mae") #' plot(cvob1a) #' title("Gaussian Family", line = 2.5) #' set.seed(1011) #' par(mfrow = c(2, 2), mar = c(4.5, 4.5, 4, 1)) #' cvob2 = cv.glmnet(x, ly, family = "binomial") #' plot(cvob2) #' title("Binomial Family", line = 2.5) #' frame() #' set.seed(1011) #' cvob3 = cv.glmnet(x, ly, family = "binomial", type.measure = "class") #' plot(cvob3) #' title("Binomial Family", line = 2.5) #' \dontrun{ #' cvob1r = cv.glmnet(x, y, relax = TRUE) #' plot(cvob1r) #' predict(cvob1r, newx = x[, 1:5]) #' set.seed(1011) #' cvob3a = cv.glmnet(x, ly, family = "binomial", type.measure = "auc") #' plot(cvob3a) #' title("Binomial Family", line = 2.5) #' set.seed(1011) #' mu = exp(fx/10) #' y = rpois(n, mu) #' cvob4 = cv.glmnet(x, y, family = "poisson") #' plot(cvob4) #' title("Poisson Family", line = 2.5) #' #' # Multinomial #' n = 500 #' p = 30 #' nzc = trunc(p/10) #' x = matrix(rnorm(n * p), n, p) #' beta3 = matrix(rnorm(30), 10, 3) #' beta3 = rbind(beta3, matrix(0, p - 10, 3)) #' f3 = x %*% beta3 #' p3 = exp(f3) #' p3 = p3/apply(p3, 1, sum) #' g3 = glmnet:::rmult(p3) #' set.seed(10101) #' cvfit = cv.glmnet(x, g3, family = "multinomial") #' plot(cvfit) #' title("Multinomial Family", line = 2.5) #' # Cox #' beta = rnorm(nzc) #' fx = x[, seq(nzc)] %*% beta/3 #' hx = exp(fx) #' ty = rexp(n, hx) #' tcens = rbinom(n = n, prob = 0.3, size = 1) # censoring indicator #' y = cbind(time = ty, status = 1 - tcens) # y=Surv(ty,1-tcens) with library(survival) #' foldid = sample(rep(seq(10), length = n)) #' fit1_cv = cv.glmnet(x, y, family = "cox", foldid = foldid) #' plot(fit1_cv) #' title("Cox Family", line = 2.5) #' # Parallel #' require(doMC) #' registerDoMC(cores = 4) #' x = matrix(rnorm(1e+05 * 100), 1e+05, 100) #' y = rnorm(1e+05) #' system.time(cv.glmnet(x, y)) #' system.time(cv.glmnet(x, y, parallel = TRUE)) #' } #' #' @export cv.glmnet cv.glmnet <- function (x, y, weights=NULL, offset = NULL, lambda = NULL, type.measure = c("default","mse", "deviance", "class", "auc", "mae","C"), nfolds = 10, foldid=NULL, alignment=c("lambda","fraction"),grouped = TRUE, keep = FALSE, parallel = FALSE, gamma=c(0,.25,.5,.75,1),relax=FALSE,trace.it=0, ...) { type.measure = match.arg(type.measure) alignment=match.arg(alignment) if (!is.null(lambda) && length(lambda) < 2) stop("Need more than one value of lambda for cv.glmnet") if (!is.null(lambda) && alignment=="fraction"){ warning("fraction of path alignment not available if lambda given as argument; switched to alignment=`lambda`") alignment="lambda" } N = nrow(x) if (is.null(weights)) weights = rep(1, N) else weights = as.double(weights) y = drop(y) cv.call=glmnet.call = match.call(expand.dots = TRUE) which = match(c("type.measure", "nfolds", "foldid", "grouped", "keep"), names(glmnet.call), FALSE) if (any(which)) glmnet.call = glmnet.call[-which] glmnet.call[[1]] = as.name("glmnet") if(glmnet.control()$itrace)trace.it=1 else{ if(trace.it){ glmnet.control(itrace=1) on.exit(glmnet.control(itrace=0)) } } if (is.null(foldid)) foldid = sample(rep(seq(nfolds), length = N)) else nfolds = max(foldid) if (nfolds < 3) stop("nfolds must be bigger than 3; nfolds=10 recommended") ### Now we switch depending on relax if(relax) cv.relaxed.raw(x,y,weights,offset,lambda,type.measure,nfolds,foldid, alignment,grouped,keep,parallel,trace.it,glmnet.call,cv.call,gamma,...) else cv.glmnet.raw(x,y,weights,offset,lambda,type.measure,nfolds,foldid, alignment,grouped,keep,parallel,trace.it,glmnet.call,cv.call,...) } glmnet/R/coxgrad.R0000644000176200001440000000636513553704056013535 0ustar liggesusers#' compute gradient for cox model #' #' Compute the gradient of the partial likelihood at a particular fit #' #' Compute a gradient vector at the fitted vector for the log partial likelihood. #' This is like a residual vector, and useful for manual screening of predictors for \code{glmnet} #' in applications where \code{p} is very large (as in GWAS). Uses the Breslow approach to ties #' #' @aliases coxgrad #' @param f fit vector #' @param time time vector (can have ties) #' @param d death/censoring indicator 1/0 #' @param w observation weights (default equal) #' @param eps (default 0.00001) Breaks ties between death and censoring by making death times \code{eps} earlier #' @return a single gradient vector the same length as \code{f} #' @author Trevor Hastie\cr Maintainer: Trevor Hastie #' @seealso \code{coxnet.deviance} #' @keywords Cox model #' #' @export coxgrad coxgrad=function(f,time,d,w,eps=0.00001){ ### f is fitted function from glmnet at a particular lambda ### time is death or censoring time ### d is death indicator; d=0 means censored, d=1 means death ### w is a weight vector of non-negative weights, which will be normalized to sum to 1 if(missing(w))w=rep(1,length(f)) w=w/sum(w) f=scale(f,TRUE,FALSE)#center f so exponents are not too large time=time-d*eps#break ties between death times and non death times, leaving tied death times tied o=order(time) ef=exp(f)[o] time=time[o] d=d[o] w=w[o] rskden=rev(cumsum(rev(ef*w))) ##reverse order inside;last guy is in all the risk sets ### See if there are dups in death times dups=fid(time[d==1],seq(length(d))[d==1]) dd=d ww=w ### next code replaces each sequence of tied death indicators by a new ### sequence where only the first is a 1 and the rest are zero. This ### makes the accounting in the following step work properly we also ### sums the weights in each of the tied death sets, and assign that ### weight to the first if(!is.null(ties<-dups$index_ties)){ dd[unlist(ties)]=0 dd[dups$index_first]=1 wsum=sapply(ties,function(i,w)sum(w[i]),ww) tie1=sapply(ties,function(i)i[1]) ww[tie1]=wsum } ### Get counts over risk sets at each death time rskcount=cumsum(dd)#this says how many of the risk sets each observation is in; 0 is none ### We now form partial sums of the 1/den just at the risk sets rskdeninv=cumsum((ww/rskden)[dd==1]) ### pad with a zero, so we can index it rskdeninv=c(0,rskdeninv) ### compute gradient for each obs grad=(d-rskdeninv[rskcount+1]*ef)*w grad[o]=grad grad } fid=function(x,index){ ### Input: ### x is a sorted vector of death times ### index is vector of indices of this set ### Output: ### index of first member of every death set as they appear in sorted list ### list of ties for each element of index, in the case of two or more ties; ## if no ties, this list is NULL idup=duplicated(x) if(!any(idup)) list(index_first=index,index_ties=NULL) else{ ndup=!idup xu=x[ndup]# first death times index_first=index[ndup] ities=match(x,xu) index_ties=split(index,ities) nties=sapply(index_ties,length) list(index_first=index_first,index_ties=index_ties[nties>1]) } } glmnet/R/deviance.glmnet.R0000644000176200001440000000274013552673674015154 0ustar liggesusers#' Extract the deviance from a glmnet object #' #' Compute the deviance sequence from the glmnet object #' #' A glmnet object has components \code{dev.ratio} and \code{nulldev}. The #' former is the fraction of (null) deviance explained. The deviance #' calculations incorporate weights if present in the model. The deviance is #' defined to be 2*(loglike_sat - loglike), where loglike_sat is the #' log-likelihood for the saturated model (a model with a free parameter per #' observation). Null deviance is defined to be 2*(loglike_sat #' -loglike(Null)); The NULL model refers to the intercept model, except for #' the Cox, where it is the 0 model. Hence dev.ratio=1-deviance/nulldev, and #' this \code{deviance} method returns (1-dev.ratio)*nulldev. #' #' @param object fitted glmnet object #' @param \dots additional print arguments #' @return (1-dev.ratio)*nulldev #' @author Jerome Friedman, Trevor Hastie and Rob Tibshirani\cr Maintainer: #' Trevor Hastie #' @seealso \code{glmnet}, \code{predict}, \code{print}, and \code{coef} #' methods. #' @references Friedman, J., Hastie, T. and Tibshirani, R. (2008) #' \emph{Regularization Paths for Generalized Linear Models via Coordinate #' Descent} #' @keywords models regression #' @examples #' #' x = matrix(rnorm(100 * 20), 100, 20) #' y = rnorm(100) #' fit1 = glmnet(x, y) #' deviance(fit1) #' @method deviance glmnet #' @export deviance.glmnet=function(object,...){ dev=object$dev nulldev=object$nulldev (1-dev)*nulldev } glmnet/R/predict.elnet.R0000644000176200001440000000026613552673674014652 0ustar liggesusers#' @method predict elnet #' @export predict.elnet=function(object,newx,s=NULL,type=c("link","response","coefficients","nonzero"),exact=FALSE,newoffset,...){ NextMethod("predict") } glmnet/R/onAttach.R0000644000176200001440000000022013534022702013615 0ustar liggesusers.onAttach=function(libname,pkgname){ packageStartupMessage("Loaded glmnet ", as.character(packageDescription("glmnet")[["Version"]]),"\n") } glmnet/R/plot.mrelnet.R0000644000176200001440000000201013552673674014522 0ustar liggesusers#' @method plot mrelnet #' @param type.coef If \code{type.coef="2norm"} then a single curve per #' variable, else if \code{type.coef="coef"}, a coefficient plot per response #' @rdname plot.glmnet #' @export plot.mrelnet <- function(x, xvar=c("norm","lambda","dev"),label=FALSE,type.coef=c("coef","2norm"),...){ type.coef=match.arg(type.coef) xvar=match.arg(xvar) beta=x$beta if(xvar=="norm") { cnorm1=function(beta){ which=nonzeroCoef(beta) beta=as.matrix(beta[which,]) apply(abs(beta),2,sum) } norm=apply(sapply(x$beta,cnorm1),1,sum) } else norm = NULL dfmat=x$dfmat if(type.coef=="coef"){ ncl=nrow(dfmat) clnames=rownames(dfmat) for( i in seq(ncl)){ plotCoef(beta[[i]],norm,x$lambda,dfmat[i,],x$dev.ratio,label=label,xvar=xvar,ylab=paste("Coefficients: Response",clnames[i]),...) } } else plotCoef(coefnorm(beta,2),norm,x$lambda,dfmat[1,],x$dev.ratio,label=label,xvar=xvar,ylab="Coefficient 2Norms",...) } glmnet/R/buildPredmat.default.R0000644000176200001440000000217013552673674016145 0ustar liggesusers#' @export buildPredmat.default=function(outlist, lambda, x, offset, foldid, alignment,...){ if (!is.null(offset)) { is.offset = TRUE offset = drop(offset) } else is.offset = FALSE predmat = matrix(NA, nrow(x), length(lambda)) nfolds = max(foldid) nlams = double(nfolds) nlambda=length(lambda) for (i in seq(nfolds)) { which = foldid == i fitobj = outlist[[i]] if (is.offset) off_sub = offset[which] preds = switch(alignment, fraction=predict(fitobj, x[which, , drop = FALSE], newoffset = off_sub,...), lambda=predict(fitobj, x[which, , drop = FALSE], s=lambda, newoffset = off_sub,...) ) nlami = min(ncol(preds),nlambda) predmat[which, seq(nlami)] = preds[,seq(nlami)] if(nlami0){#fatal error outlist=jerr.elnet(n) if(outlist$msg!="Unknown error")return(outlist) if(n==8888)msg="All observations censored - cannot proceed" else if(n==9999)msg="No positive observation weights" else if(match(n,c(20000,30000),FALSE)) msg="Inititialization numerical error; probably too many censored observations" else msg="Unknown error" list(n=n,fatal=TRUE,msg=msg) } else if(n<0){ # non-fatal error if(n<= -30000){ msg=paste("Numerical error at ",-n-30000,"th lambda value; solutions for larger values of lambda returned",sep="") list(n=n,fatal=FALSE,msg=msg) } else jerr.elnet(n,maxit,pmax) } } glmnet/R/assess.coxnet.R0000644000176200001440000000074613545227360014702 0ustar liggesusersassess.coxnet <- function(predmat,y,type.measure,weights,foldid,grouped){ ### This is a hack, because CV for coxnet is complex if(type.measure=="C")return(cv.coxnet(predmat,y,type.measure,weights,foldid,grouped)) nlambda=ncol(predmat) N=nrow(predmat) cvraw = matrix(NA, 1, nlambda) for (j in seq(nlambda)) { cvraw[1, j] = coxnet.deviance(predmat[,j],y, weights=weights) } list(cvraw=cvraw,weights=N,N=N,type.measure=type.measure,grouped=FALSE) } glmnet/R/nonzeroCoef.R0000644000176200001440000000200713534022702014350 0ustar liggesusersnonzeroCoef = function (beta, bystep = FALSE) { ### bystep = FALSE means which variables were ever nonzero ### bystep = TRUE means which variables are nonzero for each step nr=nrow(beta) if (nr == 1) {#degenerate case if (bystep) apply(beta, 2, function(x) if (abs(x) > 0) 1 else NULL) else { if (any(abs(beta) > 0)) 1 else NULL } } else { beta=abs(beta)>0 # this is sparse which=seq(nr) ones=rep(1,ncol(beta)) nz=as.vector((beta%*%ones)>0) which=which[nz] if (bystep) { if(length(which)>0){ beta=as.matrix(beta[which,,drop=FALSE]) nzel = function(x, which) if (any(x)) which[x] else NULL which=apply(beta, 2, nzel, which) if(!is.list(which))which=data.frame(which)# apply can return a matrix!! which } else{ dn=dimnames(beta)[[2]] which=vector("list",length(dn)) names(which)=dn which } } else which } } glmnet/R/coef.cv.relaxed.R0000644000176200001440000000141713552673674015057 0ustar liggesusers#' @method coef cv.relaxed #' @export coef.cv.relaxed <- function (object, s = c("lambda.1se", "lambda.min"), gamma=c("gamma.1se","gamma.min"),...) { relaxed=object$relaxed if (is.numeric(s)) lambda = s else if (is.character(s)) { s = match.arg(s) lambda = relaxed[[s]] if(missing(gamma)){ gamma=switch(s, lambda.1se=relaxed[["gamma.1se"]], lambda.min=relaxed[["gamma.min"]] ) } } else stop("Invalid form for s") if (is.character(gamma)) { gamma = match.arg(gamma) gamma = relaxed[[gamma]] } if(!is.numeric(gamma))stop("Invalid form for gamma") coef(object$glmnet.fit, s = lambda, gamma=gamma, ...) } glmnet/R/cvcompute.R0000644000176200001440000000115713534022702014073 0ustar liggesuserscvcompute <- function (cvstuff, foldid, nlams) { weights=cvstuff$weights mat=cvstuff$cvraw wisum = tapply(weights, foldid, sum) nfolds = max(foldid) outmat = matrix(NA, nfolds, ncol(mat)) good = matrix(0, nfolds, ncol(mat)) mat[is.infinite(mat)] = NA for (i in seq(nfolds)) { mati = mat[foldid == i, , drop = FALSE] wi = weights[foldid == i] outmat[i, ] = apply(mati, 2, weighted.mean, w = wi, na.rm = TRUE) good[i, seq(nlams[i])] = 1 } N = apply(good, 2, sum) list(cvraw = outmat, weights = wisum, N = N, type.measure=cvstuff$type.measure) } glmnet/R/check.dots.R0000644000176200001440000000122213534022702014104 0ustar liggesuserscheck_dots<- function(object,...,need=c("x","y","weights","offset","penalty.factor","lower.limits","upper.limits"),error="used coef.glmnet() or predict.glmnet() with `exact=TRUE`"){ if(is.null(need))return(invisible()) thiscall=object$call ncall=names(thiscall)[-1] w=match(ncall,need,0) need=need[w] nargs=names(list(...)) w=match(need,nargs,0)>0 if(!all(w)){ margs=need[!w] stop(paste(error,"so must in addition supply original argument(s) ",paste(margs,collapse=" and "), " in order to safely rerun glmnet"),call.=FALSE) } invisible() } glmnet/R/print.confusion.table.R0000644000176200001440000000077213552673674016340 0ustar liggesusers#' @method print confusion.table #' @export print.confusion.table=function(x,digits = max(3, getOption("digits") - 3), ...){ ndn=names(dimnames(x)) rowtot=rowSums(x) coltot=colSums(x) tot=sum(coltot) ncorrect=sum(diag(x)) correct=(ncorrect)/tot x=cbind(x,Total=rowtot) x=rbind(x,Total=c(coltot,tot)) dn=dimnames(x) names(dn)=ndn dimnames(x)=dn print(x,digits=digits,...) cat("\n Percent Correct: ",format(round(correct,digits)),"\n") invisible() } glmnet/R/rmult.R0000644000176200001440000000110113554367213013231 0ustar liggesusers#' Generate multinomial samples from a probability matrix #' #' Generate multinomial samples #' #' Simple function that calls the \code{rmultinom} function. It generates a class label #' for each row of its input matrix of class probabilities. #' #' @param p matrix of probabilities, with number of columns the number of classes #' @return a vector of class memberships #' @author Trevor Hastie \cr Maintainer: Trevor Hastie #' @export rmult rmult <- function(p){ x=t(apply(p,1,function(x)rmultinom(1,1,x))) x=x%*%seq(ncol(p)) drop(x) } glmnet/R/relax.glmnet.R0000644000176200001440000000770213560703434014477 0ustar liggesusers#' Fit a relaxed glmnet model #' #' @param fit For \code{relax.glmnet} a fitted 'glmnet' object #' @param maxp a limit on how many relaxed coefficients are allowed. Default is #' 'n-3', where 'n' is the sample size. This may not be sufficient for #' non-gaussian familes, in which case users should supply a smaller value. #' This argument can be supplied directly to 'glmnet'. #' @param path Since \code{glmnet} does not do stepsize optimization, the Newton #' algorithm can get stuck and not converge, especially with relaxed fits. With \code{path=TRUE}, #' each relaxed fit on a particular set of variables is computed pathwise using the original sequence #' of lambda values (with a zero attached to the end). Not needed for Gaussian models, and should not #' be used unless needed, since will lead to longer compute times. Default is \code{path=FALSE}. #' appropriate subset of variables #' @param check.args Should \code{relax.glmnet} make sure that all the data #' dependent arguments used in creating 'fit' have been resupplied. Default is #' 'TRUE'. #' @rdname glmnet #' @export relax.glmnet relax.glmnet=function(fit,x,..., maxp = n-3, path=FALSE, check.args=TRUE){ ## maxp could be a sensitive parameter, especially for glms ## This next call fails if you don't supply the crucial arguments ## that were used in the creation of "fit". Not needed if called relax.glmnet if(check.args)check_dots(fit,..., need=c("y","weights","offset","lower.limits","upper.limits"), error="used relax.glmnet(),") d=dim(x) n=d[1];p=d[2] ## extract coefs, and find out who is nonzero at each lambda a0=fit$a0 beta=fit$beta islistbeta=is.list(beta) if(islistbeta){ ngroups=length(beta) nzmat=abs(beta[[1]]) for(k in 2:ngroups)nzmat=nzmat+abs(beta[[k]]) } else nzmat=abs(beta) nzmat=nzmat>0 ## Here we go to some trouble to make sure we dont fit the same model more than once rgauss=sort(abs(rnorm(p))) undex=colSums(rgauss*nzmat)#a unique number for each distinct set of nonzeros sundex=sort(unique(undex[undex>0])) nzindex=match(sundex,undex)# get a match for each unique alldex=match(undex,sundex,0)# for putting them back ## dev=fit$dev.ratio nulldev=fit$nulldev excluders=seq(p) lam0 = if(path)c(fit$lambda,0)else 0 ### Fit a coefficient for each non null model internal.parms <- glmnet.control() if (internal.parms$itrace) { glmnet.control(itrace = 0) ## disable glmnet reporting cat("Relaxed Fits\n") pb <- utils::txtProgressBar(min = 0, max = length(sundex), initial = 0, style = 3) on.exit(glmnet.control(itrace = internal.parms$itrace)) on.exit(close(pb), add = TRUE) } for(i in seq(along=sundex)){ ind=nzmat[,nzindex[i]] wh=alldex==i pi=sum(ind) if(pi>maxp)next #can only fit maxp coefficients exclude=excluders[!ind] ifit=update(fit,lambda=lam0,exclude=exclude,...,relax=FALSE) nlam=ifit$dim[2] if (islistbeta) { a0[, wh] = ifit$a0[,nlam] for (i in 1:ngroups) beta[[i]][, wh] = ifit$beta[[i]][,nlam] } else { a0[wh] = ifit$a0[nlam] beta[, wh] = ifit$beta[,nlam] } dev[wh] = ifit$dev.ratio[nlam] if (internal.parms$itrace) utils::setTxtProgressBar(pb, i) } if (internal.parms$itrace) { utils::setTxtProgressBar(pb = pb, value = length(sundex)) } fitg=fit omit=colSums(nzmat)>maxp if(any(omit)){ if(islistbeta){ a0=a0[,!omit] for(i in 1:ngroups)beta[[i]]=beta[[i]][,!omit] } else{ beta=beta[,!omit] a0=a0[!omit] } dev=dev[!omit] fit$lambda=fit$lambda[!omit] fit$df=fit$df[!omit] } fit$beta=beta fit$a0=a0 fit$dev.ratio=dev class(fitg)=c("relaxed",class(fitg)) fitg$relaxed=fit fitg } glmnet/R/cv.multnet.R0000644000176200001440000000314513566632201014172 0ustar liggesuserscv.multnet <-function(predmat,y,type.measure,weights,foldid,grouped){ prob_min = 1e-05 prob_max = 1 - prob_min nc = dim(y) if (is.null(nc)) { y = as.factor(y) ntab = table(y) nc = as.integer(length(ntab)) y = diag(nc)[as.numeric(y), ] } else nc = nc[2] ywt = apply(y, 1, sum) y = y/ywt weights = weights * ywt N = nrow(y) - apply(is.na(predmat[, 1, ,drop=FALSE]), 2, sum)## dimensions could be lost if third dim=1 bigY = array(y, dim(predmat)) predmat=exp(predmat) predtot=apply(predmat,c(1,3),sum) for(i in 1:nc)predmat[,i,]=predmat[,i,]/predtot cvraw = switch(type.measure, mse = apply((bigY - predmat)^2,c(1, 3), sum), mae = apply(abs(bigY - predmat), c(1,3), sum), deviance = { predmat = pmin(pmax(predmat, prob_min), prob_max) lp = bigY * log(predmat) ly = bigY * log(bigY) ly[bigY == 0] = 0 apply(2 * (ly - lp), c(1, 3), sum) }, class = { classid = as.numeric(apply(predmat, 3, glmnet_softmax,ignore_labels=TRUE)) yperm = matrix(aperm(bigY, c(1, 3, 2)), ncol = nc) matrix(1 - yperm[cbind(seq(classid), classid)], ncol = ncol(predtot)) } ) list(cvraw=cvraw,weights=weights,N=N,type.measure=type.measure,grouped=grouped) } glmnet/R/plot.glmnet.R0000644000176200001440000000311313552673674014347 0ustar liggesusers#' plot coefficients from a "glmnet" object #' #' Produces a coefficient profile plot of the coefficient paths for a fitted #' \code{"glmnet"} object. #' #' A coefficient profile plot is produced. If \code{x} is a multinomial model, #' a coefficient plot is produced for each class. #' #' @aliases plot.glmnet plot.multnet plot.mrelnet plot.relaxed #' @param x fitted \code{"glmnet"} model #' @param xvar What is on the X-axis. \code{"norm"} plots against the L1-norm #' of the coefficients, \code{"lambda"} against the log-lambda sequence, and #' \code{"dev"} against the percent deviance explained. #' @param label If \code{TRUE}, label the curves with variable sequence #' numbers. #' @param \dots Other graphical parameters to plot #' @author Jerome Friedman, Trevor Hastie and Rob Tibshirani\cr Maintainer: #' Trevor Hastie #' @seealso \code{glmnet}, and \code{print}, \code{predict} and \code{coef} #' methods. #' @references Friedman, J., Hastie, T. and Tibshirani, R. (2008) #' \emph{Regularization Paths for Generalized Linear Models via Coordinate #' Descent} #' @keywords models regression #' #' @examples #' x=matrix(rnorm(100*20),100,20) #' y=rnorm(100) #' g2=sample(1:2,100,replace=TRUE) #' g4=sample(1:4,100,replace=TRUE) #' fit1=glmnet(x,y) #' plot(fit1) #' plot(fit1,xvar="lambda",label=TRUE) #' fit3=glmnet(x,g4,family="multinomial") #' plot(fit3,pch=19) #' @method plot glmnet #' @export plot.glmnet=function(x, xvar=c("norm","lambda","dev"),label=FALSE,...){ xvar=match.arg(xvar) plotCoef(x$beta,lambda=x$lambda,df=x$df,dev=x$dev.ratio,label=label,xvar=xvar,...) } glmnet/R/predict.relaxed.R0000644000176200001440000000223613557667713015170 0ustar liggesusers#' @method predict relaxed #' @param gamma Single value of \code{gamma} at which predictions are required, #' for "relaxed" objects. #' @rdname predict.glmnet #' @export #' @export predict.relaxed predict.relaxed=function (object, newx, s = NULL, gamma=1,type = c("link", "response", "coefficients", "nonzero", "class"), exact = FALSE, newoffset, ...) { exact=FALSE # we cannot accommodate exact here type=match.arg(type) gamma=checkgamma.relax(gamma) if(length(gamma)>1){ ng=length(gamma) outlist=as.list(length(ng)) names(outlist(format(round(gamma,2)))) for( i in 1:ng)outlist[[i]]=predict(object, newx, s, gamma=gamma[i], exact, newoffset, ...) return(outlist) } if(gamma==1)return(NextMethod("predict")) predict(blend.relaxed(object,gamma),newx, s,type, exact, newoffset, ...) } checkgamma.relax=function(gamma){ if(any(wh<-gamma<0)){ warning("negative gamma values ignored") gamma=gamma[!wh] } if(any(wh<-gamma>1)){ warning("gamma values larger than 1 ignored") gamma=gamma[!wh] } if(!length(gamma))stop("no valid values of gamma") gamma } glmnet/R/print.cv.relaxed.R0000644000176200001440000000147713552673674015305 0ustar liggesusers#' @method print cv.relaxed #' @export print.cv.relaxed <- function(x, digits = max(3, getOption("digits") - 3), ...) { cat("\nCall: ", deparse(x$call), "\n\n") cat("Measure:", x$name,"\n\n") x=x$relaxed optlams=c(x$lambda.min,x$lambda.1se) wg1=match(x$gamma.min,x$gamma) wl1=match(x$lambda.min,x$statlist[[wg1]]$lambda) s1=with(x$statlist[[wg1]],c(x$gamma.min,x$lambda.min,cvm[wl1],cvsd[wl1],x$nzero.min)) wg2=match(x$gamma.1se,x$gamma) wl2=match(x$lambda.1se,x$statlist[[wg2]]$lambda) s2=with(x$statlist[[wg2]],c(x$gamma.1se,x$lambda.1se,cvm[wl2],cvsd[wl2],x$nzero.1se)) mat=rbind(s1,s2) dimnames(mat)=list(c("min","1se"),c("Gamma","Lambda","Measure","SE","Nonzero")) mat=data.frame(mat,check.names=FALSE) class(mat)=c("anova",class(mat)) print(mat,digits=digits) } glmnet/R/cv.elnet.R0000644000176200001440000000056313534022702013604 0ustar liggesuserscv.elnet <-function(predmat,y,type.measure,weights,foldid,grouped){ N = length(y) - apply(is.na(predmat), 2, sum) cvraw = switch(type.measure, mse = (y - predmat)^2, deviance = (y - predmat)^2, mae = abs(y - predmat)) list(cvraw=cvraw,weights=weights,N=N,type.measure=type.measure,grouped=grouped) } glmnet/R/buildPredmat.coxnetlist.R0000644000176200001440000000417413552673674016723 0ustar liggesusers#' @method buildPredmat coxnetlist #' @export buildPredmat.coxnetlist <- function(outlist, lambda, x, offset, foldid, alignment,y,weights,grouped,type.measure="deviance",...){ nfolds = max(foldid) if ((length(weights)/nfolds < 10) && !grouped) grouped = TRUE devtrue=type.measure=="deviance" cvraw = if(devtrue) matrix(NA, nfolds, length(lambda)) else NULL nlambda = length(lambda) predmat=matrix(NA, nrow(x), length(lambda)) rn=rownames(x) sn=paste("s",seq(0,length=nlambda),sep="") dimnames(predmat)=list(rn,sn) predmat for (i in seq(nfolds)) { which = foldid == i fitobj = outlist[[i]] coefmat = switch(alignment, fraction = predict(fitobj,type = "coefficients",...), lambda = predict(fitobj, type = "coefficients",s = lambda,...) ) nlami = min(ncol(coefmat), nlambda) if(devtrue){ if (grouped) { plfull = coxnet.deviance(x = x, y = y, offset = offset, weights = weights, beta = coefmat) plminusk = coxnet.deviance(x = x[!which, ], y = y[!which, ], offset = offset[!which], weights = weights[!which],beta = coefmat) cvraw[i, seq(nlami)] = (plfull - plminusk)[seq(nlami)] } else { plk = coxnet.deviance(x = x[which, ], y = y[which, ], offset = offset[which], weights = weights[which], beta = coefmat) cvraw[i, seq(nlami)] = plk[seq(nlami)] } } predmat[which, seq(nlami)] = as.matrix(x[which,]%*% coefmat) if (nlami < nlambda){ if(devtrue)cvraw[i, seq(from = nlami, to = nlambda)] = cvraw[i, nlami] predmat[which,seq(from=nlami,to=nlambda)]=predmat[which,nlami] } } if(devtrue) attr(predmat,"cvraw")=cvraw predmat } glmnet/R/auc.mat.R0000644000176200001440000000026213534022702013412 0ustar liggesusersauc.mat=function(y,prob,weights=rep(1,nrow(y))){ Weights=as.vector(weights*y) ny=nrow(y) Y=rep(c(0,1),c(ny,ny)) Prob=c(prob,prob) auc(Y,Prob,Weights) } glmnet/R/auc.R0000644000176200001440000000103313560673471012645 0ustar liggesusersauc=function(y,prob,w){ if(missing(w)){ rprob=rank(prob) n1=sum(y);n0=length(y)-n1 u=sum(rprob[y==1])-n1*(n1+1)/2 exp(log(u) - log(n1) - log(n0)) } else{ rprob=runif(length(prob)) op=order(prob,rprob)#randomize ties y=y[op] w=w[op] cw=cumsum(w) w1=w[y==1] cw1=cumsum(w1) wauc = log(sum(w1*(cw[y==1]-cw1))) sumw1 = cw1[length(cw1)] sumw2 = cw[length(cw)] - sumw1 exp(wauc - log(sumw1) - log(sumw2)) } } glmnet/R/glmnet_softmax.R0000644000176200001440000000130713552136161015117 0ustar liggesusersglmnet_softmax <- function (x,ignore_labels=FALSE) { d <- dim(x) dd <- dimnames(x)[[2]] if(is.null(dd) || !length(dd)) ignore_labels=TRUE nas=apply(is.na(x),1,any) if(any(nas)){ pclass=rep(NA,d[1]) if(sum(nas) maxdist pclass[l] <- i maxdist[l] <- x[l, i] } dd <- dimnames(x)[[2]] if(!ignore_labels) pclass=factor(pclass, levels = seq(d[2]), labels = dd) } pclass } glmnet/R/cv.glmnet.raw.R0000644000176200001440000000576313545025710014566 0ustar liggesuserscv.glmnet.raw <- function (x, y, weights, offset, lambda, type.measure, nfolds, foldid, alignment,grouped, keep, parallel, trace.it, glmnet.call, cv.call, ...) { if (trace.it) cat("Training\n") glmnet.object = glmnet(x, y, weights = weights, offset = offset, lambda = lambda, ...) glmnet.object$call = glmnet.call subclass=class(glmnet.object)[[1]] type.measure=cvtype(type.measure,subclass) is.offset = glmnet.object$offset ###Next line is commented out so each call generates its own lambda sequence # lambda=glmnet.object$lambda if (inherits(glmnet.object, "multnet") && !glmnet.object$grouped) { nz = predict(glmnet.object, type = "nonzero") nz = sapply(nz, function(x) sapply(x, length)) nz = ceiling(apply(nz, 1, median)) } else nz = sapply(predict(glmnet.object, type = "nonzero"), length) outlist = as.list(seq(nfolds)) N=nrow(x) if (parallel) { # if (parallel && require(foreach)) { outlist = foreach(i = seq(nfolds), .packages = c("glmnet")) %dopar% { which = foldid == i # if (is.matrix(y)) if (length(dim(y))>1) y_sub = y[!which, ] else y_sub = y[!which] if (is.offset) offset_sub = as.matrix(offset)[!which, ] else offset_sub = NULL glmnet(x[!which, , drop = FALSE], y_sub, lambda = lambda, offset = offset_sub, weights = weights[!which], ...) } } else { for (i in seq(nfolds)) { if (trace.it) cat(sprintf("Fold: %d/%d\n", i, nfolds)) which = foldid == i if (is.matrix(y)) y_sub = y[!which, ] else y_sub = y[!which] if (is.offset) offset_sub = as.matrix(offset)[!which, ] else offset_sub = NULL outlist[[i]] = glmnet(x[!which, , drop = FALSE], y_sub, lambda = lambda, offset = offset_sub, weights = weights[!which], ...) } } lambda = glmnet.object$lambda class(outlist)=paste0(subclass,"list") predmat=buildPredmat(outlist,lambda,x,offset,foldid,alignment,y=y,weights=weights, grouped=grouped,type.measure=type.measure) ### we include type.measure for the special case of coxnet with the deviance vs C-index discrepancy ### Next we compute the measures fun = paste("cv", subclass, sep = ".") cvstuff = do.call(fun, list(predmat,y,type.measure,weights,foldid,grouped)) grouped=cvstuff$grouped if ((N/nfolds < 3) && grouped) { warning("Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per fold", call. = FALSE) grouped = FALSE } out=cvstats(cvstuff,foldid,nfolds,lambda,nz,grouped) cvname = names(cvstuff$type.measure) names(cvname)=cvstuff$type.measure# to be compatible with earlier version; silly, I know out = c(out,list(call=cv.call,name = cvname, glmnet.fit = glmnet.object)) if (keep) out = c(out, list(fit.preval = predmat, foldid = foldid)) lamin=with(out,getOptcv.glmnet(lambda, cvm, cvsd, cvname)) obj = c(out, as.list(lamin)) class(obj) = "cv.glmnet" obj } glmnet/R/predict.lognet.R0000644000176200001440000000137113552673674015031 0ustar liggesusers#' @method predict lognet #' @export predict.lognet=function(object,newx,s=NULL,type=c("link","response","coefficients","class","nonzero"),exact=FALSE,newoffset,...){ type=match.arg(type) ### remember that although the fortran lognet makes predictions ### for the first class, we make predictions for the second class ### to avoid confusion with 0/1 responses. ### glmnet flipped the signs of the coefficients nfit=NextMethod("predict") switch(type, response={ pp=exp(-nfit) 1/(1+pp) }, class={ cnum=ifelse(nfit>0,2,1) clet=object$classnames[cnum] if(is.matrix(cnum))clet=array(clet,dim(cnum),dimnames(cnum)) clet }, nfit ) } glmnet/R/glmnet.R0000644000176200001440000005515213560637066013376 0ustar liggesusers#' fit a GLM with lasso or elasticnet regularization #' #' Fit a generalized linear model via penalized maximum likelihood. The #' regularization path is computed for the lasso or elasticnet penalty at a #' grid of values for the regularization parameter lambda. Can deal with all #' shapes of data, including very large sparse data matrices. Fits linear, #' logistic and multinomial, poisson, and Cox regression models. #' #' The sequence of models implied by \code{lambda} is fit by coordinate #' descent. For \code{family="gaussian"} this is the lasso sequence if #' \code{alpha=1}, else it is the elasticnet sequence. For the other families, #' this is a lasso or elasticnet regularization path for fitting the #' generalized linear regression paths, by maximizing the appropriate penalized #' log-likelihood (partial likelihood for the "cox" model). Sometimes the #' sequence is truncated before \code{nlambda} values of \code{lambda} have #' been used, because of instabilities in the inverse link functions near a #' saturated fit. \code{glmnet(...,family="binomial")} fits a traditional #' logistic regression model for the log-odds. #' \code{glmnet(...,family="multinomial")} fits a symmetric multinomial model, #' where each class is represented by a linear model (on the log-scale). The #' penalties take care of redundancies. A two-class \code{"multinomial"} model #' will produce the same fit as the corresponding \code{"binomial"} model, #' except the pair of coefficient matrices will be equal in magnitude and #' opposite in sign, and half the \code{"binomial"} values. Note that the #' objective function for \code{"gaussian"} is \deqn{1/2 RSS/nobs + #' \lambda*penalty,} and for the other models it is \deqn{-loglik/nobs + #' \lambda*penalty.} Note also that for \code{"gaussian"}, \code{glmnet} #' standardizes y to have unit variance (using 1/n rather than 1/(n-1) formula) #' before computing its lambda sequence (and then unstandardizes the resulting #' coefficients); if you wish to reproduce/compare results with other software, #' best to supply a standardized y. The coefficients for any predictor #' variables with zero variance are set to zero for all values of lambda. The #' latest two features in glmnet are the \code{family="mgaussian"} family and #' the \code{type.multinomial="grouped"} option for multinomial fitting. The #' former allows a multi-response gaussian model to be fit, using a "group #' -lasso" penalty on the coefficients for each variable. Tying the responses #' together like this is called "multi-task" learning in some domains. The #' grouped multinomial allows the same penalty for the #' \code{family="multinomial"} model, which is also multi-responsed. For both #' of these the penalty on the coefficient vector for variable j is #' \deqn{(1-\alpha)/2||\beta_j||_2^2+\alpha||\beta_j||_2.} When \code{alpha=1} #' this is a group-lasso penalty, and otherwise it mixes with quadratic just #' like elasticnet. A small detail in the Cox model: if death times are tied #' with censored times, we assume the censored times occurred just #' \emph{before} the death times in computing the Breslow approximation; if #' users prefer the usual convention of \emph{after}, they can add a small #' number to all censoring times to achieve this effect. If \code{relax=TRUE} #' a duplicate sequence of models is produced, where each active set in the #' elastic-net path is refit without regularization. The result of this is a #' matching \code{"glmnet"} object which is stored on the original object in a #' component named \code{"relaxed"}, and is part of the glmnet output. #' Generally users will not call \code{relax.glmnet} directly, unless the #' original 'glmnet' object took a long time to fit. But if they do, they must #' supply the fit, and all the original arguments used to create that fit. They #' can limit the length of the relaxed path via 'maxp'. #' #' @param x input matrix, of dimension nobs x nvars; each row is an observation #' vector. Can be in sparse matrix format (inherit from class #' \code{"sparseMatrix"} as in package \code{Matrix}; not yet available for #' \code{family="cox"}) #' @param y response variable. Quantitative for \code{family="gaussian"}, or #' \code{family="poisson"} (non-negative counts). For \code{family="binomial"} #' should be either a factor with two levels, or a two-column matrix of counts #' or proportions (the second column is treated as the target class; for a #' factor, the last level in alphabetical order is the target class). For #' \code{family="multinomial"}, can be a \code{nc>=2} level factor, or a matrix #' with \code{nc} columns of counts or proportions. For either #' \code{"binomial"} or \code{"multinomial"}, if \code{y} is presented as a #' vector, it will be coerced into a factor. For \code{family="cox"}, \code{y} #' should be a two-column matrix with columns named 'time' and 'status'. The #' latter is a binary variable, with '1' indicating death, and '0' indicating #' right censored. The function \code{Surv()} in package \pkg{survival} #' produces such a matrix. For \code{family="mgaussian"}, \code{y} is a matrix #' of quantitative responses. #' @param family Response type (see above) #' @param weights observation weights. Can be total counts if responses are #' proportion matrices. Default is 1 for each observation #' @param offset A vector of length \code{nobs} that is included in the linear #' predictor (a \code{nobs x nc} matrix for the \code{"multinomial"} family). #' Useful for the \code{"poisson"} family (e.g. log of exposure time), or for #' refining a model by starting at a current fit. Default is \code{NULL}. If #' supplied, then values must also be supplied to the \code{predict} function. #' @param alpha The elasticnet mixing parameter, with \eqn{0\le\alpha\le 1}. #' The penalty is defined as #' \deqn{(1-\alpha)/2||\beta||_2^2+\alpha||\beta||_1.} \code{alpha=1} is the #' lasso penalty, and \code{alpha=0} the ridge penalty. #' @param nlambda The number of \code{lambda} values - default is 100. #' @param lambda.min.ratio Smallest value for \code{lambda}, as a fraction of #' \code{lambda.max}, the (data derived) entry value (i.e. the smallest value #' for which all coefficients are zero). The default depends on the sample size #' \code{nobs} relative to the number of variables \code{nvars}. If \code{nobs #' > nvars}, the default is \code{0.0001}, close to zero. If \code{nobs < #' nvars}, the default is \code{0.01}. A very small value of #' \code{lambda.min.ratio} will lead to a saturated fit in the \code{nobs < #' nvars} case. This is undefined for \code{"binomial"} and #' \code{"multinomial"} models, and \code{glmnet} will exit gracefully when the #' percentage deviance explained is almost 1. #' @param lambda A user supplied \code{lambda} sequence. Typical usage is to #' have the program compute its own \code{lambda} sequence based on #' \code{nlambda} and \code{lambda.min.ratio}. Supplying a value of #' \code{lambda} overrides this. WARNING: use with care. Avoid supplying a #' single value for \code{lambda} (for predictions after CV use #' \code{predict()} instead). Supply instead a decreasing sequence of #' \code{lambda} values. \code{glmnet} relies on its warms starts for speed, #' and its often faster to fit a whole path than compute a single fit. #' @param standardize Logical flag for x variable standardization, prior to #' fitting the model sequence. The coefficients are always returned on the #' original scale. Default is \code{standardize=TRUE}. If variables are in the #' same units already, you might not wish to standardize. See details below for #' y standardization with \code{family="gaussian"}. #' @param intercept Should intercept(s) be fitted (default=TRUE) or set to zero #' (FALSE) #' @param thresh Convergence threshold for coordinate descent. Each inner #' coordinate-descent loop continues until the maximum change in the objective #' after any coefficient update is less than \code{thresh} times the null #' deviance. Defaults value is \code{1E-7}. #' @param dfmax Limit the maximum number of variables in the model. Useful for #' very large \code{nvars}, if a partial path is desired. #' @param pmax Limit the maximum number of variables ever to be nonzero #' @param exclude Indices of variables to be excluded from the model. Default #' is none. Equivalent to an infinite penalty factor (next item). #' @param penalty.factor Separate penalty factors can be applied to each #' coefficient. This is a number that multiplies \code{lambda} to allow #' differential shrinkage. Can be 0 for some variables, which implies no #' shrinkage, and that variable is always included in the model. Default is 1 #' for all variables (and implicitly infinity for variables listed in #' \code{exclude}). Note: the penalty factors are internally rescaled to sum to #' nvars, and the lambda sequence will reflect this change. #' @param lower.limits Vector of lower limits for each coefficient; default #' \code{-Inf}. Each of these must be non-positive. Can be presented as a #' single value (which will then be replicated), else a vector of length #' \code{nvars} #' @param upper.limits Vector of upper limits for each coefficient; default #' \code{Inf}. See \code{lower.limits} #' @param maxit Maximum number of passes over the data for all lambda values; #' default is 10^5. #' @param type.gaussian Two algorithm types are supported for (only) #' \code{family="gaussian"}. The default when \code{nvar<500} is #' \code{type.gaussian="covariance"}, and saves all inner-products ever #' computed. This can be much faster than \code{type.gaussian="naive"}, which #' loops through \code{nobs} every time an inner-product is computed. The #' latter can be far more efficient for \code{nvar >> nobs} situations, or when #' \code{nvar > 500}. #' @param type.logistic If \code{"Newton"} then the exact hessian is used #' (default), while \code{"modified.Newton"} uses an upper-bound on the #' hessian, and can be faster. #' @param standardize.response This is for the \code{family="mgaussian"} #' family, and allows the user to standardize the response variables #' @param type.multinomial If \code{"grouped"} then a grouped lasso penalty is #' used on the multinomial coefficients for a variable. This ensures they are #' all in our out together. The default is \code{"ungrouped"} #' @param relax If \code{TRUE} then for each \emph{active set} in the path of #' solutions, the model is refit without any regularization. See \code{details} #' for more information. This argument is new, and users may experience convergence issues #' with small datasets, especially with non-gaussian families. Limiting the #' value of 'maxp' can alleviate these issues in some cases. #' @param trace.it If \code{trace.it=1}, then a progress bar is displayed; #' useful for big models that take a long time to fit. #' @param ... Additional argument used in \code{relax.glmnet}. These include #' some of the original arguments to 'glmnet', and each must be named if used. #' @return An object with S3 class \code{"glmnet","*" }, where \code{"*"} is #' \code{"elnet"}, \code{"lognet"}, \code{"multnet"}, \code{"fishnet"} #' (poisson), \code{"coxnet"} or \code{"mrelnet"} for the various types of #' models. If the model was created with \code{relax=TRUE} then this class has #' a prefix class of \code{"relaxed"}. \item{call}{the call that produced this #' object} \item{a0}{Intercept sequence of length \code{length(lambda)}} #' \item{beta}{For \code{"elnet"}, \code{"lognet"}, \code{"fishnet"} and #' \code{"coxnet"} models, a \code{nvars x length(lambda)} matrix of #' coefficients, stored in sparse column format (\code{"CsparseMatrix"}). For #' \code{"multnet"} and \code{"mgaussian"}, a list of \code{nc} such matrices, #' one for each class.} \item{lambda}{The actual sequence of \code{lambda} #' values used. When \code{alpha=0}, the largest lambda reported does not quite #' give the zero coefficients reported (\code{lambda=inf} would in principle). #' Instead, the largest \code{lambda} for \code{alpha=0.001} is used, and the #' sequence of \code{lambda} values is derived from this.} \item{dev.ratio}{The #' fraction of (null) deviance explained (for \code{"elnet"}, this is the #' R-square). The deviance calculations incorporate weights if present in the #' model. The deviance is defined to be 2*(loglike_sat - loglike), where #' loglike_sat is the log-likelihood for the saturated model (a model with a #' free parameter per observation). Hence dev.ratio=1-dev/nulldev.} #' \item{nulldev}{Null deviance (per observation). This is defined to be #' 2*(loglike_sat -loglike(Null)); The NULL model refers to the intercept #' model, except for the Cox, where it is the 0 model.} \item{df}{The number of #' nonzero coefficients for each value of \code{lambda}. For \code{"multnet"}, #' this is the number of variables with a nonzero coefficient for \emph{any} #' class.} \item{dfmat}{For \code{"multnet"} and \code{"mrelnet"} only. A #' matrix consisting of the number of nonzero coefficients per class} #' \item{dim}{dimension of coefficient matrix (ices)} \item{nobs}{number of #' observations} \item{npasses}{total passes over the data summed over all #' lambda values} \item{offset}{a logical variable indicating whether an offset #' was included in the model} \item{jerr}{error flag, for warnings and errors #' (largely for internal debugging).} \item{relaxed}{If \code{relax=TRUE}, this #' additional item is another glmnet object with different values for #' \code{beta} and \code{dev.ratio}} #' @author Jerome Friedman, Trevor Hastie, Balasubramanian Narasimhan, Noah #' Simon and Rob Tibshirani\cr Maintainer: Trevor Hastie #' \email{hastie@@stanford.edu} #' @seealso \code{print}, \code{predict}, \code{coef} and \code{plot} methods, #' and the \code{cv.glmnet} function. #' @references Friedman, J., Hastie, T. and Tibshirani, R. (2008) #' \emph{Regularization Paths for Generalized Linear Models via Coordinate #' Descent}, \url{https://web.stanford.edu/~hastie/Papers/glmnet.pdf}\cr #' \emph{Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010}\cr #' \url{https://www.jstatsoft.org/v33/i01/}\cr Simon, N., Friedman, J., Hastie, #' T., Tibshirani, R. (2011) \emph{Regularization Paths for Cox's Proportional #' Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. #' 39(5) 1-13}\cr \url{https://www.jstatsoft.org/v39/i05/}\cr Tibshirani, #' Robert, Bien, J., Friedman, J., Hastie, T.,Simon, N.,Taylor, J. and #' Tibshirani, Ryan. (2012) \emph{Strong Rules for Discarding Predictors in #' Lasso-type Problems, JRSSB vol 74},\cr #' \url{https://statweb.stanford.edu/~tibs/ftp/strong.pdf}\cr \emph{Stanford #' Statistics Technical Report}\cr \url{https://arxiv.org/abs/1707.08692}\cr #' Hastie, T., Tibshirani, Robert, Tibshirani, Ryan (2019) \emph{Extended #' Comparisons of Best Subset Selection, Forward Stepwise Selection, and the #' Lasso}\cr \emph{Glmnet Vignette} #' \url{https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html} #' @keywords models regression #' @examples #' #' # Gaussian #' x = matrix(rnorm(100 * 20), 100, 20) #' y = rnorm(100) #' fit1 = glmnet(x, y) #' print(fit1) #' coef(fit1, s = 0.01) # extract coefficients at a single value of lambda #' predict(fit1, newx = x[1:10, ], s = c(0.01, 0.005)) # make predictions #' #' # Relaxed #' fit1r = glmnet(x, y, relax = TRUE) # can be used with any model #' #' # multivariate gaussian #' y = matrix(rnorm(100 * 3), 100, 3) #' fit1m = glmnet(x, y, family = "mgaussian") #' plot(fit1m, type.coef = "2norm") #' #' # binomial #' g2 = sample(1:2, 100, replace = TRUE) #' fit2 = glmnet(x, g2, family = "binomial") #' fit2r = glmnet(x,g2, family = "binomial", relax=TRUE) #' fit2rp = glmnet(x,g2, family = "binomial", relax=TRUE, path=TRUE) #' #' # multinomial #' g4 = sample(1:4, 100, replace = TRUE) #' fit3 = glmnet(x, g4, family = "multinomial") #' fit3a = glmnet(x, g4, family = "multinomial", type.multinomial = "grouped") #' # poisson #' N = 500 #' p = 20 #' nzc = 5 #' x = matrix(rnorm(N * p), N, p) #' beta = rnorm(nzc) #' f = x[, seq(nzc)] %*% beta #' mu = exp(f) #' y = rpois(N, mu) #' fit = glmnet(x, y, family = "poisson") #' plot(fit) #' pfit = predict(fit, x, s = 0.001, type = "response") #' plot(pfit, y) #' #' # Cox #' set.seed(10101) #' N = 1000 #' p = 30 #' nzc = p/3 #' x = matrix(rnorm(N * p), N, p) #' beta = rnorm(nzc) #' fx = x[, seq(nzc)] %*% beta/3 #' hx = exp(fx) #' ty = rexp(N, hx) #' tcens = rbinom(n = N, prob = 0.3, size = 1) # censoring indicator #' y = cbind(time = ty, status = 1 - tcens) # y=Surv(ty,1-tcens) with library(survival) #' fit = glmnet(x, y, family = "cox") #' plot(fit) #' #' # Sparse #' n = 10000 #' p = 200 #' nzc = trunc(p/10) #' x = matrix(rnorm(n * p), n, p) #' iz = sample(1:(n * p), size = n * p * 0.85, replace = FALSE) #' x[iz] = 0 #' sx = Matrix(x, sparse = TRUE) #' inherits(sx, "sparseMatrix") #confirm that it is sparse #' beta = rnorm(nzc) #' fx = x[, seq(nzc)] %*% beta #' eps = rnorm(n) #' y = fx + eps #' px = exp(fx) #' px = px/(1 + px) #' ly = rbinom(n = length(px), prob = px, size = 1) #' system.time(fit1 <- glmnet(sx, y)) #' system.time(fit2n <- glmnet(x, y)) #' #' @export glmnet glmnet=function(x,y,family=c("gaussian","binomial","poisson","multinomial","cox","mgaussian"),weights,offset=NULL,alpha=1.0,nlambda=100,lambda.min.ratio=ifelse(nobs1){ warning("alpha >1; set to 1") alpha=1 } if(alpha<0){ warning("alpha<0; set to 0") alpha=0 } alpha=as.double(alpha) this.call=match.call() nlam=as.integer(nlambda) y=drop(y) # we dont like matrix responses unless we need them np=dim(x) ###check dims if(is.null(np)|(np[2]<=1))stop("x should be a matrix with 2 or more columns") nobs=as.integer(np[1]) if(missing(weights))weights=rep(1,nobs) else if(length(weights)!=nobs)stop(paste("number of elements in weights (",length(weights),") not equal to the number of rows of x (",nobs,")",sep="")) nvars=as.integer(np[2]) dimy=dim(y) nrowy=ifelse(is.null(dimy),length(y),dimy[1]) if(nrowy!=nobs)stop(paste("number of observations in y (",nrowy,") not equal to the number of rows of x (",nobs,")",sep="")) vnames=colnames(x) if(is.null(vnames))vnames=paste("V",seq(nvars),sep="") ne=as.integer(dfmax) nx=as.integer(pmax) if(missing(exclude))exclude=integer(0) if(any(penalty.factor==Inf)){ exclude=c(exclude,seq(nvars)[penalty.factor==Inf]) exclude=sort(unique(exclude)) } if(length(exclude)>0){ jd=match(exclude,seq(nvars),0) if(!all(jd>0))stop("Some excluded variables out of range") penalty.factor[jd]=1 #ow can change lambda sequence jd=as.integer(c(length(jd),jd)) }else jd=as.integer(0) vp=as.double(penalty.factor) internal.parms=glmnet.control() if(internal.parms$itrace)trace.it=1 else{ if(trace.it){ glmnet.control(itrace=1) on.exit(glmnet.control(itrace=0)) } } ###check on limits if(any(lower.limits>0)){stop("Lower limits should be non-positive")} if(any(upper.limits<0)){stop("Upper limits should be non-negative")} lower.limits[lower.limits==-Inf]=-internal.parms$big upper.limits[upper.limits==Inf]=internal.parms$big if(length(lower.limits)=1)stop("lambda.min.ratio should be less than 1") flmin=as.double(lambda.min.ratio) ulam=double(1) } else{ flmin=as.double(1) if(any(lambda<0))stop("lambdas should be non-negative") ulam=as.double(rev(sort(lambda))) nlam=as.integer(length(lambda)) } is.sparse=FALSE ix=jx=NULL if(inherits(x,"sparseMatrix")){##Sparse case is.sparse=TRUE x=as(x,"CsparseMatrix") x=as(x,"dgCMatrix") ix=as.integer(x@p+1) jx=as.integer(x@i+1) x=as.double(x@x) } if (trace.it) { if (relax) cat("Training Fit\n") pb <- createPB(min = 0, max = nlam, initial = 0, style = 3) } kopt=switch(match.arg(type.logistic), "Newton"=0,#This means to use the exact Hessian "modified.Newton"=1 # Use the upper bound ) if(family=="multinomial"){ type.multinomial=match.arg(type.multinomial) if(type.multinomial=="grouped")kopt=2 #overrules previous kopt } kopt=as.integer(kopt) fit=switch(family, "gaussian"=elnet(x,is.sparse,ix,jx,y,weights,offset,type.gaussian,alpha,nobs,nvars,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,vnames,maxit), "poisson"=fishnet(x,is.sparse,ix,jx,y,weights,offset,alpha,nobs,nvars,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,vnames,maxit), "binomial"=lognet(x,is.sparse,ix,jx,y,weights,offset,alpha,nobs,nvars,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,vnames,maxit,kopt,family), "multinomial"=lognet(x,is.sparse,ix,jx,y,weights,offset,alpha,nobs,nvars,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,vnames,maxit,kopt,family), "cox"=coxnet(x,is.sparse,ix,jx,y,weights,offset,alpha,nobs,nvars,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,vnames,maxit), "mgaussian"=mrelnet(x,is.sparse,ix,jx,y,weights,offset,alpha,nobs,nvars,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,jsd,intr,vnames,maxit) ) if (trace.it) { utils::setTxtProgressBar(pb, nlam) close(pb) } if(is.null(lambda))fit$lambda=fix.lam(fit$lambda)##first lambda is infinity; changed to entry point fit$call=this.call fit$nobs=nobs class(fit)=c(class(fit),"glmnet") if(relax) relax.glmnet(fit, x=x,y=y,weights=weights,offset=offset, lower.limits=lower.limits,upper.limits=upper.limits, check.args=FALSE,...) else fit } glmnet/R/print.glmnet.R0000644000176200001440000000305713552673674014534 0ustar liggesusers#' print a glmnet object #' #' Print a summary of the glmnet path at each step along the path. #' @details #' The call that produced the object `x` is printed, followed by a #' three-column matrix with columns `Df`, `%Dev` and `Lambda`. #' The `Df` column is the number of nonzero coefficients (Df is a #' reasonable name only for lasso fits). `%Dev` is the percent deviance #' explained (relative to the null deviance). In the case of a 'relaxed' fit, #' an additional column is inserted, `%Dev R` which gives the percent #' deviance explained by the relaxed model. For a "bigGlm" model, a simpler #' summary is printed. #' #' @aliases print.glmnet print.relaxed print.bigGlm #' @param x fitted glmnet object #' @param digits significant digits in printout #' @param \dots additional print arguments #' @return The matrix above is silently returned #' @seealso \code{glmnet}, \code{predict} and \code{coef} methods. #' @references Friedman, J., Hastie, T. and Tibshirani, R. (2008). Regularization Paths for Generalized Linear Models via Coordinate Descent #' @keywords models regression #' @examples #' #' x = matrix(rnorm(100 * 20), 100, 20) #' y = rnorm(100) #' fit1 = glmnet(x, y) #' print(fit1) #' @method print glmnet #' @export print.glmnet <- function (x, digits = max(3, getOption("digits") - 3), ...) { cat("\nCall: ", deparse(x$call), "\n\n") out=data.frame(Df = x$df, `%Dev` = signif(x$dev.ratio, digits), Lambda = signif(x$lambda, digits),check.names=FALSE,row.names=seq(along=x$df)) class(out)=c("anova",class(out)) print(out) } glmnet/R/coxnet.R0000644000176200001440000000322613534022702013365 0ustar liggesuserscoxnet=function(x,is.sparse,ix,jx,y,weights,offset,alpha,nobs,nvars,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,vnames,maxit){ if(!is.matrix(y)||!all(match(c("time","status"),dimnames(y)[[2]],0)))stop("Cox model requires a matrix with columns 'time' (>0) and 'status' (binary) as a response; a 'Surv' object suffices",call.=FALSE) ty=as.double(y[,"time"]) tevent=as.double(y[,"status"]) ty=ty+(1-tevent)*100*.Machine$double.eps## ties issue if(any(ty<=0))stop("negative event times encountered; not permitted for Cox family") maxit=as.integer(maxit) weights=as.double(weights) if(is.null(offset)){ offset=ty*0 is.offset=FALSE} else{ storage.mode(offset)="double" is.offset=TRUE } fit=if(is.sparse)stop("Cox model mot implemented for sparse x in glmnet",call.=FALSE) else .Fortran("coxnet", parm=alpha,nobs,nvars,as.double(x),ty,tevent,offset,weights,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh, maxit,isd,# need to get JHF to reverse these lmu=integer(1), ca=double(nx*nlam), ia=integer(nx), nin=integer(nlam), nulldev=double(1), dev=double(nlam), alm=double(nlam), nlp=integer(1), jerr=integer(1),PACKAGE="glmnet" ) if(fit$jerr!=0){ errmsg=jerr(fit$jerr,maxit,pmax=nx,family="cox") if(errmsg$fatal)stop(errmsg$msg,call.=FALSE) else warning(errmsg$msg,call.=FALSE) } outlist=getcoef(fit,nvars,nx,vnames) dev=fit$dev[seq(fit$lmu)] outlist=c(outlist,list(dev.ratio=dev,nulldev=fit$nulldev,npasses=fit$nlp,jerr=fit$jerr,offset=is.offset)) class(outlist)="coxnet" outlist } glmnet/R/coef.cv.glmnet.R0000644000176200001440000000045013552673674014715 0ustar liggesusers#' @method coef cv.glmnet #' @export coef.cv.glmnet=function(object,s=c("lambda.1se","lambda.min"),...){ if(is.numeric(s))lambda=s else if(is.character(s)){ s=match.arg(s) lambda=object[[s]] } else stop("Invalid form for s") coef(object$glmnet.fit,s=lambda,...) } glmnet/R/assess.glmnet.R0000644000176200001440000001257413573535116014674 0ustar liggesusers#' assess performace of a 'glmnet' object using test data. #' #' Given a test set, produce summary performance measures for the glmnet #' model(s) #' #' \code{assess.glmnet} produces all the different performance measures #' provided by \code{cv.glmnet} for each of the families. A single vector, or a #' matrix of predictions can be provided, or fitted model objects or CV #' objects. In the case when the predictions are still to be made, the #' \code{...} arguments allow, for example, 'offsets' and other prediction #' parameters such as values for 'gamma' for 'relaxed' fits. \code{roc.glmnet} #' produces for a single vector a two column matrix with columns TPR and FPR #' (true positive rate and false positive rate). This object can be plotted to #' produce an ROC curve. If more than one predictions are called for, then a #' list of such matrices is produced. \code{confusion.glmnet} produces a #' confusion matrix tabulating the classification results. Again, a single #' table or a list, with a print method. #' #' @aliases assess.glmnet confusion.glmnet roc.glmnet #' @param object Fitted \code{"glmnet"} or \code{"cv.glmnet"}, \code{"relaxed"} #' or \code{"cv.relaxed"} object, or a matrix of predictions (for #' \code{roc.glmnet} or \code{assess.glmnet}). For \code{roc.glmnet} the model #' must be a 'binomial', and for \code{confusion.glmnet} must be either #' 'binomial' or 'multinomial' #' @param newx If predictions are to made, these are the 'x' values. Required #' for \code{confusion.glmnet} #' @param newy required argument for all functions; the new response values #' @param weights For observation weights for the test observations #' @param family The family of the model, in case predictions are passed in as #' 'object' #' @param ... additional arguments to \code{predict.glmnet} when "object" is a #' "glmnet" fit, and predictions must be made to produce the statistics. #' @return \code{assess.glmnet} produces a list of vectors of measures. #' \code{roc.glmnet} a list of 'roc' two-column matrices, and #' \code{confusion.glmnet} a list of tables. If a single prediction is #' provided, or predictions are made from a CV object, the latter two drop the #' list status and produce a single matrix or table. #' @author Trevor Hastie and Rob Tibshirani\cr Maintainer: Trevor Hastie #' #' @seealso \code{cv.glmnet}, \code{glmnet.measures} and \code{vignette("relax",package="glmnet")} #' @keywords models classification #' @examples #' #' data(QuickStartExample) #' set.seed(11) #' train = sample(seq(length(y)),70,replace=FALSE) #' fit1 = glmnet(x[train,], y[train]) #' assess.glmnet(fit1, newx = x[-train,], newy = y[-train]) #' preds = predict(fit1, newx = x[-train, ], s = c(1, 0.25)) #' assess.glmnet(preds, newy = y[-train], family = "gaussian") #' fit1c = cv.glmnet(x, y, keep = TRUE) #' fit1a = assess.glmnet(fit1c$fit.preval, newy=y,family="gaussian") #' plot(fit1c$lambda, log="x",fit1a$mae,xlab="Log Lambda",ylab="Mean Absolute Error") #' abline(v=fit1c$lambda.min, lty=2, col="red") #' data(BinomialExample) #' fit2 = glmnet(x[train,], y[train], family = "binomial") #' assess.glmnet(fit2,newx = x[-train,], newy=y[-train], s=0.1) #' plot(roc.glmnet(fit2, newx = x[-train,], newy=y[-train])[[10]]) #' fit2c = cv.glmnet(x, y, family = "binomial", keep=TRUE) #' idmin = match(fit2c$lambda.min, fit2c$lambda) #' plot(roc.glmnet(fit2c$fit.preval, newy = y)[[idmin]]) #' data(MultinomialExample) #' set.seed(103) #' train = sample(seq(length(y)),100,replace=FALSE) #' fit3 = glmnet(x[train,], y[train], family = "multinomial") #' confusion.glmnet(fit3, newx = x[-train, ], newy = y[-train], s = 0.01) #' fit3c = cv.glmnet(x, y, family = "multinomial", type.measure="class", keep=TRUE) #' idmin = match(fit3c$lambda.min, fit3c$lambda) #' confusion.glmnet(fit3c$fit.preval, newy = y, family="multinomial")[[idmin]] #' #' @export assess.glmnet assess.glmnet=function(object,newx=NULL,newy,weights=NULL, family=c("gaussian","binomial","poisson","multinomial","cox","mgaussian"), ...){ ### object must be either a glmnet or cv.glmnet object, or else a matrix/vector of predictions of a glmnet model (not on the mean scale, but natural parameter scale) ### It must either be a matrix or an array. The last dimension can be 1. oclass=grep("glmnet",class(object)) if(length(oclass)){# either a glmnet or cv.glmnet predmat=predict(object,newx=newx,...) fam=family(object) } else{ fam=match.arg(family) predmat=object if(is.vector(predmat))predmat=as.matrix(predmat) } type.measures = glmnet.measures(fam) y = drop(newy) dimy=dim(y) nrowy=ifelse(is.null(dimy),length(y),dimy[1]) if (is.null(weights)) weights = rep(1, nrowy) subclass=switch(fam,gaussian="elnet",binomial="lognet",poisson="fishnet",multinomial="multnet", cox="coxnet",mgaussian="mrelnet") fun = paste("cv", subclass, sep = ".") if(subclass=="coxnet")fun="assess.coxnet" outlist=as.list(type.measures) names(outlist)=type.measures for(type.measure in type.measures){ type.measure=cvtype(type.measure,subclass) teststuff = do.call(fun, list(predmat,y,type.measure,weights,foldid=rep(1,nrowy),grouped=FALSE)) out=drop(with(teststuff, apply(cvraw, 2, weighted.mean, w = weights, na.rm = TRUE))) attr(out,"measure")=names(type.measure) outlist[[type.measure]]=out } outlist } glmnet/R/fishnet.R0000644000176200001440000000321013534022702013516 0ustar liggesusersfishnet=function(x,is.sparse,ix,jx,y,weights,offset,alpha,nobs,nvars,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,vnames,maxit){ if(any(y<0))stop("negative responses encountered; not permitted for Poisson family") maxit=as.integer(maxit) weights=as.double(weights) storage.mode(y)="double" if(is.null(offset)){ offset=y*0 #keeps the shape of y is.offset=FALSE} else{ storage.mode(offset)="double" is.offset=TRUE } fit=if(is.sparse).Fortran("spfishnet", parm=alpha,nobs,nvars,x,ix,jx,y,offset,weights,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,maxit, lmu=integer(1), a0=double(nlam), ca=double(nx*nlam), ia=integer(nx), nin=integer(nlam), nulldev=double(1), dev=double(nlam), alm=double(nlam), nlp=integer(1), jerr=integer(1),PACKAGE="glmnet" ) else .Fortran("fishnet", parm=alpha,nobs,nvars,as.double(x),y,offset,weights,jd,vp,cl,ne,nx,nlam,flmin,ulam,thresh,isd,intr,maxit, lmu=integer(1), a0=double(nlam), ca=double(nx*nlam), ia=integer(nx), nin=integer(nlam), nulldev=double(1), dev=double(nlam), alm=double(nlam), nlp=integer(1), jerr=integer(1),PACKAGE="glmnet" ) if(fit$jerr!=0){ errmsg=jerr(fit$jerr,maxit,pmax=nx,family="poisson") if(errmsg$fatal)stop(errmsg$msg,call.=FALSE) else warning(errmsg$msg,call.=FALSE) } outlist=getcoef(fit,nvars,nx,vnames) dev=fit$dev[seq(fit$lmu)] outlist=c(outlist,list(dev.ratio=dev,nulldev=fit$nulldev,npasses=fit$nlp,jerr=fit$jerr,offset=is.offset)) class(outlist)="fishnet" outlist } glmnet/NEWS.md0000644000176200001440000001434713574046107012656 0ustar liggesusers# glmnet 3.0-2 Bug fixes * `confusion.glmnet` was sometimes not returning a list because of apply collapsing structure * `cv.mrelnet` and `cv.multnet` dropping dimensions inappropriately * Fix to `storePB` to avoid segfault. Thanks [Tomas Kalibera](https://github.com/kalibera)! * Changed the help for `assess.glmnet` and cousins to be more helpful! * Changed some logic in `lambda.interp` to avoid edge cases (thanks David Keplinger) # glmnet 3.0-1 Minor fix to correct Depends in the DESCRIPTION to R (>= 3.6.0) # glmnet 3.0 This is a major revision with much added functionality, listed roughly in order of importance. An additional vignette called `relax` is supplied to describe the usage. * `relax` argument added to `glmnet`. This causes the models in the path to be refit without regularization. The resulting object inherits from class `glmnet`, and has an additional component, itself a glmnet object, which is the relaxed fit. * `relax` argument to `cv.glmnet`. This allows selection from a mixture of the relaxed fit and the regular fit. The mixture is governed by an argument `gamma` with a default of 5 values between 0 and 1. * `predict`, `coef` and `plot` methods for `relaxed` and `cv.relaxed` objects. * `print` method for `relaxed` object, and new `print` methods for `cv.glmnet` and `cv.relaxed` objects. * A progress bar is provided via an additional argument `trace.it=TRUE` to `glmnet` and `cv.glmnet`. This can also be set for the session via `glmnet.control`. * Three new functions `assess.glmnet`, `roc.glmnet` and `confusion.glmnet` for displaying the performance of models. * `makeX` for building the `x` matrix for input to `glmnet`. Main functionality is *one-hot-encoding* of factor variables, treatment of `NA` and creating sparse inputs. * `bigGlm` for fitting the GLMs of `glmnet` unpenalized. In addition to these new features, some of the code in `glmnet` has been tidied up, especially related to CV. # glmnet 2.0-20 * Fixed a bug in internal function `coxnet.deviance` to do with input `pred`, as well as saturated `loglike` (missing) and weights * added a `coxgrad` function for computing the gradient # glmnet 2.0-19 * Fixed a bug in coxnet to do with ties between death set and risk set # glmnet 2.0-18 * Added an option alignment to `cv.glmnet`, for cases when wierd things happen # glmnet 2.0-17 * Further fixes to mortran to get clean fortran; current mortran src is in `inst/mortran` # glmnet 2.0-16 * Additional fixes to mortran; current mortran src is in `inst/mortran` * Mortran uses double precision, and variables are initialized to avoid `-Wall` warnings * cleaned up repeat code in CV by creating a utility function # glmnet 2.0-15 * Fixed up the mortran so that generic fortran compiler can run without any configure # glmnet 2.0-13 * Cleaned up some bugs to do with exact prediction * `newoffset` created problems all over - fixed these # glmnet 2.0-11 * Added protection with `exact=TRUE` calls to `coef` and `predict`. See help file for more details # glmnet 2.0-10 * Two iterations to fix to fix native fortran registration. # glmnet 2.0-8 * included native registration of fortran # glmnet 2.0-7 * constant `y` blows up `elnet`; error trap included * fixed `lambda.interp` which was returning `NaN` under degenerate circumstances. # glmnet 2.0-6 * added some code to extract time and status gracefully from a `Surv` object # glmnet 2.0-3 * changed the usage of `predict` and `coef` with `exact=TRUE`. The user is strongly encouraged to supply the original `x` and `y` values, as well as any other data such as weights that were used in the original fit. # glmnet 2.0-1 * Major upgrade to CV; let each model use its own lambdas, then predict at original set. * fixed some minor bugs # glmnet 1.9-9 * fixed subsetting bug in `lognet` when some weights are zero and `x` is sparse # glmnet 1.9-8 * fixed bug in multivariate response model (uninitialized variable), leading to valgrind issues * fixed issue with multinomial response matrix and zeros * Added a link to a glmnet vignette # glmnet 1.9-6 * fixed bug in `predict.glmnet`, `predict.multnet` and `predict.coxnet`, when `s=` argument is used with a vector of values. It was not doing the matrix multiply correctly * changed documentation of glmnet to explain logistic response matrix # glmnet 1.9-5 * added parallel capabilities, and fixed some minor bugs # glmnet 1.9-3 * added `intercept` option # glmnet 1.9-1 * added upper and lower bounds for coefficients * added `glmnet.control` for setting systems parameters * fixed serious bug in `coxnet` # glmnet 1.8-5 * added `exact=TRUE` option for prediction and coef functions # glmnet 1.8 * Major new release * added `mgaussian` family for multivariate response * added `grouped` option for multinomial family # glmnet 1.7-4 * nasty bug fixed in fortran - removed reference to dble * check class of `newx` and make `dgCmatrix` if sparse # glmnet 1.7-1 * `lognet` added a classnames component to the object * `predict.lognet(type="class")` now returns a character vector/matrix # glmnet 1.6 * `predict.glmnet` : fixed bug with `type="nonzero"` * `glmnet`: Now x can inherit from `sparseMatrix` rather than the very specific `dgCMatrix`, and this will trigger sparse mode for glmnet # glmnet 1.5 * `glmnet.Rd` (`lambda.min`) : changed value to 0.01 if `nobs < nvars`, (`lambda`) added warnings to avoid single value, (`lambda.min`): renamed it `lambda.min.ratio` * `glmnet` (`lambda.min`) : changed value to 0.01 if `nobs < nvars` (`HessianExact`) : changed the sense (it was wrong), (`lambda.min`): renamed it `lambda.min.ratio`. 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ef63dce9f91dc617a84540178b92a89f *vignettes/assets/glmnet_refs.bib 1085a7b8d861bec70ffdc10540f9c4bb *vignettes/glmnet.Rmd 9a02753803d77927adb06d655a99ecb0 *vignettes/relax.Rmd glmnet/inst/0000755000176200001440000000000013574046201012517 5ustar liggesusersglmnet/inst/mortran/0000755000176200001440000000000013542237467014214 5ustar liggesusersglmnet/inst/mortran/glmnet5dpclean.m0000644000176200001440000045163513542237467017312 0ustar liggesusers" c c newGLMnet (5/12/14) c c c Elastic net with squared-error loss c c dense predictor matrix: c c call elnet(ka,parm,no,ni,x,y,w,jd,vp,cl,ne,nx,nlam,flmin,ulam,thr,isd, c intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr) c c x(no,ni) = predictor data matrix flat file (overwritten) c c c sparse predictor matrix: c c call spelnet(ka,parm,no,ni,x,ix,jx,y,w,jd,vp,cl,ne,nx,nlam,flmin,ulam,thr, c isd,intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr) c c x, ix, jx = predictor data matrix in compressed sparse row format c c c other inputs: c c ka = algorithm flag c ka=1 => covariance updating algorithm c ka=2 => naive algorithm c parm = penalty member index (0 <= parm <= 1) c = 0.0 => ridge c = 1.0 => lasso c no = number of observations c ni = number of predictor variables c y(no) = response vector (overwritten) c w(no)= observation weights (overwritten) c jd(jd(1)+1) = predictor variable deletion flag c jd(1) = 0 => use all variables c jd(1) != 0 => do not use variables jd(2)...jd(jd(1)+1) c vp(ni) = relative penalties for each predictor variable c vp(j) = 0 => jth variable unpenalized c cl(2,ni) = interval constraints on coefficient values (overwritten) c cl(1,j) = lower bound for jth coefficient value (<= 0.0) c cl(2,j) = upper bound for jth coefficient value (>= 0.0) c ne = maximum number of variables allowed to enter largest model c (stopping criterion) c nx = maximum number of variables allowed to enter all models c along path (memory allocation, nx > ne). c nlam = (maximum) number of lamda values c flmin = user control of lamda values (>=0) c flmin < 1.0 => minimum lamda = flmin*(largest lamda value) c flmin >= 1.0 => use supplied lamda values (see below) c ulam(nlam) = user supplied lamda values (ignored if flmin < 1.0) c thr = convergence threshold for each lamda solution. c iterations stop when the maximum reduction in the criterion value c as a result of each parameter update over a single pass c is less than thr times the null criterion value. c (suggested value, thr=1.0e-5) c isd = predictor variable standarization flag: c isd = 0 => regression on original predictor variables c isd = 1 => regression on standardized predictor variables c Note: output solutions always reference original c variables locations and scales. c intr = intercept flag c intr = 0/1 => don't/do include intercept in model c maxit = maximum allowed number of passes over the data for all lambda c values (suggested values, maxit = 100000) c c output: c c lmu = actual number of lamda values (solutions) c a0(lmu) = intercept values for each solution c ca(nx,lmu) = compressed coefficient values for each solution c ia(nx) = pointers to compressed coefficients c nin(lmu) = number of compressed coefficients for each solution c rsq(lmu) = R**2 values for each solution c alm(lmu) = lamda values corresponding to each solution c nlp = actual number of passes over the data for all lamda values c jerr = error flag: c jerr = 0 => no error c jerr > 0 => fatal error - no output returned c jerr < 7777 => memory allocation error c jerr = 7777 => all used predictors have zero variance c jerr = 10000 => maxval(vp) <= 0.0 C jerr < 0 => non fatal error - partial output: c Solutions for larger lamdas (1:(k-1)) returned. c jerr = -k => convergence for kth lamda value not reached c after maxit (see above) iterations. c jerr = -10000-k => number of non zero coefficients along path c exceeds nx (see above) at kth lamda value. c c c c least-squares utility routines: c c c uncompress coefficient vectors for all solutions: c c call solns(ni,nx,lmu,ca,ia,nin,b) c c input: c c ni,nx = input to elnet c lmu,ca,ia,nin = output from elnet c c output: c c b(ni,lmu) = all elnet returned solutions in uncompressed format c c c uncompress coefficient vector for particular solution: c c call uncomp(ni,ca,ia,nin,a) c c input: c c ni = total number of predictor variables c ca(nx) = compressed coefficient values for the solution c ia(nx) = pointers to compressed coefficients c nin = number of compressed coefficients for the solution c c output: c c a(ni) = uncompressed coefficient vector c referencing original variables c c c evaluate linear model from compressed coefficients and c uncompressed predictor matrix: c c call modval(a0,ca,ia,nin,n,x,f); c c input: c c a0 = intercept c ca(nx) = compressed coefficient values for a solution c ia(nx) = pointers to compressed coefficients c nin = number of compressed coefficients for solution c n = number of predictor vectors (observations) c x(n,ni) = full (uncompressed) predictor matrix c c output: c c f(n) = model predictions c c c evaluate linear model from compressed coefficients and c compressed predictor matrix: c c call cmodval(a0,ca,ia,nin,x,ix,jx,n,f); c c input: c c a0 = intercept c ca(nx) = compressed coefficient values for a solution c ia(nx) = pointers to compressed coefficients c nin = number of compressed coefficients for solution c x, ix, jx = predictor matrix in compressed sparse row format c n = number of predictor vectors (observations) c c output: c c f(n) = model predictions c c c c c Multiple response c elastic net with squared-error loss c c dense predictor matrix: c c call multelnet(parm,no,ni,nr,x,y,w,jd,vp,cl,ne,nx,nlam,flmin,ulam,thr,isd, c jsd,intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr) c c x(no,ni) = predictor data matrix flat file (overwritten) c c c sparse predictor matrix: c c call multspelnet(parm,no,ni,nr,x,ix,jx,y,w,jd,vp,cl,ne,nx,nlam,flmin,ulam,thr, c isd,jsd,intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr) c c x, ix, jx = predictor data matrix in compressed sparse row format c c other inputs: c c nr = number of response variables c y(no,nr) = response data matrix (overwritten) c jsd = response variable standardization flag c jsd = 0 => regression using original response variables c jsd = 1 => regression using standardized response variables c Note: output solutions always reference original c variables locations and scales. c all other inputs same as elnet/spelnet above c c output: c c a0(nr,lmu) = intercept values for each solution c ca(nx,nr,lmu) = compressed coefficient values for each solution c all other outputs same as elnet/spelnet above c (jerr = 90000 => bounds adjustment non convergence) c c c c multiple response least-squares utility routines: c c c uncompress coefficient matrix for all solutions: c c call multsolns(ni,nx,nr,lmu,ca,ia,nin,b) c c input: c c ni,nx,nr = input to multelnet c lmu,ca,ia,nin = output from multelnet c c output: c c b(ni,nr,lmu) = all multelnet returned solutions in uncompressed format c c c uncompress coefficient matrix for particular solution: c c call multuncomp(ni,nr,nx,ca,ia,nin,a) c c input: c c ni,nr,nx = input to multelnet c ca(nx,nr) = compressed coefficient values for the solution c ia(nx) = pointers to compressed coefficients c nin = number of compressed coefficients for the solution c c output: c c a(ni,nr) = uncompressed coefficient matrix c referencing original variables c c c evaluate linear model from compressed coefficients and c uncompressed predictor matrix: c c call multmodval(nx,nr,a0,ca,ia,nin,n,x,f); c c input: c c nx,nr = input to multelnet c a0(nr) = intercepts c ca(nx,nr) = compressed coefficient values for a solution c ia(nx) = pointers to compressed coefficients c nin = number of compressed coefficients for solution c n = number of predictor vectors (observations) c x(n,ni) = full (uncompressed) predictor matrix c c output: c c f(nr,n) = model predictions c c c evaluate linear model from compressed coefficients and c compressed predictor matrix: c c call multcmodval(nx,nr,a0,ca,ia,nin,x,ix,jx,n,f); c c input: c c nx,nr = input to multelnet c a0(nr) = intercepts c ca(nx,nr) = compressed coefficient values for a solution c ia(nx) = pointers to compressed coefficients c nin = number of compressed coefficients for solution c x, ix, jx = predictor matrix in compressed sparse row format c n = number of predictor vectors (observations) c c output: c c f(nr,n) = model predictions c c c c c Symmetric binomial/multinomial logistic elastic net c c c dense predictor matrix: c c call lognet (parm,no,ni,nc,x,y,o,jd,vp,cl,ne,nx,nlam,flmin,ulam,thr,isd, c intr,maxit,kopt,lmu,a0,ca,ia,nin,dev0,fdev,alm,nlp,jerr) c c x(no,ni) = predictor data matrix flat file (overwritten) c c c sparse predictor matrix: c c call splognet (parm,no,ni,nc,x,ix,jx,y,o,jd,vp,cl,ne,nx,nlam,flmin, c ulam,thr,isd,intr,maxit,kopt,lmu,a0,ca,ia,nin,dev0,fdev,alm,nlp,jerr) c c x, ix, jx = predictor data matrix in compressed sparse row format c c c other inputs: c c parm,no,ni,jd,vp,cl,ne,nx,nlam,flmin,ulam,thr,isd,intr,maxit c = same as elnet above. c c nc = number of classes (distinct outcome values) c nc=1 => binomial two-class logistic regression c (all output references class 1) c y(no,max(2,nc)) = number of each class at each design point c entries may have fractional values or all be zero (overwritten) c o(no,nc) = observation off-sets for each class c kopt = optimization flag c kopt = 0 => Newton-Raphson (recommended) c kpot = 1 => modified Newton-Raphson (sometimes faster) c kpot = 2 => nonzero coefficients same for each class (nc > 1) c c c output: c c lmu,ia,nin,alm,nlp = same as elent above c c a0(nc,lmu) = intercept values for each class at each solution c ca(nx,nc,lmu) = compressed coefficient values for each class at c each solution c dev0 = null deviance (intercept only model) c fdev(lmu) = fraction of devience explained by each solution c jerr = error flag c jerr = 0 => no error c jerr > 0 => fatal error - no output returned c jerr < 7777 => memory allocation error c jerr = 7777 => all used predictors have zero variance c jerr = 8000 + k => null probability < 1.0e-5 for class k c jerr = 9000 + k => null probability for class k c > 1.0 - 1.0e-5 c jerr = 10000 => maxval(vp) <= 0.0 c jerr = 90000 => bounds adjustment non convergence C jerr < 0 => non fatal error - partial output: c Solutions for larger lamdas (1:(k-1)) returned. c jerr = -k => convergence for kth lamda value not reached c after maxit (see above) iterations. c jerr = -10000-k => number of non zero coefficients along path c exceeds nx (see above) at kth lamda value. c jerr = -20000-k => max(p*(1-p)) < 1.0e-6 at kth lamda value. c o(no,nc) = training data values for last (lmu_th) solution linear c combination. c c c c logistic/multinomial utilitity routines: c c c uncompress coefficient vectors for all solutions: c c call lsolns(ni,nx,nc,lmu,ca,ia,nin,b) c c input: c c ni,nx,nc = input to lognet c lmu,ca,ia,nin = output from lognet c c output: c c b(ni,nc,lmu) = all lognet returned solutions in uncompressed format c c c uncompress coefficient vector for particular solution: c c call luncomp(ni,nx,nc,ca,ia,nin,a) c c input: c c ni, nx, nc = same as above c ca(nx,nc) = compressed coefficient values (for each class) c ia(nx) = pointers to compressed coefficients c nin = number of compressed coefficients c c output: c c a(ni,nc) = uncompressed coefficient vectors c referencing original variables c c c evaluate linear model from compressed coefficients and c uncompressed predictor vectors: c c call lmodval(nt,x,nc,nx,a0,ca,ia,nin,ans); c c input: c c nt = number of observations c x(nt,ni) = full (uncompressed) predictor vectors c nc, nx = same as above c a0(nc) = intercepts c ca(nx,nc) = compressed coefficient values (for each class) c ia(nx) = pointers to compressed coefficients c nin = number of compressed coefficients c c output: c c ans(nc,nt) = model predictions c c c evaluate linear model from compressed coefficients and c compressed predictor matrix: c c call lcmodval(nc,nx,a0,ca,ia,nin,x,ix,jx,n,f); c c input: c c nc, nx = same as above c a0(nc) = intercept c ca(nx,nc) = compressed coefficient values for a solution c ia(nx) = pointers to compressed coefficients c nin = number of compressed coefficients for solution c x, ix, jx = predictor matrix in compressed sparse row format c n = number of predictor vectors (observations) c c output: c c f(nc,n) = model predictions c c c c c Poisson elastic net c c c dense predictor matrix: c c call fishnet (parm,no,ni,x,y,o,w,jd,vp,cl,ne,nx,nlam,flmin,ulam,thr, c isd,intr,maxit,lmu,a0,ca,ia,nin,dev0,fdev,alm,nlp,jerr) c c x(no,ni) = predictor data matrix flat file (overwritten) c c sparse predictor matrix: c c call spfishnet (parm,no,ni,x,ix,jx,y,o,w,jd,vp,cl,ne,nx,nlam,flmin,ulam,thr, c isd,intr,maxit,lmu,a0,ca,ia,nin,dev0,fdev,alm,nlp,jerr) c c x, ix, jx = predictor data matrix in compressed sparse row format c c other inputs: c c y(no) = observation response counts c o(no) = observation off-sets c parm,no,ni,w,jd,vp,cl,ne,nx,nlam,flmin,ulam,thr,isd,intr,maxit c = same as elnet above c c output: c c lmu,a0,ca,ia,nin,alm = same as elnet above c dev0,fdev = same as lognet above c nlp = total number of passes over predictor variables c jerr = error flag c jerr = 0 => no error c jerr > 0 => fatal error - no output returned c jerr < 7777 => memory allocation error c jerr = 7777 => all used predictors have zero variance c jerr = 8888 => negative response count y values c jerr = 9999 => no positive observations weights c jerr = 10000 => maxval(vp) <= 0.0 C jerr < 0 => non fatal error - partial output: c Solutions for larger lamdas (1:(k-1)) returned. c jerr = -k => convergence for kth lamda value not reached c after maxit (see above) iterations. c jerr = -10000-k => number of non zero coefficients along path c exceeds nx (see above) at kth lamda value. c o(no) = training data values for last (lmu_th) solution linear c combination. c c c Poisson utility routines: c c c same as elnet above: c c call solns(ni,nx,lmu,ca,ia,nin,b) c call uncomp(ni,ca,ia,nin,a) c call modval(a0,ca,ia,nin,n,x,f); c call cmodval(a0,ca,ia,nin,x,ix,jx,n,f); c c compute deviance for given uncompressed data and set of uncompressed c solutions c c call deviance(no,ni,x,y,o,w,nsol,a0,a,flog,jerr) c c input: c c no = number of observations c ni = number of predictor variables c x(no,ni) = predictor data matrix flat file c y(no) = observation response counts c o(no) = observation off-sets c w(no)= observation weights c nsol = number of solutions c a0(nsol) = intercept for each solution c a(ni,nsol) = solution coefficient vectors (uncompressed) c c output: c c flog(nsol) = respective deviance values minus null deviance c jerr = error flag - see above c c c compute deviance for given compressed data and set of uncompressed solutions c c call spdeviance(no,ni,x,ix,jx,y,o,w,nsol,a0,a,flog,jerr) c c input: c c no = number of observations c ni = number of predictor variables c x, ix, jx = predictor data matrix in compressed sparse row format c y(no) = observation response counts c o(no) = observation off-sets c w(no)= observation weights c nsol = number of solutions c a0(nsol) = intercept for each solution c a(ni,nsol) = solution coefficient vectors (uncompressed) c c output c c flog(nsol) = respective deviance values minus null deviance c jerr = error flag - see above c c c compute deviance for given compressed data and compressed solutions c c call cspdeviance(no,x,ix,jx,y,o,w,nx,lmu,a0,ca,ia,nin,flog,jerr) c c input: c c no = number of observations c x, ix, jx = predictor data matrix in compressed sparse row format c y(no) = observation response counts c o(no) = observation off-sets c w(no)= observation weights c nx = input to spfishnet c lmu,a0(lmu),ca(nx,lmu),ia(nx),nin(lmu) = output from spfishnet c c output c c flog(lmu) = respective deviance values minus null deviance c jerr = error flag - see above c c c c Elastic net with Cox proportional hazards model c c c dense predictor matrix: c c call coxnet (parm,no,ni,x,y,d,o,w,jd,vp,cl,ne,nx,nlam,flmin,ulam,thr, c maxit,isd,lmu,ca,ia,nin,dev0,fdev,alm,nlp,jerr) c c input: c c x(no,ni) = predictor data matrix flat file (overwritten) c y(no) = observation times c d(no) = died/censored indicator c d(i)=0.0 => y(i) = censoring time c d(i)=1.0 => y(i) = death time c o(no) = observation off-sets c parm,no,ni,w,jd,vp,cl,ne,nx,nlam,flmin,ulam,thr,maxit c = same as fishnet above c c output: c c lmu,ca,ia,nin,dev0,fdev,alm,nlp = same as fishnet above c jerr = error flag c jerr = 0 => no error - output returned c jerr > 0 => fatal error - no output returned c jerr < 7777 => memory allocation error c jerr = 7777 => all used predictors have zero variance c jerr = 8888 => all observations censored (d(i)=0.0) c jerr = 9999 => no positive observations weights c jerr = 10000 => maxval(vp) <= 0.0 c jerr = 20000, 30000 => initialization numerical error C jerr < 0 => non fatal error - partial output: c Solutions for larger lamdas (1:(k-1)) returned. c jerr = -k => convergence for kth lamda value not reached c after maxit (see above) iterations. c jerr = -10000-k => number of non zero coefficients along path c exceeds nx (see above) at kth lamda value. c jerr = -30000-k => numerical error at kth lambda value c o(no) = training data values for last (lmu_th) solution linear c combination. c c c c coxnet utility routines: c c c same as elnet above: c c call solns(ni,nx,lmu,ca,ia,nin,b) c call uncomp(ni,ca,ia,nin,a) c c c evaluate linear model from compressed coefficients and c uncompressed predictor matrix: c c call cxmodval(ca,ia,nin,n,x,f); c c input: c c ca(nx) = compressed coefficient values for a solution c ia(nx) = pointers to compressed coefficients c nin = number of compressed coefficients for solution c n = number of predictor vectors (observations) c x(n,ni) = full (uncompressed) predictor matrix c c output: c c f(n) = model predictions c c c compute log-likelihood for given data set and vectors of coefficients c c call loglike(no,ni,x,y,d,o,w,nvec,a,flog,jerr) c c input: c c no = number of observations c ni = number of predictor variables c x(no,ni) = predictor data matrix flat file c y(no) = observation times c d(no) = died/censored indicator c d(i)=0.0 => y(i) = censoring time c d(i)=1.0 => y(i) = death time c o(no) = observation off-sets c w(no)= observation weights c nvec = number of coefficient vectors c a(ni,nvec) = coefficient vectors (uncompressed) c c output c c flog(nvec) = respective log-likelihood values c jerr = error flag - see coxnet above c c c c c Changing internal parameter values c c c call chg_fract_dev(fdev) c fdev = minimum fractional change in deviance for stopping path c default = 1.0e-5 c c call chg_dev_max(devmax) c devmax = maximum fraction of explained deviance for stopping path c default = 0.999 c c call chg_min_flmin(eps) c eps = minimum value of flmin (see above). default= 1.0e-6 c c call chg_big(big) c big = large floating point number. default = 9.9e35 c c call chg_min_lambdas(mnlam) c mnlam = minimum number of path points (lambda values) allowed c default = 5 c c call chg_min_null_prob(pmin) c pmin = minimum null probability for any class. default = 1.0e-9 c c call chg _max_exp(exmx) c exmx = maximum allowed exponent. default = 250.0 c c call chg_bnorm(prec,mxit) c prec = convergence threshold for multi response bounds adjustment c solution. default = 1.0e-10. c mxit = maximum iterations for multiresponse bounds adjustment solution c default = 100. c c c Obtain current internal parameter values c c call get_int_parms(fdev,eps,big,mnlam,devmax,pmin,exmx,itrace) c call get_bnorm(prec,mxit); c c c " subroutine get_int_parms(sml,eps,big,mnlam,rsqmax,pmin,exmx,itrace); implicit double precision(a-h,o-z); data sml0,eps0,big0,mnlam0,rsqmax0,pmin0,exmx0,itrace0 /1.0d-5,1.0d-6,9.9d35,5,0.999,1.0d-9,250.0,0/; sml=sml0; eps=eps0; big=big0; mnlam=mnlam0; rsqmax=rsqmax0; pmin=pmin0; exmx=exmx0; itrace=itrace0; return; entry chg_fract_dev(arg); sml0=arg; return; entry chg_dev_max(arg); rsqmax0=arg; return; entry chg_min_flmin(arg); eps0=arg; return; entry chg_big(arg); big0=arg; return; entry chg_min_lambdas(irg); mnlam0=irg; return; entry chg_min_null_prob(arg); pmin0=arg; return; entry chg_max_exp(arg); exmx0=arg; return; entry chg_itrace(irg); itrace0=irg; return; end; subroutine elnet(ka,parm,no,ni,x,y,w,jd,vp,cl,ne,nx,nlam, flmin,ulam,thr,isd,intr,maxit, lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(no,ni),y(no),w(no),vp(ni),ca(nx,nlam),cl(2,ni); double precision ulam(nlam),a0(nlam),rsq(nlam),alm(nlam); integer jd(*),ia(nx),nin(nlam); %fortran double precision, dimension (:), allocatable :: vq; %mortran if maxval(vp).le.0.0 < jerr=10000; return;> allocate(vq(1:ni),stat=jerr); if(jerr.ne.0) return; vq=max(0d0,vp); vq=vq*ni/sum(vq); if ka.eq.1 < call elnetu (parm,no,ni,x,y,w,jd,vq,cl,ne,nx,nlam,flmin,ulam,thr,isd,intr,maxit, lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr); > else < call elnetn (parm,no,ni,x,y,w,jd,vq,cl,ne,nx,nlam,flmin,ulam,thr,isd,intr,maxit, lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr); > deallocate(vq); return; end; subroutine elnetu(parm,no,ni,x,y,w,jd,vp,cl,ne,nx,nlam, flmin,ulam,thr,isd,intr,maxit, lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(no,ni),y(no),w(no),vp(ni),ulam(nlam),cl(2,ni); double precision ca(nx,nlam),a0(nlam),rsq(nlam),alm(nlam); integer jd(*),ia(nx),nin(nlam); %fortran double precision, dimension (:), allocatable :: xm,xs,g,xv,vlam integer, dimension (:), allocatable :: ju %mortran allocate(g(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xs(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ju(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xv(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(vlam(1:nlam),stat=jerr); if(jerr.ne.0) return; call chkvars(no,ni,x,ju); if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0; if maxval(ju).le.0 < jerr=7777; return;> call standard(no,ni,x,y,w,isd,intr,ju,g,xm,xs,ym,ys,xv,jerr); if(jerr.ne.0) return; cl=cl/ys; if isd.gt.0 < > if(flmin.ge.1.0) vlam=ulam/ys; call elnet1(parm,ni,ju,vp,cl,g,no,ne,nx,x,nlam,flmin,vlam,thr,maxit,xv, lmu,ca,ia,nin,rsq,alm,nlp,jerr); if(jerr.gt.0) return; a0(k)=0.0; if(intr.ne.0) a0(k)=ym-dot_product(ca(1:nk,k),xm(ia(1:nk))); > deallocate(xm,xs,g,ju,xv,vlam); return; end; subroutine standard(no,ni,x,y,w,isd,intr,ju,g,xm,xs,ym,ys,xv,jerr); implicit double precision(a-h,o-z); double precision x(no,ni),y(no),w(no),g(ni),xm(ni),xs(ni),xv(ni); integer ju(ni); %fortran double precision, dimension (:), allocatable :: v %mortran allocate(v(1:no),stat=jerr); if(jerr.ne.0) return; w=w/sum(w); v=sqrt(w); if intr.eq.0 < ym=0.0; y=v*y; ys=sqrt(dot_product(y,y)-dot_product(v,y)**2); y=y/ys; else < xs(j)=1.0;> > > else < if isd.eq.0 < xs=1.0;> else < xv=1.0; > ym=dot_product(w,y); y=v*(y-ym); ys=sqrt(dot_product(y,y)); y=y/ys; > g=0.0; deallocate(v); return; end; subroutine elnet1(beta,ni,ju,vp,cl,g,no,ne,nx,x,nlam,flmin,ulam,thr,maxit,xv, lmu,ao,ia,kin,rsqo,almo,nlp,jerr); implicit double precision(a-h,o-z); double precision vp(ni),g(ni),x(no,ni),ulam(nlam),ao(nx,nlam); double precision rsqo(nlam),almo(nlam),xv(ni); double precision cl(2,ni); integer ju(ni),ia(nx),kin(nlam); %fortran double precision, dimension (:), allocatable :: a,da integer, dimension (:), allocatable :: mm double precision, dimension (:,:), allocatable :: c allocate(c(1:ni,1:nx),stat=jerr) if(jerr.ne.0) return; %mortran call get_int_parms(sml,eps,big,mnlam,rsqmax,pmin,exmx,itrace); allocate(a(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(mm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(da(1:ni),stat=jerr); if(jerr.ne.0) return; bta=beta; omb=1.0-bta; "Begin: added by Naras" alm=0.0; alf=1.0; "End: added by Naras" if flmin.lt.1.0 < eqs=max(eps,flmin); alf=eqs**(1.0/(nlam-1));> rsq=0.0; a=0.0; mm=0; /nlp,nin/=0; iz=0; mnl=min(mnlam,nlam); elseif m.gt.2 < alm=alm*alf;> elseif m.eq.1 < alm=big;> else < alm=0.0; alm=alf*alm/max(bta,1.0d-3); > dem=alm*omb; ab=alm*bta; rsq0=rsq; jz=1; loop < if(iz*jz.ne.0) go to :b:; nlp=nlp+1; dlx=0.0; if j.eq.k < c(j,nin)=xv(j); next;> c(j,nin)=dot_product(x(:,j),x(:,k)); > mm(k)=nin; ia(nin)=k; > del=a(k)-ak; rsq=rsq+del*(2.0*g(k)-del*xv(k)); dlx=max(xv(k)*del**2,dlx); > if(dlx.lt.thr) exit; if(nin.gt.nx) exit; if nlp.gt.maxit < jerr=-m; return;> :b: iz=1; da(1:nin)=a(ia(1:nin)); loop < nlp=nlp+1; dlx=0.0; > if(dlx.lt.thr) exit; if nlp.gt.maxit < jerr=-m; return;> > da(1:nin)=a(ia(1:nin))-da(1:nin); jz=0; > if nin.gt.nx < jerr=-10000-m; exit;> if(nin.gt.0) ao(1:nin,m)=a(ia(1:nin)); kin(m)=nin; rsqo(m)=rsq; almo(m)=alm; lmu=m; if(m.lt.mnl) next; if(flmin.ge.1.0) next; me=0; if(me.gt.ne) exit; if(rsq-rsq0.lt.sml*rsq) exit; if(rsq.gt.rsqmax) exit; > deallocate(a,mm,c,da); return; end; subroutine elnetn(parm,no,ni,x,y,w,jd,vp,cl,ne,nx,nlam,flmin,ulam,thr,isd, intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision vp(ni),x(no,ni),y(no),w(no),ulam(nlam),cl(2,ni); double precision ca(nx,nlam),a0(nlam),rsq(nlam),alm(nlam); integer jd(*),ia(nx),nin(nlam); %fortran double precision, dimension (:), allocatable :: xm,xs,xv,vlam integer, dimension (:), allocatable :: ju %mortran allocate(xm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xs(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ju(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xv(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(vlam(1:nlam),stat=jerr); if(jerr.ne.0) return; call chkvars(no,ni,x,ju); if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0; if maxval(ju).le.0 < jerr=7777; return;> call standard1(no,ni,x,y,w,isd,intr,ju,xm,xs,ym,ys,xv,jerr); if(jerr.ne.0) return; cl=cl/ys; if isd.gt.0 < > if(flmin.ge.1.0) vlam=ulam/ys; call elnet2(parm,ni,ju,vp,cl,y,no,ne,nx,x,nlam,flmin,vlam,thr,maxit,xv, lmu,ca,ia,nin,rsq,alm,nlp,jerr); if(jerr.gt.0) return; a0(k)=0.0; if(intr.ne.0) a0(k)=ym-dot_product(ca(1:nk,k),xm(ia(1:nk))); > deallocate(xm,xs,ju,xv,vlam); return; end; subroutine standard1(no,ni,x,y,w,isd,intr,ju,xm,xs,ym,ys,xv,jerr); implicit double precision(a-h,o-z); double precision x(no,ni),y(no),w(no),xm(ni),xs(ni),xv(ni); integer ju(ni); %fortran double precision, dimension (:), allocatable :: v %mortran allocate(v(1:no),stat=jerr); if(jerr.ne.0) return; w=w/sum(w); v=sqrt(w); if intr.eq.0 < ym=0.0; y=v*y; ys=sqrt(dot_product(y,y)-dot_product(v,y)**2); y=y/ys; else < xs(j)=1.0;> > go to :out:; > if isd.eq.0 < xs=1.0;> else < xv=1.0; > ym=dot_product(w,y); y=v*(y-ym); ys=sqrt(dot_product(y,y)); y=y/ys; :out:deallocate(v); return; end; subroutine elnet2(beta,ni,ju,vp,cl,y,no,ne,nx,x,nlam,flmin,ulam,thr,maxit,xv, lmu,ao,ia,kin,rsqo,almo,nlp,jerr); implicit double precision(a-h,o-z); double precision vp(ni),y(no),x(no,ni),ulam(nlam),ao(nx,nlam); double precision rsqo(nlam),almo(nlam),xv(ni); double precision cl(2,ni); integer ju(ni),ia(nx),kin(nlam); %fortran double precision, dimension (:), allocatable :: a,g integer, dimension (:), allocatable :: mm,ix %mortran call get_int_parms(sml,eps,big,mnlam,rsqmax,pmin,exmx,itrace); allocate(a(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(mm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(g(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ix(1:ni),stat=jerr); if(jerr.ne.0) return; bta=beta; omb=1.0-bta; ix=0; "Begin: added by Naras" alf=1.0; "End: added by Naras" if flmin.lt.1.0 < eqs=max(eps,flmin); alf=eqs**(1.0/(nlam-1));> rsq=0.0; a=0.0; mm=0; /nlp,nin/=0; iz=0; mnl=min(mnlam,nlam); alm=0.0; elseif m.gt.2 < alm=alm*alf;> elseif m.eq.1 < alm=big;> else < alm0=0.0; alm0=alm0/max(bta,1.0d-3); alm=alf*alm0; > dem=alm*omb; ab=alm*bta; rsq0=rsq; jz=1; tlam=bta*(2.0*alm-alm0); loop < if(iz*jz.ne.0) go to :b:; :again:nlp=nlp+1; dlx=0.0; del=a(k)-ak; rsq=rsq+del*(2.0*gk-del*xv(k)); y=y-del*x(:,k); dlx=max(xv(k)*del**2,dlx); > if(nin.gt.nx) exit; if dlx.lt.thr < ixx=0; > if(ixx.eq.1) go to :again:; exit; > if nlp.gt.maxit < jerr=-m; return;> :b: iz=1; loop < nlp=nlp+1; dlx=0.0; if(dlx.lt.thr) exit; if nlp.gt.maxit < jerr=-m; return;> > jz=0; > if nin.gt.nx < jerr=-10000-m; exit;> if(nin.gt.0) ao(1:nin,m)=a(ia(1:nin)); kin(m)=nin; rsqo(m)=rsq; almo(m)=alm; lmu=m; if(m.lt.mnl) next; if(flmin.ge.1.0) next; me=0; if(me.gt.ne) exit; if(rsq-rsq0.lt.sml*rsq) exit; if(rsq.gt.rsqmax) exit; > deallocate(a,mm,g,ix); return; end; subroutine chkvars(no,ni,x,ju); implicit double precision(a-h,o-z); double precision x(no,ni); integer ju(ni); > return; end; subroutine uncomp(ni,ca,ia,nin,a); implicit double precision(a-h,o-z); double precision ca(*),a(ni); integer ia(*); a=0.0; if(nin.gt.0) a(ia(1:nin))=ca(1:nin); return; end; subroutine modval(a0,ca,ia,nin,n,x,f); implicit double precision(a-h,o-z); double precision ca(nin),x(n,*),f(n); integer ia(nin); f=a0; if(nin.le.0) return; return; end; subroutine spelnet(ka,parm,no,ni,x,ix,jx,y,w,jd,vp,cl,ne,nx,nlam, flmin,ulam,thr,isd,intr, maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(*),y(no),w(no),vp(ni),ulam(nlam),cl(2,ni); double precision ca(nx,nlam),a0(nlam),rsq(nlam),alm(nlam); integer ix(*),jx(*),jd(*),ia(nx),nin(nlam); %fortran double precision, dimension (:), allocatable :: vq; %mortran if maxval(vp).le.0.0 < jerr=10000; return;> allocate(vq(1:ni),stat=jerr); if(jerr.ne.0) return; vq=max(0d0,vp); vq=vq*ni/sum(vq); if ka.eq.1 < call spelnetu (parm,no,ni,x,ix,jx,y,w,jd,vq,cl,ne,nx,nlam,flmin,ulam,thr,isd, intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr); > else < call spelnetn (parm,no,ni,x,ix,jx,y,w,jd,vq,cl,ne,nx,nlam,flmin,ulam,thr,isd,intr, maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr); > deallocate(vq); return; end; subroutine spelnetu(parm,no,ni,x,ix,jx,y,w,jd,vp,cl,ne,nx,nlam, flmin,ulam,thr,isd,intr, maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(*),y(no),w(no),vp(ni),ulam(nlam),cl(2,ni); double precision ca(nx,nlam),a0(nlam),rsq(nlam),alm(nlam); integer ix(*),jx(*),jd(*),ia(nx),nin(nlam); %fortran double precision, dimension (:), allocatable :: xm,xs,g,xv,vlam integer, dimension (:), allocatable :: ju %mortran allocate(g(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xs(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ju(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xv(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(vlam(1:nlam),stat=jerr); if(jerr.ne.0) return; call spchkvars(no,ni,x,ix,ju); if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0; if maxval(ju).le.0 < jerr=7777; return;> call spstandard(no,ni,x,ix,jx,y,w,ju,isd,intr,g,xm,xs,ym,ys,xv,jerr); if(jerr.ne.0) return; cl=cl/ys; if isd.gt.0 < > if(flmin.ge.1.0) vlam=ulam/ys; call spelnet1(parm,ni,g,no,w,ne,nx,x,ix,jx,ju,vp,cl,nlam,flmin,vlam,thr,maxit, xm,xs,xv,lmu,ca,ia,nin,rsq,alm,nlp,jerr); if(jerr.gt.0) return; a0(k)=0.0; if(intr.ne.0) a0(k)=ym-dot_product(ca(1:nk,k),xm(ia(1:nk))); > deallocate(xm,xs,g,ju,xv,vlam); return; end; subroutine spstandard(no,ni,x,ix,jx,y,w,ju,isd,intr,g,xm,xs,ym,ys,xv,jerr); implicit double precision(a-h,o-z); double precision x(*),y(no),w(no),g(ni),xm(ni),xs(ni),xv(ni); integer ix(*),jx(*),ju(ni); "Start: Naras Edit" jerr = jerr*1; "End: Naras Edit" w=w/sum(w); if intr.eq.0 < ym=0.0; ys=sqrt(dot_product(w,y**2)-dot_product(w,y)**2); y=y/ys; else < xs(j)=1.0;> > > else < if isd.eq.0 < xs=1.0;> else < xv=1.0;> ym=dot_product(w,y); y=y-ym; ys=sqrt(dot_product(w,y**2)); y=y/ys; > g=0.0; return; end; subroutine spelnet1(beta,ni,g,no,w,ne,nx,x,ix,jx,ju,vp,cl,nlam,flmin,ulam, thr,maxit,xm,xs,xv,lmu,ao,ia,kin,rsqo,almo,nlp,jerr); implicit double precision(a-h,o-z); double precision g(ni),vp(ni),x(*),ulam(nlam),w(no); double precision ao(nx,nlam),rsqo(nlam),almo(nlam); double precision xm(ni),xs(ni),xv(ni),cl(2,ni); integer ix(*),jx(*),ju(ni),ia(nx),kin(nlam); %fortran double precision, dimension (:), allocatable :: a,da integer, dimension (:), allocatable :: mm double precision, dimension (:,:), allocatable :: c allocate(c(1:ni,1:nx),stat=jerr) if(jerr.ne.0) return; %mortran call get_int_parms(sml,eps,big,mnlam,rsqmax,pmin,exmx,itrace); allocate(a(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(mm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(da(1:ni),stat=jerr); if(jerr.ne.0) return; bta=beta; omb=1.0-bta; "Begin: added by Naras" alm=0.0; alf=1.0; "End: added by Naras" if flmin.lt.1.0 < eqs=max(eps,flmin); alf=eqs**(1.0/(nlam-1));> rsq=0.0; a=0.0; mm=0; /nlp,nin/=0; iz=0; mnl=min(mnlam,nlam); elseif m.gt.2 < alm=alm*alf;> elseif m.eq.1 < alm=big;> else < alm=0.0; alm=alf*alm/max(bta,1.0d-3); > dem=alm*omb; ab=alm*bta; rsq0=rsq; jz=1; loop < if(iz*jz.ne.0) go to :b:; nlp=nlp+1; dlx=0.0; if j.eq.k < c(j,nin)=xv(j); next;> c(j,nin)= (row_prod(j,k,ix,jx,x,w)-xm(j)*xm(k))/(xs(j)*xs(k)); > mm(k)=nin; ia(nin)=k; > del=a(k)-ak; rsq=rsq+del*(2.0*g(k)-del*xv(k)); dlx=max(xv(k)*del**2,dlx); > if(dlx.lt.thr) exit; if(nin.gt.nx) exit; if nlp.gt.maxit < jerr=-m; return;> :b: iz=1; da(1:nin)=a(ia(1:nin)); loop < nlp=nlp+1; dlx=0.0; > if(dlx.lt.thr) exit; if nlp.gt.maxit < jerr=-m; return;> > da(1:nin)=a(ia(1:nin))-da(1:nin); jz=0; > if nin.gt.nx < jerr=-10000-m; exit;> if(nin.gt.0) ao(1:nin,m)=a(ia(1:nin)); kin(m)=nin; rsqo(m)=rsq; almo(m)=alm; lmu=m; if(m.lt.mnl) next; if(flmin.ge.1.0) next; me=0; if(me.gt.ne) exit; if(rsq-rsq0.lt.sml*rsq) exit; if(rsq.gt.rsqmax) exit; > deallocate(a,mm,c,da); return; end; subroutine spelnetn(parm,no,ni,x,ix,jx,y,w,jd,vp,cl,ne,nx,nlam,flmin,ulam, thr,isd,intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(*),vp(ni),y(no),w(no),ulam(nlam),cl(2,ni); double precision ca(nx,nlam),a0(nlam),rsq(nlam),alm(nlam); integer ix(*),jx(*),jd(*),ia(nx),nin(nlam); %fortran double precision, dimension (:), allocatable :: xm,xs,xv,vlam integer, dimension (:), allocatable :: ju %mortran allocate(xm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xs(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ju(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xv(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(vlam(1:nlam),stat=jerr); if(jerr.ne.0) return; call spchkvars(no,ni,x,ix,ju); if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0; if maxval(ju).le.0 < jerr=7777; return;> call spstandard1(no,ni,x,ix,jx,y,w,ju,isd,intr,xm,xs,ym,ys,xv,jerr); if(jerr.ne.0) return; cl=cl/ys; if isd.gt.0 < > if(flmin.ge.1.0) vlam=ulam/ys; call spelnet2(parm,ni,y,w,no,ne,nx,x,ix,jx,ju,vp,cl,nlam,flmin,vlam,thr,maxit, xm,xs,xv,lmu,ca,ia,nin,rsq,alm,nlp,jerr); if(jerr.gt.0) return; a0(k)=0.0; if(intr.ne.0) a0(k)=ym-dot_product(ca(1:nk,k),xm(ia(1:nk))); > deallocate(xm,xs,ju,xv,vlam); return; end; subroutine spstandard1(no,ni,x,ix,jx,y,w,ju,isd,intr,xm,xs,ym,ys,xv,jerr); implicit double precision(a-h,o-z); double precision x(*),y(no),w(no),xm(ni),xs(ni),xv(ni); integer ix(*),jx(*),ju(ni); "Start: Naras Edit" jerr = jerr*1; "End: Naras Edit" w=w/sum(w); if intr.eq.0 < ym=0.0; ys=sqrt(dot_product(w,y**2)-dot_product(w,y)**2); y=y/ys; else < xs(j)=1.0;> > return; > if isd.eq.0 < xs=1.0;> else < xv=1.0;> ym=dot_product(w,y); y=y-ym; ys=sqrt(dot_product(w,y**2)); y=y/ys; return; end; subroutine spelnet2(beta,ni,y,w,no,ne,nx,x,ix,jx,ju,vp,cl,nlam,flmin,ulam, thr,maxit,xm,xs,xv,lmu,ao,ia,kin,rsqo,almo,nlp,jerr); implicit double precision(a-h,o-z); double precision y(no),w(no),x(*),vp(ni),ulam(nlam),cl(2,ni); double precision ao(nx,nlam),rsqo(nlam),almo(nlam),xm(ni),xs(ni),xv(ni); integer ix(*),jx(*),ju(ni),ia(nx),kin(nlam); %fortran double precision, dimension (:), allocatable :: a,g integer, dimension (:), allocatable :: mm,iy %mortran call get_int_parms(sml,eps,big,mnlam,rsqmax,pmin,exmx,itrace); allocate(a(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(mm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(g(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(iy(1:ni),stat=jerr); if(jerr.ne.0) return; bta=beta; omb=1.0-bta; alm=0.0; iy=0; "Begin: added by Naras" alf=1.0; "End: added by Naras" if flmin.lt.1.0 < eqs=max(eps,flmin); alf=eqs**(1.0/(nlam-1));> rsq=0.0; a=0.0; mm=0; o=0.0; /nlp,nin/=0; iz=0; mnl=min(mnlam,nlam); elseif m.gt.2 < alm=alm*alf;> elseif m.eq.1 < alm=big;> else < alm0=0.0; alm0=alm0/max(bta,1.0d-3); alm=alf*alm0; > dem=alm*omb; ab=alm*bta; rsq0=rsq; jz=1; tlam=bta*(2.0*alm-alm0); loop < if(iz*jz.ne.0) go to :b:; :again:nlp=nlp+1; dlx=0.0; del=a(k)-ak; rsq=rsq+del*(2.0*gk-del*xv(k)); y(jx(jb:je))=y(jx(jb:je))-del*x(jb:je)/xs(k); o=o+del*xm(k)/xs(k); dlx=max(xv(k)*del**2,dlx); > if(nin.gt.nx) exit; if dlx.lt.thr < ixx=0; > if(ixx.eq.1) go to :again:; exit; > if nlp.gt.maxit < jerr=-m; return;> :b: iz=1; loop < nlp=nlp+1; dlx=0.0; if(dlx.lt.thr) exit; if nlp.gt.maxit < jerr=-m; return;> > jz=0; > if nin.gt.nx < jerr=-10000-m; exit;> if(nin.gt.0) ao(1:nin,m)=a(ia(1:nin)); kin(m)=nin; rsqo(m)=rsq; almo(m)=alm; lmu=m; if(m.lt.mnl) next; if(flmin.ge.1.0) next; me=0; if(me.gt.ne) exit; if(rsq-rsq0.lt.sml*rsq) exit; if(rsq.gt.rsqmax) exit; > deallocate(a,mm,g,iy); return; end; subroutine spchkvars(no,ni,x,ix,ju); implicit double precision(a-h,o-z); double precision x(*); integer ix(*),ju(ni); > else < t=x(jb); > > return; end; subroutine cmodval(a0,ca,ia,nin,x,ix,jx,n,f); implicit double precision(a-h,o-z); double precision ca(*),x(*),f(n); integer ia(*),ix(*),jx(*); f=a0; return; end; function row_prod(i,j,ia,ja,ra,w); implicit double precision(a-h,o-z); integer ia(*),ja(*); double precision ra(*),w(*); row_prod=dot(ra(ia(i)),ra(ia(j)),ja(ia(i)),ja(ia(j)), ia(i+1)-ia(i),ia(j+1)-ia(j),w); return; end; function dot(x,y,mx,my,nx,ny,w); implicit double precision(a-h,o-z); double precision x(*),y(*),w(*); integer mx(*),my(*); /i,j/=1; s=0.0; loop < until mx(i).ge.my(j) < i=i+1; if(i.gt.nx) go to :done:;> if(mx(i).eq.my(j)) go to :equal:; until my(j).ge.mx(i) < j=j+1; if(j.gt.ny) go to :done:;> if(mx(i).eq.my(j)) go to :equal:; next; :equal:s=s+w(mx(i))*x(i)*y(j); i=i+1; if(i.gt.nx) exit; j=j+1; if(j.gt.ny) exit; > :done: dot=s; return; end; subroutine lognet(parm,no,ni,nc,x,y,g,jd,vp,cl,ne,nx,nlam,flmin,ulam,thr, isd,intr,maxit,kopt,lmu,a0,ca,ia,nin,dev0,dev,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(no,ni),y(no,max(2,nc)),g(no,nc),vp(ni),ulam(nlam); double precision ca(nx,nc,nlam),a0(nc,nlam),dev(nlam),alm(nlam),cl(2,ni); integer jd(*),ia(nx),nin(nlam); %fortran double precision, dimension (:), allocatable :: xm,xs,ww,vq,xv integer, dimension (:), allocatable :: ju %mortran if maxval(vp).le.0.0 < jerr=10000; return;> allocate(ww(1:no),stat=jerr); if(jerr.ne.0) return; allocate(ju(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(vq(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xm(1:ni),stat=jerr); if(jerr.ne.0) return; if kopt.eq.2 < allocate(xv(1:ni),stat=jerr); if(jerr.ne.0) return;> if isd.gt.0 < allocate(xs(1:ni),stat=jerr); if(jerr.ne.0) return;> call chkvars(no,ni,x,ju); if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0; if maxval(ju).le.0 < jerr=7777; return;> vq=max(0d0,vp); vq=vq*ni/sum(vq); sw=sum(ww); ww=ww/sw; if nc.eq.1 < call lstandard1(no,ni,x,ww,ju,isd,intr,xm,xs); if isd.gt.0 < > call lognet2n(parm,no,ni,x,y(:,1),g(:,1),ww,ju,vq,cl,ne,nx,nlam,flmin,ulam, thr,isd,intr,maxit,kopt,lmu,a0,ca,ia,nin,dev0,dev,alm,nlp,jerr); > elseif kopt.eq.2 < call multlstandard1(no,ni,x,ww,ju,isd,intr,xm,xs,xv); if isd.gt.0 < > call multlognetn(parm,no,ni,nc,x,y,g,ww,ju,vq,cl,ne,nx,nlam,flmin,ulam,thr, intr,maxit,xv,lmu,a0,ca,ia,nin,dev0,dev,alm,nlp,jerr); > else < call lstandard1(no,ni,x,ww,ju,isd,intr,xm,xs); if isd.gt.0 < > call lognetn(parm,no,ni,nc,x,y,g,ww,ju,vq,cl,ne,nx,nlam,flmin,ulam,thr, isd,intr,maxit,kopt,lmu,a0,ca,ia,nin,dev0,dev,alm,nlp,jerr); > if(jerr.gt.0) return; dev0=2.0*sw*dev0; > if intr.eq.0 < a0(ic,k)=0.0;> else < a0(ic,k)=a0(ic,k)-dot_product(ca(1:nk,ic,k),xm(ia(1:nk)));> > > deallocate(ww,ju,vq,xm); if(isd.gt.0) deallocate(xs); if(kopt.eq.2) deallocate(xv); return; end; subroutine lstandard1(no,ni,x,w,ju,isd,intr,xm,xs); implicit double precision(a-h,o-z); double precision x(no,ni),w(no),xm(ni),xs(ni); integer ju(ni); if intr.eq.0 < > return; > > return; end; subroutine multlstandard1(no,ni,x,w,ju,isd,intr,xm,xs,xv); implicit double precision(a-h,o-z); double precision x(no,ni),w(no),xm(ni),xs(ni),xv(ni); integer ju(ni); if intr.eq.0 < > return; > > return; end; subroutine lognet2n(parm,no,ni,x,y,g,w,ju,vp,cl,ne,nx,nlam,flmin,ulam,shri, isd,intr,maxit,kopt,lmu,a0,a,m,kin,dev0,dev,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(no,ni),y(no),g(no),w(no),vp(ni),ulam(nlam),cl(2,ni); double precision a(nx,nlam),a0(nlam),dev(nlam),alm(nlam); integer ju(ni),m(nx),kin(nlam); %fortran double precision, dimension (:), allocatable :: b,bs,v,r,xv,q,ga integer, dimension (:), allocatable :: mm,ixx %mortran call get_int_parms(sml,eps,big,mnlam,devmax,pmin,exmx,itrace); allocate(b(0:ni),stat=jerr); if(jerr.ne.0) return; allocate(xv(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ga(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(bs(0:ni),stat=jerr); if(jerr.ne.0) return; allocate(mm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ixx(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(r(1:no),stat=jerr); if(jerr.ne.0) return; allocate(v(1:no),stat=jerr); if(jerr.ne.0) return; allocate(q(1:no),stat=jerr); if(jerr.ne.0) return; fmax=log(1.0/pmin-1.0); fmin=-fmax; vmin=(1.0+pmin)*pmin*(1.0-pmin); bta=parm; omb=1.0-bta; q0=dot_product(w,y); if q0.le.pmin < jerr=8001; return;> if q0.ge.1.0-pmin < jerr=9001; return;> if(intr.eq.0.0) q0=0.5; ixx=0; al=0.0; bz=0.0; if(intr.ne.0) bz=log(q0/(1.0-q0)); if nonzero(no,g).eq.0 < vi=q0*(1.0-q0); b(0)=bz; v=vi*w; r=w*(y-q0); q=q0; xmz=vi; dev1=-(bz*q0+log(1.0-q0)); > else < b(0)=0.0; if intr.ne.0 < b(0)=azero(no,y,g,w,jerr); if(jerr.ne.0) return;> q=1.0/(1.0+exp(-b(0)-g)); v=w*q*(1.0-q); r=w*(y-q); xmz=sum(v); dev1=-(b(0)*q0+dot_product(w,y*g+log(1.0-q))); > if kopt.gt.0 < if isd.gt.0.and.intr.ne.0 < xv=0.25;> else < > > dev0=dev1; "Begin: added by Naras" alf=1.0; "End: added by Naras" if flmin.lt.1.0 < eqs=max(eps,flmin); alf=eqs**(1.0/(nlam-1));> m=0; mm=0; /nlp,nin/=0; mnl=min(mnlam,nlam); bs=0.0; b(1:ni)=0.0; shr=shri*dev0; elseif ilm.gt.2 < al=al*alf;> elseif ilm.eq.1 < al=big;> else < al0=0.0; al0=al0/max(bta,1.0d-3); al=alf*al0; > al2=al*omb; al1=al*bta; tlam=bta*(2.0*al-al0); :again:continue; loop < bs(0)=b(0); if(nin.gt.0) bs(m(1:nin))=b(m(1:nin)); if kopt.eq.0 < > loop < nlp=nlp+1; dlx=0.0; else < b(k)=max(cl(1,k),min(cl(2,k),sign(au,u)/(xv(k)+vp(k)*al2))); > d=b(k)-bk; if(abs(d).le.0.0) next; dlx=max(dlx,xv(k)*d**2); r=r-d*v*x(:,k); if mm(k).eq.0 < nin=nin+1; if(nin.gt.nx) exit; mm(k)=nin; m(nin)=k; > > if(nin.gt.nx) exit; d=0.0; if(intr.ne.0) d=sum(r)/xmz; if d.ne.0.0 < b(0)=b(0)+d; dlx=max(dlx,xmz*d**2); r=r-d*v;> if(dlx.lt.shr) exit; if nlp.gt.maxit < jerr=-ilm; return;> loop < nlp=nlp+1; dlx=0.0; else < b(k)=max(cl(1,k),min(cl(2,k),sign(au,u)/(xv(k)+vp(k)*al2))); > d=b(k)-bk; if(abs(d).le.0.0) next; dlx=max(dlx,xv(k)*d**2); r=r-d*v*x(:,k); > d=0.0; if(intr.ne.0) d=sum(r)/xmz; if d.ne.0.0 < b(0)=b(0)+d; dlx=max(dlx,xmz*d**2); r=r-d*v;> if(dlx.lt.shr) exit; if nlp.gt.maxit < jerr=-ilm; return;> > > if(nin.gt.nx) exit; elseif fi.gt.fmax < q(i)=1.0;> else < q(i)=1.0/(1.0+exp(-fi));> > v=w*q*(1.0-q); xmz=sum(v); if(xmz.le.vmin) exit; r=w*(y-q); if xmz*(b(0)-bs(0))**2.lt.shr < ix=0; if ix.eq.0 < > if(ix.eq.1) go to :again:; exit; > > > if nin.gt.nx < jerr=-10000-ilm; exit;> if(nin.gt.0) a(1:nin,ilm)=b(m(1:nin)); kin(ilm)=nin; a0(ilm)=b(0); alm(ilm)=al; lmu=ilm; devi=dev2(no,w,y,q,pmin); dev(ilm)=(dev1-devi)/dev0; if(xmz.le.vmin) exit; if(ilm.lt.mnl) next; if(flmin.ge.1.0) next; me=0; if(me.gt.ne) exit; if(dev(ilm).gt.devmax) exit; if(dev(ilm)-dev(ilm-1).lt.sml) exit; > g=log(q/(1.0-q)); deallocate(b,bs,v,r,xv,q,mm,ga,ixx); return; end; function dev2(n,w,y,p,pmin); implicit double precision(a-h,o-z); double precision w(n),y(n),p(n); pmax=1.0-pmin; s=0.0; dev2=s; return; end; function azero(n,y,g,q,jerr); implicit double precision(a-h,o-z); parameter(eps=1.0d-7); double precision y(n),g(n),q(n); %fortran double precision, dimension (:), allocatable :: e,p,w %mortran azero = 0.0; allocate(e(1:n),stat=jerr); if(jerr.ne.0) return; allocate(p(1:n),stat=jerr); if(jerr.ne.0) return; allocate(w(1:n),stat=jerr); if(jerr.ne.0) return; az=0.0; e=exp(-g); qy=dot_product(q,y); p=1.0/(1.0+e); loop < w=q*p*(1.0-p); d=(qy-dot_product(q,p))/sum(w); az=az+d; if(abs(d).lt.eps) exit; ea0=exp(-az); p=1.0/(1.0+ea0*e); > azero=az; deallocate(e,p,w); return; end; subroutine lognetn(parm,no,ni,nc,x,y,g,w,ju,vp,cl,ne,nx,nlam,flmin,ulam,shri, isd,intr,maxit,kopt,lmu,a0,a,m,kin,dev0,dev,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(no,ni),y(no,nc),g(no,nc),w(no),vp(ni),ulam(nlam); double precision a(nx,nc,nlam),a0(nc,nlam),dev(nlam),alm(nlam),cl(2,ni); integer ju(ni),m(nx),kin(nlam); %fortran double precision, dimension (:,:), allocatable :: q double precision, dimension (:), allocatable :: sxp,sxpl double precision, dimension (:), allocatable :: di,v,r,ga double precision, dimension (:,:), allocatable :: b,bs,xv integer, dimension (:), allocatable :: mm,is,ixx allocate(b(0:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(xv(1:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(bs(0:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(q(1:no,1:nc),stat=jerr) if(jerr.ne.0) return %mortran call get_int_parms(sml,eps,big,mnlam,devmax,pmin,exmx,itrace); exmn=-exmx; allocate(r(1:no),stat=jerr); if(jerr.ne.0) return; allocate(v(1:no),stat=jerr); if(jerr.ne.0) return; allocate(mm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(is(1:max(nc,ni)),stat=jerr); if(jerr.ne.0) return; allocate(sxp(1:no),stat=jerr); if(jerr.ne.0) return; allocate(sxpl(1:no),stat=jerr); if(jerr.ne.0) return; allocate(di(1:no),stat=jerr); if(jerr.ne.0) return; allocate(ga(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ixx(1:ni),stat=jerr); if(jerr.ne.0) return; pmax=1.0-pmin; emin=pmin/pmax; emax=1.0/emin; pfm=(1.0+pmin)*pmin; pfx=(1.0-pmin)*pmax; vmin=pfm*pmax; bta=parm; omb=1.0-bta; dev1=0.0; dev0=0.0; if q0.ge.1.0-pmin < jerr =9000+ic; return;> if intr.eq.0 < q0=1.0/nc; b(0,ic)=0.0;> else < b(0,ic)=log(q0); dev1=dev1-q0*b(0,ic);> b(1:ni,ic)=0.0; > if(intr.eq.0) dev1=log(float(nc)); ixx=0; al=0.0; if nonzero(no*nc,g).eq.0 < b(0,:)=b(0,:)-sum(b(0,:))/nc; sxp=0.0; > else < sxp=0.0; if intr.eq.0 < b(0,:)=0.0;> else < call kazero(nc,no,y,g,w,b(0,:),jerr); if(jerr.ne.0) return;> dev1=0.0; sxpl=w*log(sxp); > > dev0=dev0+dev1; if kopt.gt.0 < if isd.gt.0.and.intr.ne.0 < xv=0.25;> else < > > "Begin: added by Naras" alf=1.0; "End: added by Naras" if flmin.lt.1.0 < eqs=max(eps,flmin); alf=eqs**(1.0/(nlam-1));> m=0; mm=0; nin=0; nlp=0; mnl=min(mnlam,nlam); bs=0.0; shr=shri*dev0; ga=0.0; > elseif ilm.gt.2 < al=al*alf;> elseif ilm.eq.1 < al=big;> else < al0=0.0; al0=al0/max(bta,1.0d-3); al=alf*al0; > al2=al*omb; al1=al*bta; tlam=bta*(2.0*al-al0); :again:continue; loop < /ix,jx/=0; ig=0; elseif pic.gt.pfx < pic=1.0; v(i)=0.0;> else < v(i)=w(i)*pic*(1.0-pic); xmz=xmz+v(i);> r(i)=w(i)*(y(i,ic)-pic); > if(xmz.le.vmin) next; ig=1; if kopt.eq.0 < > loop < nlp=nlp+1; dlx=0.0; else < b(k,ic)=max(cl(1,k),min(cl(2,k),sign(au,u)/ (xv(k,ic)+vp(k)*al2))); > d=b(k,ic)-bk; if(abs(d).le.0.0) next; dlx=max(dlx,xv(k,ic)*d**2); r=r-d*v*x(:,k); if mm(k).eq.0 < nin=nin+1; if nin.gt.nx < jx=1; exit;> mm(k)=nin; m(nin)=k; > > if(jx.gt.0) exit; d=0.0; if(intr.ne.0) d=sum(r)/xmz; if d.ne.0.0 < b(0,ic)=b(0,ic)+d; dlx=max(dlx,xmz*d**2); r=r-d*v;> if(dlx.lt.shr) exit; if nlp.gt.maxit < jerr=-ilm; return;> loop < nlp=nlp+1; dlx=0.0; else < b(k,ic)=max(cl(1,k),min(cl(2,k),sign(au,u)/ (xv(k,ic)+vp(k)*al2))); > d=b(k,ic)-bk; if(abs(d).le.0.0) next; dlx=max(dlx,xv(k,ic)*d**2); r=r-d*v*x(:,k); > d=0.0; if(intr.ne.0) d=sum(r)/xmz; if d.ne.0.0 < b(0,ic)=b(0,ic)+d; dlx=max(dlx,xmz*d**2); r=r-d*v; > if(dlx.lt.shr) exit; if nlp.gt.maxit < jerr=-ilm; return;> > > if(jx.gt.0) exit; if(xmz*(b(0,ic)-bs(0,ic))**2.gt.shr) ix=1; if ix.eq.0 < > > > s=-sum(b(0,:))/nc; b(0,:)=b(0,:)+s; di=s; else < s=elc(parm,nc,cl(:,l),b(l,:),is);> b(l,:)=b(l,:)-s; di=di-s*x(:,l); > di=exp(di); sxp=sxp*di; if(jx.gt.0) exit; if(ig.eq.0) exit; if ix.eq.0 < > > if(ix.eq.1) go to :again:; exit; > > if jx.gt.0 < jerr=-10000-ilm; exit;> devi=0.0; > kin(ilm)=nin; alm(ilm)=al; lmu=ilm; dev(ilm)=(dev1-devi)/dev0; if(ig.eq.0) exit; if(ilm.lt.mnl) next; if(flmin.ge.1.0) next; if(nintot(ni,nx,nc,a(1,1,ilm),m,nin,is).gt.ne) exit; if(dev(ilm).gt.devmax) exit; if(dev(ilm)-dev(ilm-1).lt.sml) exit; > g=log(q); deallocate(sxp,b,bs,v,r,xv,q,mm,is,ga,ixx); return; end; subroutine kazero(kk,n,y,g,q,az,jerr); implicit double precision(a-h,o-z); parameter(eps=1.0d-7); double precision y(n,kk),g(n,kk),q(n),az(kk); %fortran double precision, dimension (:), allocatable :: s double precision, dimension (:,:), allocatable :: e allocate(e(1:n,1:kk),stat=jerr) if(jerr.ne.0) return %mortran allocate(s(1:n),stat=jerr); if(jerr.ne.0) return; az=0.0; e=exp(g); loop < dm=0.0; d=t/u; az(k)=az(k)+d; ed=exp(d); dm=max(dm,abs(d)); > > until dm.lt.eps; az=az-sum(az)/kk; deallocate(e,s); return; end; function elc(parm,n,cl,a,m); implicit double precision(a-h,o-z); double precision a(n),cl(2); integer m(n); fn=n; am=sum(a)/fn; if parm.eq.0.0.or.n.eq.2 < elc=am; go to :chk:;> call psort7(a,m,1,n); if a(m(1)).eq.a(m(n)) < elc=a(1); go to :chk:;> if mod(n,2).eq.1 < ad=a(m(n/2+1));> else < ad=0.5*(a(m(n/2+1))+a(m(n/2)));> if parm.eq.1.0 < elc=ad; go to :chk:;> b1=min(am,ad); b2=max(am,ad); k2=1; until a(m(k2)).gt.b1 < k2=k2+1;> k1=k2-1; until a(m(k2)).ge.b2 < k2=k2+1;> r=parm/((1.0-parm)*fn); is=0; sm=n-2*(k1-1); > if is.ne.0 < elc=s; go to :chk:;> r2=2.0*r; s1=a(m(k1)); am2=2.0*am; cri=r2*sum(abs(a-s1))+s1*(s1-am2); elc=s1; s1=s; > :chk:elc=max(maxval(a-cl(2)),min(minval(a-cl(1)),elc)); return; end; function nintot(ni,nx,nc,a,m,nin,is); implicit double precision(a-h,o-z); double precision a(nx,nc); integer m(nx),is(ni); is=0; nintot=0; > return; end; subroutine luncomp(ni,nx,nc,ca,ia,nin,a); implicit double precision(a-h,o-z); double precision ca(nx,nc),a(ni,nc); integer ia(nx); a=0.0; return; end; subroutine lmodval(nt,x,nc,nx,a0,ca,ia,nin,ans); implicit double precision(a-h,o-z); double precision a0(nc),ca(nx,nc),x(nt,*),ans(nc,nt); integer ia(nx); > return; end; subroutine splognet(parm,no,ni,nc,x,ix,jx,y,g,jd,vp,cl,ne,nx,nlam,flmin, ulam,thr,isd,intr,maxit,kopt,lmu,a0,ca,ia,nin,dev0,dev,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(*),y(no,max(2,nc)),g(no,nc),vp(ni),ulam(nlam); double precision ca(nx,nc,nlam),a0(nc,nlam),dev(nlam),alm(nlam),cl(2,ni); integer ix(*),jx(*),jd(*),ia(nx),nin(nlam); %fortran double precision, dimension (:), allocatable :: xm,xs,ww,vq,xv integer, dimension (:), allocatable :: ju %mortran if maxval(vp).le.0.0 < jerr=10000; return;> allocate(ww(1:no),stat=jerr); if(jerr.ne.0) return; allocate(ju(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(vq(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xs(1:ni),stat=jerr); if(jerr.ne.0) return; if kopt.eq.2 < allocate(xv(1:ni),stat=jerr); if(jerr.ne.0) return;> call spchkvars(no,ni,x,ix,ju); if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0; if maxval(ju).le.0 < jerr=7777; return;> vq=max(0d0,vp); vq=vq*ni/sum(vq); sw=sum(ww); ww=ww/sw; if nc.eq.1 < call splstandard2(no,ni,x,ix,jx,ww,ju,isd,intr,xm,xs); if isd.gt.0 < > call sprlognet2n(parm,no,ni,x,ix,jx,y(:,1),g(:,1),ww,ju,vq,cl,ne,nx,nlam, flmin,ulam,thr,isd,intr,maxit,kopt,xm,xs,lmu,a0,ca,ia,nin,dev0,dev, alm,nlp,jerr); > elseif kopt.eq.2 < call multsplstandard2(no,ni,x,ix,jx,ww,ju,isd,intr,xm,xs,xv); if isd.gt.0 < > call multsprlognetn(parm,no,ni,nc,x,ix,jx,y,g,ww,ju,vq,cl,ne,nx,nlam,flmin, ulam,thr,intr,maxit,xv,xm,xs,lmu,a0,ca,ia,nin,dev0,dev,alm,nlp,jerr); > else < call splstandard2(no,ni,x,ix,jx,ww,ju,isd,intr,xm,xs); if isd.gt.0 < > call sprlognetn(parm,no,ni,nc,x,ix,jx,y,g,ww,ju,vq,cl,ne,nx,nlam,flmin, ulam,thr,isd,intr,maxit,kopt,xm,xs,lmu,a0,ca, ia,nin,dev0,dev,alm,nlp,jerr); > if(jerr.gt.0) return; dev0=2.0*sw*dev0; > if intr.eq.0 < a0(ic,k)=0.0;> else < a0(ic,k)=a0(ic,k)-dot_product(ca(1:nk,ic,k),xm(ia(1:nk)));> > > deallocate(ww,ju,vq,xm,xs); if(kopt.eq.2) deallocate(xv); return; end; subroutine multsplstandard2(no,ni,x,ix,jx,w,ju,isd,intr,xm,xs,xv); implicit double precision(a-h,o-z); double precision x(*),w(no),xm(ni),xs(ni),xv(ni); integer ix(*),jx(*),ju(ni); if intr.eq.0 < else < xs(j)=1.0;> > return; > > if(isd.eq.0) xs=1.0; return; end; subroutine splstandard2(no,ni,x,ix,jx,w,ju,isd,intr,xm,xs); implicit double precision(a-h,o-z); double precision x(*),w(no),xm(ni),xs(ni); integer ix(*),jx(*),ju(ni); if intr.eq.0 < else < xs(j)=1.0;> > return; > if(isd.eq.0) xs=1.0; return; end; subroutine sprlognet2n(parm,no,ni,x,ix,jx,y,g,w,ju,vp,cl,ne,nx,nlam, flmin,ulam,shri,isd,intr,maxit,kopt,xb,xs, lmu,a0,a,m,kin,dev0,dev,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(*),y(no),g(no),w(no),vp(ni),ulam(nlam),cl(2,ni); double precision a(nx,nlam),a0(nlam),dev(nlam),alm(nlam); double precision xb(ni),xs(ni); integer ix(*),jx(*),ju(ni),m(nx),kin(nlam); %fortran double precision, dimension (:), allocatable :: xm,b,bs,v,r double precision, dimension (:), allocatable :: sc,xv,q,ga integer, dimension (:), allocatable :: mm,ixx %mortran call get_int_parms(sml,eps,big,mnlam,devmax,pmin,exmx,itrace); allocate(b(0:ni),stat=jerr); if(jerr.ne.0) return; allocate(xm(0:ni),stat=jerr); if(jerr.ne.0) return; allocate(xv(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(bs(0:ni),stat=jerr); if(jerr.ne.0) return; allocate(ga(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(mm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ixx(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(q(1:no),stat=jerr); if(jerr.ne.0) return; allocate(r(1:no),stat=jerr); if(jerr.ne.0) return; allocate(v(1:no),stat=jerr); if(jerr.ne.0) return; allocate(sc(1:no),stat=jerr); if(jerr.ne.0) return; fmax=log(1.0/pmin-1.0); fmin=-fmax; vmin=(1.0+pmin)*pmin*(1.0-pmin); bta=parm; omb=1.0-bta; q0=dot_product(w,y); if q0.le.pmin < jerr=8001; return;> if q0.ge.1.0-pmin < jerr=9001; return;> if(intr.eq.0) q0=0.5; bz=0.0; if(intr.ne.0) bz=log(q0/(1.0-q0)); if nonzero(no,g).eq.0 < vi=q0*(1.0-q0); b(0)=bz; v=vi*w; r=w*(y-q0); q=q0; xm(0)=vi; dev1=-(bz*q0+log(1.0-q0)); > else < b(0)=0.0; if intr.ne.0 < b(0)=azero(no,y,g,w,jerr); if(jerr.ne.0) return;> q=1.0/(1.0+exp(-b(0)-g)); v=w*q*(1.0-q); r=w*(y-q); xm(0)=sum(v); dev1=-(b(0)*q0+dot_product(w,y*g+log(1.0-q))); > if kopt.gt.0 < if isd.gt.0.and.intr.ne.0 < xv=0.25;> else < > > b(1:ni)=0.0; dev0=dev1; "Begin: added by Naras" alf=1.0; "End: added by Naras" if flmin.lt.1.0 < eqs=max(eps,flmin); alf=eqs**(1.0/(nlam-1));> m=0; mm=0; nin=0; /o,svr/=0.0; mnl=min(mnlam,nlam); bs=0.0; /nlp,nin/=0; shr=shri*dev0; al=0.0; ixx=0; elseif ilm.gt.2 < al=al*alf;> elseif ilm.eq.1 < al=big;> else < al0=0.0; al0=al0/max(bta,1.0d-3); al=alf*al0; > al2=al*omb; al1=al*bta; tlam=bta*(2.0*al-al0); :again:continue; loop < bs(0)=b(0); if(nin.gt.0) bs(m(1:nin))=b(m(1:nin)); > loop < nlp=nlp+1; dlx=0.0; else < b(k)=max(cl(1,k),min(cl(2,k),sign(au,u)/(xv(k)+vp(k)*al2))); > d=b(k)-bk; if(abs(d).le.0.0) next; dlx=max(dlx,xv(k)*d**2); if mm(k).eq.0 < nin=nin+1; if(nin.gt.nx) exit; mm(k)=nin; m(nin)=k; sc(1:jn)=v(jx(jb:je)); xm(k)=dot_product(sc(1:jn),x(jb:je)); > r(jx(jb:je))=r(jx(jb:je))-d*v(jx(jb:je))*x(jb:je)/xs(k); o=o+d*(xb(k)/xs(k)); svr=svr-d*(xm(k)-xb(k)*xm(0))/xs(k); > if(nin.gt.nx) exit; d=0.0; if(intr.ne.0) d=svr/xm(0); if d.ne.0.0 < b(0)=b(0)+d; dlx=max(dlx,xm(0)*d**2); r=r-d*v; svr=svr-d*xm(0); > if(dlx.lt.shr) exit; if nlp.gt.maxit < jerr=-ilm; return;> loop < nlp=nlp+1; dlx=0.0; else < b(k)=max(cl(1,k),min(cl(2,k),sign(au,u)/(xv(k)+vp(k)*al2))); > d=b(k)-bk; if(abs(d).le.0.0) next; dlx=max(dlx,xv(k)*d**2); r(jx(jb:je))=r(jx(jb:je))-d*v(jx(jb:je))*x(jb:je)/xs(k); o=o+d*(xb(k)/xs(k)); svr=svr-d*(xm(k)-xb(k)*xm(0))/xs(k); > d=0.0; if(intr.ne.0) d=svr/xm(0); if d.ne.0.0 < b(0)=b(0)+d; dlx=max(dlx,xm(0)*d**2); r=r-d*v; svr=svr-d*xm(0); > if(dlx.lt.shr) exit; if nlp.gt.maxit < jerr=-ilm; return;> > > if(nin.gt.nx) exit; sc=b(0); b0=0.0; sc=sc+b0; elseif fi.gt.fmax < q(i)=1.0;> else < q(i)=1.0/(1.0+exp(-fi));> > v=w*q*(1.0-q); xm(0)=sum(v); if(xm(0).lt.vmin) exit; r=w*(y-q); svr=sum(r); o=0.0; if xm(0)*(b(0)-bs(0))**2.lt.shr < kx=0; if kx.eq.0 < > if(kx.eq.1) go to :again:; exit; > > > if nin.gt.nx < jerr=-10000-ilm; exit;> if(nin.gt.0) a(1:nin,ilm)=b(m(1:nin)); kin(ilm)=nin; a0(ilm)=b(0); alm(ilm)=al; lmu=ilm; devi=dev2(no,w,y,q,pmin); dev(ilm)=(dev1-devi)/dev0; if(ilm.lt.mnl) next; if(flmin.ge.1.0) next; me=0; if(me.gt.ne) exit; if(dev(ilm).gt.devmax) exit; if(dev(ilm)-dev(ilm-1).lt.sml) exit; if(xm(0).lt.vmin) exit; > g=log(q/(1.0-q)); deallocate(xm,b,bs,v,r,sc,xv,q,mm,ga,ixx); return; end; subroutine sprlognetn(parm,no,ni,nc,x,ix,jx,y,g,w,ju,vp,cl,ne,nx,nlam,flmin, ulam,shri,isd,intr,maxit,kopt,xb,xs,lmu,a0,a,m,kin,dev0,dev,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(*),y(no,nc),g(no,nc),w(no),vp(ni),ulam(nlam),xb(ni),xs(ni); double precision a(nx,nc,nlam),a0(nc,nlam),dev(nlam),alm(nlam),cl(2,ni); integer ix(*),jx(*),ju(ni),m(nx),kin(nlam); %fortran double precision, dimension (:,:), allocatable :: q double precision, dimension (:), allocatable :: sxp,sxpl double precision, dimension (:), allocatable :: sc,xm,v,r,ga double precision, dimension (:,:), allocatable :: b,bs,xv integer, dimension (:), allocatable :: mm,is,iy allocate(b(0:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(xv(1:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(bs(0:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(q(1:no,1:nc),stat=jerr) if(jerr.ne.0) return %mortran call get_int_parms(sml,eps,big,mnlam,devmax,pmin,exmx,itrace); exmn=-exmx; allocate(xm(0:ni),stat=jerr); if(jerr.ne.0) return; allocate(r(1:no),stat=jerr); if(jerr.ne.0) return; allocate(v(1:no),stat=jerr); if(jerr.ne.0) return; allocate(mm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ga(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(iy(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(is(1:max(nc,ni)),stat=jerr); if(jerr.ne.0) return; allocate(sxp(1:no),stat=jerr); if(jerr.ne.0) return; allocate(sxpl(1:no),stat=jerr); if(jerr.ne.0) return; allocate(sc(1:no),stat=jerr); if(jerr.ne.0) return; pmax=1.0-pmin; emin=pmin/pmax; emax=1.0/emin; pfm=(1.0+pmin)*pmin; pfx=(1.0-pmin)*pmax; vmin=pfm*pmax; bta=parm; omb=1.0-bta; dev1=0.0; dev0=0.0; if q0.ge.1.0-pmin < jerr =9000+ic; return;> if(intr.eq.0) q0=1.0/nc; b(1:ni,ic)=0.0; b(0,ic)=0.0; if intr.ne.0 < b(0,ic)=log(q0); dev1=dev1-q0*b(0,ic);> > if(intr.eq.0) dev1=log(float(nc)); iy=0; al=0.0; if nonzero(no*nc,g).eq.0 < b(0,:)=b(0,:)-sum(b(0,:))/nc; sxp=0.0; > else < sxp=0.0; if intr.eq.0 < b(0,:)=0.0;> else < call kazero(nc,no,y,g,w,b(0,:),jerr); if(jerr.ne.0) return;> dev1=0.0; sxpl=w*log(sxp); > > dev0=dev0+dev1; if kopt.gt.0 < if isd.gt.0.and.intr.ne.0 < xv=0.25;> else < > > "Begin: added by Naras" alf=1.0; "End: added by Naras" if flmin.lt.1.0 < eqs=max(eps,flmin); alf=eqs**(1.0/(nlam-1));> m=0; mm=0; nin=0; nlp=0; mnl=min(mnlam,nlam); bs=0.0; svr=0.0; o=0.0; shr=shri*dev0; ga=0.0; > elseif ilm.gt.2 < al=al*alf;> elseif ilm.eq.1 < al=big;> else < al0=0.0; al0=al0/max(bta,1.0d-3); al=alf*al0; > al2=al*omb; al1=al*bta; tlam=bta*(2.0*al-al0); :again:continue; loop < /ixx,jxx/=0; ig=0; elseif pic.gt.pfx < pic=1.0; v(i)=0.0;> else < v(i)=w(i)*pic*(1.0-pic); xm(0)=xm(0)+v(i);> r(i)=w(i)*(y(i,ic)-pic); svr=svr+r(i); > if(xm(0).le.vmin) next; ig=1; > loop < nlp=nlp+1; dlx=0.0; else < b(k,ic)=max(cl(1,k),min(cl(2,k),sign(au,u)/ (xv(k,ic)+vp(k)*al2))); > d=b(k,ic)-bk; if(abs(d).le.0.0) next; dlx=max(dlx,xv(k,ic)*d**2); if mm(k).eq.0 < nin=nin+1; if nin.gt.nx < jxx=1; exit;> mm(k)=nin; m(nin)=k; xm(k)=dot_product(v(jx(jb:je)),x(jb:je)); > r(jx(jb:je))=r(jx(jb:je))-d*v(jx(jb:je))*x(jb:je)/xs(k); o=o+d*(xb(k)/xs(k)); svr=svr-d*(xm(k)-xb(k)*xm(0))/xs(k); > if(jxx.gt.0) exit; d=0.0; if(intr.ne.0) d=svr/xm(0); if d.ne.0.0 < b(0,ic)=b(0,ic)+d; dlx=max(dlx,xm(0)*d**2); r=r-d*v; svr=svr-d*xm(0); > if(dlx.lt.shr) exit; if nlp.gt.maxit < jerr=-ilm; return;> loop < nlp=nlp+1; dlx=0.0; else < b(k,ic)=max(cl(1,k),min(cl(2,k),sign(au,u)/ (xv(k,ic)+vp(k)*al2))); > d=b(k,ic)-bk; if(abs(d).le.0.0) next; dlx=max(dlx,xv(k,ic)*d**2); r(jx(jb:je))=r(jx(jb:je))-d*v(jx(jb:je))*x(jb:je)/xs(k); o=o+d*(xb(k)/xs(k)); svr=svr-d*(xm(k)-xb(k)*xm(0))/xs(k); > d=0.0; if(intr.ne.0) d=svr/xm(0); if d.ne.0.0 < b(0,ic)=b(0,ic)+d; dlx=max(dlx,xm(0)*d**2); r=r-d*v; svr=svr-d*xm(0); > if(dlx.lt.shr) exit; if nlp.gt.maxit < jerr=-ilm; return;> > > if(jxx.gt.0) exit; if(xm(0)*(b(0,ic)-bs(0,ic))**2.gt.shr) ixx=1; if ixx.eq.0 < > > sc=b(0,ic)+g(:,ic); b0=0.0; sc=min(max(exmn,sc+b0),exmx); sxp=sxp-q(:,ic); q(:,ic)=min(max(emin*sxp,exp(sc)),emax*sxp); sxp=sxp+q(:,ic); > s=-sum(b(0,:))/nc; b(0,:)=b(0,:)+s; sc=s; b0=0.0; else < s=elc(parm,nc,cl(:,l),b(l,:),is);> b(l,:)=b(l,:)-s; jb=ix(l); je=ix(l+1)-1; sc(jx(jb:je))=sc(jx(jb:je))-s*x(jb:je)/xs(l); b0=b0+s*xb(l)/xs(l); > sc=sc+b0; sc=exp(sc); sxp=sxp*sc; if(jxx.gt.0) exit; if(ig.eq.0) exit; if ixx.eq.0 < > > if(ixx.eq.1) go to :again:; exit; > > if jxx.gt.0 < jerr=-10000-ilm; exit;> devi=0.0; > kin(ilm)=nin; alm(ilm)=al; lmu=ilm; dev(ilm)=(dev1-devi)/dev0; if(ig.eq.0) exit; if(ilm.lt.mnl) next; if(flmin.ge.1.0) next; if(nintot(ni,nx,nc,a(1,1,ilm),m,nin,is).gt.ne) exit; if(dev(ilm).gt.devmax) exit; if(dev(ilm)-dev(ilm-1).lt.sml) exit; > g=log(q); deallocate(sxp,b,bs,v,r,xv,q,mm,is,xm,sc,ga,iy); return; end; subroutine lcmodval(nc,nx,a0,ca,ia,nin,x,ix,jx,n,f); implicit double precision(a-h,o-z); double precision a0(nc),ca(nx,nc),x(*),f(nc,n); integer ia(*),ix(*),jx(*); > return; end; subroutine coxnet(parm,no,ni,x,y,d,g,w,jd,vp,cl,ne,nx,nlam,flmin,ulam,thr, maxit,isd,lmu,ca,ia,nin,dev0,dev,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(no,ni),y(no),d(no),g(no),w(no),vp(ni),ulam(nlam); double precision ca(nx,nlam),dev(nlam),alm(nlam),cl(2,ni); integer jd(*),ia(nx),nin(nlam); %fortran double precision, dimension (:), allocatable :: xs,ww,vq integer, dimension (:), allocatable :: ju %mortran if maxval(vp).le.0.0 < jerr=10000; return;> allocate(ww(1:no),stat=jerr); if(jerr.ne.0) return; allocate(ju(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(vq(1:ni),stat=jerr); if(jerr.ne.0) return; if isd.gt.0 < allocate(xs(1:ni),stat=jerr); if(jerr.ne.0) return;> call chkvars(no,ni,x,ju); if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0; if maxval(ju).le.0 < jerr=7777; return;> vq=max(0d0,vp); vq=vq*ni/sum(vq); ww=max(0d0,w); sw=sum(ww); if sw.le.0.0 < jerr=9999; return;> ww=ww/sw; call cstandard(no,ni,x,ww,ju,isd,xs); if isd.gt.0 < > call coxnet1(parm,no,ni,x,y,d,g,ww,ju,vq,cl,ne,nx,nlam,flmin,ulam,thr, isd,maxit,lmu,ca,ia,nin,dev0,dev,alm,nlp,jerr); if(jerr.gt.0) return; dev0=2.0*sw*dev0; if isd.gt.0 < > deallocate(ww,ju,vq); if(isd.gt.0) deallocate(xs); return; end; subroutine cstandard(no,ni,x,w,ju,isd,xs); implicit double precision(a-h,o-z); double precision x(no,ni),w(no),xs(ni); integer ju(ni); > return; end; subroutine coxnet1(parm,no,ni,x,y,d,g,q,ju,vp,cl,ne,nx,nlam,flmin,ulam,cthri, isd,maxit,lmu,ao,m,kin,dev0,dev,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(no,ni),y(no),q(no),d(no),g(no),vp(ni),ulam(nlam); double precision ao(nx,nlam),dev(nlam),alm(nlam),cl(2,ni); integer ju(ni),m(nx),kin(nlam); %fortran double precision, dimension (:), allocatable :: w,dk,v,xs,wr double precision, dimension (:), allocatable :: a,as,f,dq double precision, dimension (:), allocatable :: e,uu,ga integer, dimension (:), allocatable :: jp,kp,mm,ixx %mortran call get_int_parms(sml,eps,big,mnlam,devmax,pmin,exmx,itrace); "Start: Naras Edit" isd = isd*1; "End: Naras Edit" sml=sml*100.0; devmax=devmax*0.99/0.999; allocate(e(1:no),stat=jerr); if(jerr.ne.0) return; allocate(uu(1:no),stat=jerr); if(jerr.ne.0) return; allocate(f(1:no),stat=jerr); if(jerr.ne.0) return; allocate(w(1:no),stat=jerr); if(jerr.ne.0) return; allocate(v(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(a(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(as(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xs(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ga(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ixx(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(jp(1:no),stat=jerr); if(jerr.ne.0) return; allocate(kp(1:no),stat=jerr); if(jerr.ne.0) return; allocate(dk(1:no),stat=jerr); if(jerr.ne.0) return; allocate(wr(1:no),stat=jerr); if(jerr.ne.0) return; allocate(dq(1:no),stat=jerr); if(jerr.ne.0) return; allocate(mm(1:ni),stat=jerr); if(jerr.ne.0) return; call groups(no,y,d,q,nk,kp,jp,t0,jerr); if(jerr.ne.0) go to :done:; alpha=parm; oma=1.0-alpha; nlm=0; ixx=0; al=0.0; dq=d*q; call died(no,nk,dq,kp,jp,dk); a=0.0; f(1)=0.0; fmax=log(huge(f(1))*0.1); if nonzero(no,g).ne.0 < f=g-dot_product(q,g); e=q*exp(sign(min(abs(f),fmax),f)); > else < f=0.0; e=q;> r0=risk(no,ni,nk,dq,dk,f,e,kp,jp,uu); rr=-(dot_product(dk(1:nk),log(dk(1:nk)))+r0); dev0=rr; > call outer(no,nk,dq,dk,kp,jp,e,wr,w,jerr,uu); if(jerr.ne.0) go to :done:; "Begin: added by Naras" alf=1.0; "End: added by Naras" if flmin.lt.1.0 < eqs=max(eps,flmin); alf=eqs**(1.0/(nlam-1));> m=0; mm=0; /nlp,nin/=0; mnl=min(mnlam,nlam); as=0.0; cthr=cthri*dev0; elseif ilm.gt.2 < al=al*alf;> elseif ilm.eq.1 < al=big;> else < al0=0.0; al0=al0/max(parm,1.0d-3); al=alf*al0; > sa=alpha*al; omal=oma*al; tlam=alpha*(2.0*al-al0); :again:continue; loop < if(nin.gt.0) as(m(1:nin))=a(m(1:nin)); call vars(no,ni,x,w,ixx,v); loop < nlp=nlp+1; dli=0.0; else < at=max(cl(1,j),min(cl(2,j),sign(abs(u)-vp(j)*sa,u)/ (v(j)+vp(j)*omal))); > if at.ne.a(j) < del=at-a(j); a(j)=at; dli=max(dli,v(j)*del**2); wr=wr-del*w*x(:,j); f=f+del*x(:,j); if mm(j).eq.0 < nin=nin+1; if(nin.gt.nx) exit; mm(j)=nin; m(nin)=j; > > > if(nin.gt.nx) exit; if(dli.lt.cthr) exit; if nlp.gt.maxit < jerr=-ilm; return;> loop < nlp=nlp+1; dli=0.0; else < at=max(cl(1,j),min(cl(2,j),sign(abs(u)-vp(j)*sa,u)/ (v(j)+vp(j)*omal))); > if at.ne.a(j) < del=at-a(j); a(j)=at; dli=max(dli,v(j)*del**2); wr=wr-del*w*x(:,j); f=f+del*x(:,j); > > if(dli.lt.cthr) exit; if nlp.gt.maxit < jerr=-ilm; return;> > > if(nin.gt.nx) exit; e=q*exp(sign(min(abs(f),fmax),f)); call outer(no,nk,dq,dk,kp,jp,e,wr,w,jerr,uu); if jerr.ne.0 < jerr=jerr-ilm; go to :done:;> ix=0; if ix.eq.0 < > if(ix.eq.1) go to :again:; exit; > > if nin.gt.nx < jerr=-10000-ilm; exit;> if(nin.gt.0) ao(1:nin,ilm)=a(m(1:nin)); kin(ilm)=nin; alm(ilm)=al; lmu=ilm; dev(ilm)=(risk(no,ni,nk,dq,dk,f,e,kp,jp,uu)-r0)/rr; if(ilm.lt.mnl) next; if(flmin.ge.1.0) next; me=0; if(me.gt.ne) exit; if((dev(ilm)-dev(ilm-mnl+1))/dev(ilm).lt.sml) exit; if(dev(ilm).gt.devmax) exit; > g=f; :done: deallocate(e,uu,w,dk,v,xs,f,wr,a,as,jp,kp,dq,mm,ga,ixx); return; end; subroutine cxmodval(ca,ia,nin,n,x,f); implicit double precision(a-h,o-z); double precision ca(nin),x(n,*),f(n); integer ia(nin); f=0.0; if(nin.le.0) return; return; end; subroutine groups(no,y,d,q,nk,kp,jp,t0,jerr); implicit double precision(a-h,o-z); double precision y(no),d(no),q(no); integer jp(no),kp(*"nk"); call psort7(y,jp,1,no); nj=0; if nj.eq.0 < jerr=20000; return;> j=1; until d(jp(j)).gt.0.0 < j=j+1;> until j.gt.nj; if j.ge.nj-1 < jerr=30000; return;> t0=y(jp(j)); j0=j-1; if j0.gt.0 < until y(jp(j0)).lt.t0 < j0=j0-1;> until j0.eq.0; if j0.gt.0 < nj=nj-j0; > > jerr=0; nk=0; yk=t0; j=2; loop < until d(jp(j)).gt.0.0.and.y(jp(j)).gt.yk < j=j+1;> until j.gt.nj; nk=nk+1; kp(nk)=j-1; if(j.gt.nj) exit; if j.eq.nj < nk=nk+1; kp(nk)=nj; exit;> yk=y(jp(j)); j=j+1; > return; end; subroutine outer(no,nk,d,dk,kp,jp,e,wr,w,jerr,u); implicit double precision(a-h,o-z); double precision d(no),dk(nk),wr(no),w(no); double precision e(no),u(no),b,c; integer kp(nk),jp(no); call usk(no,nk,kp,jp,e,u); b=dk(1)/u(1); c=dk(1)/u(1)**2; jerr=0; wr(i)=d(i)-e(i)*b; > wr(i)=d(i)-e(i)*b; > > return; end; subroutine vars(no,ni,x,w,ixx,v); implicit double precision(a-h,o-z); double precision x(no,ni),w(no),v(ni); integer ixx(ni); return; end; subroutine died(no,nk,d,kp,jp,dk); implicit double precision(a-h,o-z); double precision d(no),dk(nk); integer kp(nk),jp(no); dk(1)=sum(d(jp(1:kp(1)))); return; end; subroutine usk(no,nk,kp,jp,e,u); implicit double precision(a-h,o-z); double precision e(no),u(nk),h; integer kp(nk),jp(no); h=0.0; u(k)=h; > return; end; function risk(no,ni,nk,d,dk,f,e,kp,jp,u); implicit double precision(a-h,o-z); double precision d(no),dk(nk),f(no); integer kp(nk),jp(no); double precision e(no),u(nk); "Start: Naras Edit" ni = ni*1; "End: Naras Edit" call usk(no,nk,kp,jp,e,u); u=log(u); risk=dot_product(d,f)-dot_product(dk,u); return; end; subroutine loglike(no,ni,x,y,d,g,w,nlam,a,flog,jerr); implicit double precision(a-h,o-z); double precision x(no,ni),y(no),d(no),g(no),w(no),a(ni,nlam),flog(nlam); %fortran double precision, dimension (:), allocatable :: dk,f,xm,dq,q double precision, dimension (:), allocatable :: e,uu integer, dimension (:), allocatable :: jp,kp %mortran allocate(e(1:no),stat=jerr); if(jerr.ne.0) return; allocate(q(1:no),stat=jerr); if(jerr.ne.0) return; allocate(uu(1:no),stat=jerr); if(jerr.ne.0) return; allocate(f(1:no),stat=jerr); if(jerr.ne.0) return; allocate(dk(1:no),stat=jerr); if(jerr.ne.0) return; allocate(jp(1:no),stat=jerr); if(jerr.ne.0) return; allocate(kp(1:no),stat=jerr); if(jerr.ne.0) return; allocate(dq(1:no),stat=jerr); if(jerr.ne.0) return; allocate(xm(1:ni),stat=jerr); if(jerr.ne.0) return; q=max(0d0,w); sw=sum(q); if sw.le.0.0 < jerr=9999; go to :done:;> call groups(no,y,d,q,nk,kp,jp,t0,jerr); if(jerr.ne.0) go to :done:; fmax=log(huge(e(1))*0.1); dq=d*q; call died(no,nk,dq,kp,jp,dk); gm=dot_product(q,g)/sw; flog(lam)=risk(no,ni,nk,dq,dk,f,e,kp,jp,uu); > :done: deallocate(e,uu,dk,f,jp,kp,dq); return; end; subroutine fishnet(parm,no,ni,x,y,g,w,jd,vp,cl,ne,nx,nlam,flmin,ulam,thr, isd,intr,maxit,lmu,a0,ca,ia,nin,dev0,dev,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(no,ni),y(no),g(no),w(no),vp(ni),ulam(nlam); double precision ca(nx,nlam),a0(nlam),dev(nlam),alm(nlam),cl(2,ni); integer jd(*),ia(nx),nin(nlam); %fortran double precision, dimension (:), allocatable :: xm,xs,ww,vq integer, dimension (:), allocatable :: ju %mortran if maxval(vp).le.0.0 < jerr=10000; return;> if minval(y).lt.0.0 < jerr=8888; return;> allocate(ww(1:no),stat=jerr); if(jerr.ne.0) return; allocate(ju(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(vq(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xm(1:ni),stat=jerr); if(jerr.ne.0) return; if isd.gt.0 < allocate(xs(1:ni),stat=jerr); if(jerr.ne.0) return;> call chkvars(no,ni,x,ju); if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0; if maxval(ju).le.0 < jerr=7777; go to :done:;> vq=max(0d0,vp); vq=vq*ni/sum(vq); ww=max(0d0,w); sw=sum(ww); if sw.le.0.0 < jerr=9999; go to :done:;> ww=ww/sw; call lstandard1(no,ni,x,ww,ju,isd,intr,xm,xs); if isd.gt.0 < > call fishnet1(parm,no,ni,x,y,g,ww,ju,vq,cl,ne,nx,nlam,flmin,ulam,thr, isd,intr,maxit,lmu,a0,ca,ia,nin,dev0,dev,alm,nlp,jerr); if(jerr.gt.0) go to :done:; dev0=2.0*sw*dev0; else < a0(k)=a0(k)-dot_product(ca(1:nk,k),xm(ia(1:nk)));> > :done:deallocate(ww,ju,vq,xm); if(isd.gt.0) deallocate(xs); return; end; subroutine fishnet1(parm,no,ni,x,y,g,q,ju,vp,cl,ne,nx,nlam,flmin,ulam,shri, isd,intr,maxit,lmu,a0,ca,m,kin,dev0,dev,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(no,ni),y(no),g(no),q(no),vp(ni),ulam(nlam); double precision ca(nx,nlam),a0(nlam),dev(nlam),alm(nlam),cl(2,ni); integer ju(ni),m(nx),kin(nlam); %fortran double precision, dimension (:), allocatable :: t,w,wr,v,a,f,as,ga integer, dimension (:), allocatable :: mm,ixx %mortran call get_int_parms(sml,eps,big,mnlam,devmax,pmin,exmx,itrace); sml=sml*10.0; "Start: Naras Edit" isd = isd*1; "End: Naras Edit" allocate(a(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(as(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(t(1:no),stat=jerr); if(jerr.ne.0) return; allocate(mm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ga(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ixx(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(wr(1:no),stat=jerr); if(jerr.ne.0) return; allocate(v(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(w(1:no),stat=jerr); if(jerr.ne.0) return; allocate(f(1:no),stat=jerr); if(jerr.ne.0) return; bta=parm; omb=1.0-bta; t=q*y; yb=sum(t); fmax=log(huge(bta)*0.1); if nonzero(no,g).eq.0 < if intr.ne.0 < w=q*yb; az=log(yb); f=az; dv0=yb*(az-1.0);> else < w=q; az=0.0; f=az; dv0=-1.0;> > else < w=q*exp(sign(min(abs(g),fmax),g)); v0=sum(w); if intr.ne.0 < eaz=yb/v0; w=eaz*w; az=log(eaz); f=az+g; dv0=dot_product(t,g)-yb*(1.0-az); > else < az=0.0; f=g; dv0=dot_product(t,g)-v0;> > a=0.0; as=0.0; wr=t-w; v0=1.0; if(intr.ne.0) v0=yb; dvr=-yb; dvr=dvr-dv0; dev0=dvr; "Begin: added by Naras" alf=1.0; "End: added by Naras" if flmin.lt.1.0 < eqs=max(eps,flmin); alf=eqs**(1.0/(nlam-1));> m=0; mm=0; /nlp,nin/=0; mnl=min(mnlam,nlam); shr=shri*dev0; ixx=0; al=0.0; elseif ilm.gt.2 < al=al*alf;> elseif ilm.eq.1 < al=big;> else < al0=0.0; al0=al0/max(bta,1.0d-3); al=alf*al0; > al2=al*omb; al1=al*bta; tlam=bta*(2.0*al-al0); :again:continue; loop < az0=az; if(nin.gt.0) as(m(1:nin))=a(m(1:nin)); loop < nlp=nlp+1; dlx=0.0; else < a(k)=max(cl(1,k),min(cl(2,k),sign(au,u)/(v(k)+vp(k)*al2))); > if(a(k).eq.ak) next; d=a(k)-ak; dlx=max(dlx,v(k)*d**2); wr=wr-d*w*x(:,k); f=f+d*x(:,k); if mm(k).eq.0 < nin=nin+1; if(nin.gt.nx) exit; mm(k)=nin; m(nin)=k; > > if(nin.gt.nx) exit; if intr.ne.0 < d=sum(wr)/v0; az=az+d; dlx=max(dlx,v0*d**2); wr=wr-d*w; f=f+d; > if(dlx.lt.shr) exit; if nlp.gt.maxit < jerr=-ilm; return;> loop < nlp=nlp+1; dlx=0.0; else < a(k)=max(cl(1,k),min(cl(2,k),sign(au,u)/(v(k)+vp(k)*al2))); > if(a(k).eq.ak) next; d=a(k)-ak; dlx=max(dlx,v(k)*d**2); wr=wr-d*w*x(:,k); f=f+d*x(:,k) > if intr.ne.0 < d=sum(wr)/v0; az=az+d; dlx=max(dlx,v0*d**2); wr=wr-d*w; f=f+d; > if(dlx.lt.shr) exit; if nlp.gt.maxit < jerr=-ilm; return;> > > if(nin.gt.nx) exit; w=q*exp(sign(min(abs(f),fmax),f)); v0=sum(w); wr=t-w; if v0*(az-az0)**2.lt.shr < ix=0; if ix.eq.0 < > if(ix.eq.1) go to :again:; exit; > > > if nin.gt.nx < jerr=-10000-ilm; exit;> if(nin.gt.0) ca(1:nin,ilm)=a(m(1:nin)); kin(ilm)=nin; a0(ilm)=az; alm(ilm)=al; lmu=ilm; dev(ilm)=(dot_product(t,f)-v0-dv0)/dvr; if(ilm.lt.mnl) next; if(flmin.ge.1.0) next; me=0; if(me.gt.ne) exit; if((dev(ilm)-dev(ilm-mnl+1))/dev(ilm).lt.sml) exit; if(dev(ilm).gt.devmax) exit; > g=f; :done:deallocate(t,w,wr,v,a,f,as,mm,ga,ixx); return; end; function nonzero(n,v); implicit double precision(a-h,o-z); double precision v(n); nonzero=0; > return; end; subroutine solns(ni,nx,lmu,a,ia,nin,b); implicit double precision(a-h,o-z); double precision a(nx,lmu),b(ni,lmu); integer ia(nx),nin(lmu); return; end; subroutine lsolns(ni,nx,nc,lmu,a,ia,nin,b); implicit double precision(a-h,o-z); double precision a(nx,nc,lmu),b(ni,nc,lmu); integer ia(nx),nin(lmu); return; end; subroutine deviance(no,ni,x,y,g,q,nlam,a0,a,flog,jerr); implicit double precision(a-h,o-z); double precision x(no,ni),y(no),g(no),q(no),a(ni,nlam),a0(nlam),flog(nlam); %fortran double precision, dimension (:), allocatable :: w %mortran if minval(y).lt.0.0 < jerr=8888; return;> allocate(w(1:no),stat=jerr); if(jerr.ne.0) return; w=max(0d0,q); sw=sum(w); if sw.le.0.0 < jerr=9999; go to :done:;> yb=dot_product(w,y)/sw; fmax=log(huge(y(1))*0.1); flog(lam)=2.0*(sw*yb*(log(yb)-1.0)-s); > :done: deallocate(w); return; end; subroutine spfishnet(parm,no,ni,x,ix,jx,y,g,w,jd,vp,cl,ne,nx,nlam,flmin, ulam,thr,isd,intr,maxit,lmu,a0,ca,ia,nin,dev0,dev,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(*),y(no),g(no),w(no),vp(ni),ulam(nlam),cl(2,ni); double precision ca(nx,nlam),a0(nlam),dev(nlam),alm(nlam); integer ix(*),jx(*),jd(*),ia(nx),nin(nlam); %fortran double precision, dimension (:), allocatable :: xm,xs,ww,vq integer, dimension (:), allocatable :: ju %mortran if maxval(vp).le.0.0 < jerr=10000; return;> if minval(y).lt.0.0 < jerr=8888; return;> allocate(ww(1:no),stat=jerr); if(jerr.ne.0) return; allocate(ju(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(vq(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xs(1:ni),stat=jerr); if(jerr.ne.0) return; call spchkvars(no,ni,x,ix,ju); if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0; if maxval(ju).le.0 < jerr=7777; go to :done:;> vq=max(0d0,vp); vq=vq*ni/sum(vq); ww=max(0d0,w); sw=sum(ww); if sw.le.0.0 < jerr=9999; go to :done:;> ww=ww/sw; call splstandard2(no,ni,x,ix,jx,ww,ju,isd,intr,xm,xs); if isd.gt.0 < > call spfishnet1(parm,no,ni,x,ix,jx,y,g,ww,ju,vq,cl,ne,nx,nlam,flmin,ulam,thr, isd,intr,maxit,xm,xs,lmu,a0,ca,ia,nin,dev0,dev,alm,nlp,jerr); if(jerr.gt.0) go to :done:; dev0=2.0*sw*dev0; else < a0(k)=a0(k)-dot_product(ca(1:nk,k),xm(ia(1:nk)));> > :done:deallocate(ww,ju,vq,xm,xs); return; end; subroutine spfishnet1(parm,no,ni,x,ix,jx,y,g,q,ju,vp,cl,ne,nx,nlam,flmin,ulam, shri,isd,intr,maxit,xb,xs,lmu,a0,ca,m,kin,dev0,dev,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(*),y(no),g(no),q(no),vp(ni),ulam(nlam),xb(ni),xs(ni); double precision ca(nx,nlam),a0(nlam),dev(nlam),alm(nlam),cl(2,ni); integer ix(*),jx(*),ju(ni),m(nx),kin(nlam); %fortran double precision, dimension (:), allocatable :: qy,t,w,wr,v double precision, dimension (:), allocatable :: a,as,xm,ga integer, dimension (:), allocatable :: mm,ixx %mortran call get_int_parms(sml,eps,big,mnlam,devmax,pmin,exmx,itrace); sml=sml*10.0; "Start: Naras Edit" isd = isd*1; "End: Naras Edit" allocate(a(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(as(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(t(1:no),stat=jerr); if(jerr.ne.0) return; allocate(mm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ga(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ixx(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(wr(1:no),stat=jerr); if(jerr.ne.0) return; allocate(v(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(w(1:no),stat=jerr); if(jerr.ne.0) return; allocate(qy(1:no),stat=jerr); if(jerr.ne.0) return; bta=parm; omb=1.0-bta; fmax=log(huge(bta)*0.1); qy=q*y; yb=sum(qy); if nonzero(no,g).eq.0 < t=0.0; if intr.ne.0 < w=q*yb; az=log(yb); uu=az; xm=yb*xb; dv0=yb*(az-1.0); > else < w=q; xm=0.0; /uu,az/=0.0; dv0=-1.0;> > else < w=q*exp(sign(min(abs(g),fmax),g)); ww=sum(w); t=g; if intr.ne.0 < eaz=yb/ww; w=eaz*w; /az,uu/=log(eaz); dv0=dot_product(qy,g)-yb*(1.0-az); > else < /uu,az/=0.0; dv0=dot_product(qy,g)-ww;> > tt=yb*uu; ww=1.0; if(intr.ne.0) ww=yb; wr=qy-q*(yb*(1.0-uu)); a=0.0; as=0.0; dvr=-yb; dvr=dvr-dv0; dev0=dvr; "Begin: added by Naras" alf=1.0; "End: added by Naras" if flmin.lt.1.0 < eqs=max(eps,flmin); alf=eqs**(1.0/(nlam-1));> m=0; mm=0; /nlp,nin/=0; mnl=min(mnlam,nlam); shr=shri*dev0; al=0.0; ixx=0; elseif ilm.gt.2 < al=al*alf;> elseif ilm.eq.1 < al=big;> else < al0=0.0; al0=al0/max(bta,1.0d-3); al=alf*al0; > al2=al*omb; al1=al*bta; tlam=bta*(2.0*al-al0); :again:continue; loop < az0=az; if(nin.gt.0) as(m(1:nin))=a(m(1:nin)); loop < nlp=nlp+1; dlx=0.0; else < a(k)=max(cl(1,k),min(cl(2,k),sign(au,u)/(v(k)+vp(k)*al2))); > if(a(k).eq.ak) next; if mm(k).eq.0 < nin=nin+1; if(nin.gt.nx) exit; mm(k)=nin; m(nin)=k; > d=a(k)-ak; dlx=max(dlx,v(k)*d**2); dv=d/xs(k); wr(jx(jb:je))=wr(jx(jb:je))-dv*w(jx(jb:je))*x(jb:je); t(jx(jb:je))=t(jx(jb:je))+dv*x(jb:je); uu=uu-dv*xb(k); tt=tt-dv*xm(k); > if(nin.gt.nx) exit; if intr.ne.0 < d=tt/ww-uu; az=az+d; dlx=max(dlx,ww*d**2); uu=uu+d; > if(dlx.lt.shr) exit; if nlp.gt.maxit < jerr=-ilm; return;> loop < nlp=nlp+1; dlx=0.0; else < a(k)=max(cl(1,k),min(cl(2,k),sign(au,u)/(v(k)+vp(k)*al2))); > if(a(k).eq.ak) next; d=a(k)-ak; dlx=max(dlx,v(k)*d**2); dv=d/xs(k); wr(jx(jb:je))=wr(jx(jb:je))-dv*w(jx(jb:je))*x(jb:je); t(jx(jb:je))=t(jx(jb:je))+dv*x(jb:je); uu=uu-dv*xb(k); tt=tt-dv*xm(k); > if intr.ne.0 < d=tt/ww-uu; az=az+d; dlx=max(dlx,ww*d**2); uu=uu+d; > if(dlx.lt.shr) exit; if nlp.gt.maxit < jerr=-ilm; return;> > > if(nin.gt.nx) exit; euu=exp(sign(min(abs(uu),fmax),uu)); w=euu*q*exp(sign(min(abs(t),fmax),t)); ww=sum(w); wr=qy-w*(1.0-uu); tt=sum(wr); if ww*(az-az0)**2.lt.shr < kx=0; if kx.eq.0 < > if(kx.eq.1) go to :again:; exit; > > > if nin.gt.nx < jerr=-10000-ilm; exit;> if(nin.gt.0) ca(1:nin,ilm)=a(m(1:nin)); kin(ilm)=nin; a0(ilm)=az; alm(ilm)=al; lmu=ilm; dev(ilm)=(dot_product(qy,t)+yb*uu-ww-dv0)/dvr; if(ilm.lt.mnl) next; if(flmin.ge.1.0) next; me=0; if(me.gt.ne) exit; if((dev(ilm)-dev(ilm-mnl+1))/dev(ilm).lt.sml) exit; if(dev(ilm).gt.devmax) exit; > g=t+uu; :done:deallocate(t,w,wr,v,a,qy,xm,as,mm,ga,ixx); return; end; subroutine spdeviance(no,ni,x,ix,jx,y,g,q,nlam,a0,a,flog,jerr); implicit double precision(a-h,o-z); double precision x(*),y(no),g(no),q(no),a(ni,nlam),a0(nlam),flog(nlam); integer ix(*),jx(*); %fortran double precision, dimension (:), allocatable :: w,f %mortran if minval(y).lt.0.0 < jerr=8888; return;> allocate(w(1:no),stat=jerr); if(jerr.ne.0) return; allocate(f(1:no),stat=jerr); if(jerr.ne.0) return; w=max(0d0,q); sw=sum(w); if sw.le.0.0 < jerr=9999; go to :done:;> yb=dot_product(w,y)/sw; fmax=log(huge(y(1))*0.1); f=f+g; s=dot_product(w,y*f-exp(sign(min(abs(f),fmax),f))); flog(lam)=2.0*(sw*yb*(log(yb)-1.0)-s); > :done: deallocate(w,f); return; end; subroutine cspdeviance(no,x,ix,jx,y,g,q,nx,nlam,a0,ca,ia,nin,flog,jerr); implicit double precision(a-h,o-z); double precision x(*),y(no),g(no),q(no),ca(nx,nlam),a0(nlam),flog(nlam); integer ix(*),jx(*),nin(nlam),ia(nx); %fortran double precision, dimension (:), allocatable :: w,f %mortran if minval(y).lt.0.0 < jerr=8888; return;> allocate(w(1:no),stat=jerr); if(jerr.ne.0) return; allocate(f(1:no),stat=jerr); if(jerr.ne.0) return; w=max(0d0,q); sw=sum(w); if sw.le.0.0 < jerr=9999; go to :done:;> yb=dot_product(w,y)/sw; fmax=log(huge(y(1))*0.1); f=f+g; s=dot_product(w,y*f-exp(sign(min(abs(f),fmax),f))); flog(lam)=2.0*(sw*yb*(log(yb)-1.0)-s); > :done: deallocate(w,f); return; end; subroutine multelnet(parm,no,ni,nr,x,y,w,jd,vp,cl,ne,nx,nlam, flmin,ulam,thr,isd,jsd,intr,maxit, lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(no,ni),y(no,nr),w(no),vp(ni),ca(nx,nr,nlam); double precision ulam(nlam),a0(nr,nlam),rsq(nlam),alm(nlam),cl(2,ni); integer jd(*),ia(nx),nin(nlam); %fortran double precision, dimension (:), allocatable :: vq; %mortran if maxval(vp).le.0.0 < jerr=10000; return;> allocate(vq(1:ni),stat=jerr); if(jerr.ne.0) return; vq=max(0d0,vp); vq=vq*ni/sum(vq); call multelnetn(parm,no,ni,nr,x,y,w,jd,vq,cl,ne,nx,nlam,flmin,ulam,thr,isd, jsd,intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr); deallocate(vq); return; end; subroutine multelnetn(parm,no,ni,nr,x,y,w,jd,vp,cl,ne,nx,nlam,flmin,ulam,thr, isd,jsd,intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision vp(ni),x(no,ni),y(no,nr),w(no),ulam(nlam),cl(2,ni); double precision ca(nx,nr,nlam),a0(nr,nlam),rsq(nlam),alm(nlam); integer jd(*),ia(nx),nin(nlam); %fortran double precision, dimension (:), allocatable :: xm,xs,xv,ym,ys integer, dimension (:), allocatable :: ju double precision, dimension (:,:,:), allocatable :: clt allocate(clt(1:2,1:nr,1:ni),stat=jerr); if(jerr.ne.0) return %mortran allocate(xm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xs(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ym(1:nr),stat=jerr); if(jerr.ne.0) return; allocate(ys(1:nr),stat=jerr); if(jerr.ne.0) return; allocate(ju(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xv(1:ni),stat=jerr); if(jerr.ne.0) return; call chkvars(no,ni,x,ju); if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0; if maxval(ju).le.0 < jerr=7777; return;> call multstandard1(no,ni,nr,x,y,w,isd,jsd,intr,ju,xm,xs,ym,ys,xv,ys0,jerr); if(jerr.ne.0) return; >> if isd.gt.0 < >>> if jsd.gt.0 < >>> call multelnet2(parm,ni,nr,ju,vp,clt,y,no,ne,nx,x,nlam,flmin,ulam,thr,maxit,xv, ys0,lmu,ca,ia,nin,rsq,alm,nlp,jerr); if(jerr.gt.0) return; if intr.eq.0 < a0(j,k)=0.0;> else < a0(j,k)=ym(j)-dot_product(ca(1:nk,j,k),xm(ia(1:nk)));> > > deallocate(xm,xs,ym,ys,ju,xv,clt); return; end; subroutine multstandard1(no,ni,nr,x,y,w,isd,jsd,intr,ju, xm,xs,ym,ys,xv,ys0,jerr); implicit double precision(a-h,o-z); double precision x(no,ni),y(no,nr),w(no),xm(ni),xs(ni),xv(ni),ym(nr),ys(nr); integer ju(ni); %fortran double precision, dimension (:), allocatable :: v %mortran allocate(v(1:no),stat=jerr); if(jerr.ne.0) return; w=w/sum(w); v=sqrt(w); if intr.eq.0 < else < xs(j)=1.0; xv(j)=z;> > ys0=0.0; else < ys(j)=1.0; ys0=ys0+z;> > go to :out:; > if isd.eq.0 < xs=1.0;> else < xv=1.0; > ys0=0.0; else < ys0=ys0+z;> > if jsd.eq.0 < ys=1.0;> else < ys0=nr;> :out:deallocate(v); return; end; subroutine multelnet2(beta,ni,nr,ju,vp,cl,y,no,ne,nx,x,nlam,flmin,ulam,thri, maxit,xv,ys0,lmu,ao,ia,kin,rsqo,almo,nlp,jerr); implicit double precision(a-h,o-z); double precision vp(ni),y(no,nr),x(no,ni),ulam(nlam),ao(nx,nr,nlam); double precision rsqo(nlam),almo(nlam),xv(ni),cl(2,nr,ni); integer ju(ni),ia(nx),kin(nlam); %fortran double precision, dimension (:), allocatable :: g,gk,del,gj integer, dimension (:), allocatable :: mm,ix,isc double precision, dimension (:,:), allocatable :: a allocate(a(1:nr,1:ni),stat=jerr) if(jerr.ne.0) return %mortran call get_int_parms(sml,eps,big,mnlam,rsqmax,pmin,exmx,itrace); allocate(gj(1:nr),stat=jerr); if(jerr.ne.0) return; allocate(gk(1:nr),stat=jerr); if(jerr.ne.0) return; allocate(del(1:nr),stat=jerr); if(jerr.ne.0) return; allocate(mm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(g(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ix(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(isc(1:nr),stat=jerr); if(jerr.ne.0) return; bta=beta; omb=1.0-bta; ix=0; thr=thri*ys0/nr; "Begin: added by Naras" alf=1.0; "End: added by Naras" if flmin.lt.1.0 < eqs=max(eps,flmin); alf=eqs**(1.0/(nlam-1));> rsq=ys0; a=0.0; mm=0; /nlp,nin/=0; iz=0; mnl=min(mnlam,nlam); alm=0.0; g(j)=sqrt(g(j)); > elseif m.gt.2 < alm=alm*alf;> elseif m.eq.1 < alm=big;> else < alm0=0.0; alm0=alm0/max(bta,1.0d-3); alm=alf*alm0; > dem=alm*omb; ab=alm*bta; rsq0=rsq; jz=1; tlam=bta*(2.0*alm-alm0); loop < if(iz*jz.ne.0) go to :b:; :again:nlp=nlp+1; dlx=0.0; gkn=sqrt(gkn); u=1.0-ab*vp(k)/gkn; del=a(:,k); if u.le.0.0 < a(:,k)=0.0;> else < a(:,k)=gk*(u/(xv(k)+dem*vp(k))); call chkbnds(nr,gk,gkn,xv(k),cl(1,1,k), dem*vp(k),ab*vp(k),a(:,k),isc,jerr); if(jerr.ne.0) return; > del=a(:,k)-del; if(maxval(abs(del)).le.0.0) next; if mm(k).eq.0 < nin=nin+1; if(nin.gt.nx) exit; mm(k)=nin; ia(nin)=k; > > if(nin.gt.nx) exit; if dlx.lt.thr < ixx=0; g(k)=sqrt(g(k)); if g(k).gt.ab*vp(k) < ix(k)=1; ixx=1;> > if(ixx.eq.1) go to :again:; exit; > if nlp.gt.maxit < jerr=-m; return;> :b: iz=1; loop < nlp=nlp+1; dlx=0.0; gkn=sqrt(gkn); u=1.0-ab*vp(k)/gkn; del=a(:,k); if u.le.0.0 < a(:,k)=0.0;> else < a(:,k)=gk*(u/(xv(k)+dem*vp(k))); call chkbnds(nr,gk,gkn,xv(k),cl(1,1,k), dem*vp(k),ab*vp(k),a(:,k),isc,jerr); if(jerr.ne.0) return; > del=a(:,k)-del; if(maxval(abs(del)).le.0.0) next; > if(dlx.lt.thr) exit; if nlp.gt.maxit < jerr=-m; return;> > jz=0; > if nin.gt.nx < jerr=-10000-m; exit;> if nin.gt.0 < > kin(m)=nin; rsqo(m)=1.0-rsq/ys0; almo(m)=alm; lmu=m; if(m.lt.mnl) next; if(flmin.ge.1.0) next; me=0; if(me.gt.ne) exit; if(rsq0-rsq.lt.sml*rsq) exit; if(rsqo(m).gt.rsqmax) exit; > deallocate(a,mm,g,ix,del,gj,gk); return; end; subroutine chkbnds(nr,gk,gkn,xv,cl,al1,al2,a,isc,jerr); implicit double precision(a-h,o-z); double precision gk(nr),cl(2,nr),a(nr); integer isc(nr); kerr=0; al1p=1.0+al1/xv; al2p=al2/xv; isc=0; gsq=gkn**2; asq=dot_product(a,a); usq=0.0; "Begin: Added by Naras" u=0.0; kn=-1; "End: Added by Naras" loop < vmx=0.0; > if(vmx.le.0.0) exit; if(isc(kn).ne.0) exit; gsq=gsq-gk(kn)**2; g=sqrt(gsq)/xv; if(a(kn).lt.cl(1,kn)) u=cl(1,kn); if(a(kn).gt.cl(2,kn)) u=cl(2,kn); usq=usq+u**2; if usq.eq.0.0 < b=max(0d0,(g-al2p)/al1p);> else < b0=sqrt(asq-a(kn)**2); b=bnorm(b0,al1p,al2p,g,usq,kerr); if(kerr.ne.0) exit; > asq=usq+b**2; if asq.le.0.0 < a=0.0; exit;> a(kn)=u; isc(kn)=1; f=1.0/(xv*(al1p+al2p/sqrt(asq))); > if(kerr.ne.0) jerr=kerr; return; end; subroutine chkbnds1(nr,gk,gkn,xv,cl1,cl2,al1,al2,a,isc,jerr); implicit double precision(a-h,o-z); double precision gk(nr),a(nr); integer isc(nr); kerr=0; al1p=1.0+al1/xv; al2p=al2/xv; isc=0; gsq=gkn**2; asq=dot_product(a,a); usq=0.0; "Begin: added by Naras" u=0.0; kn=-1; "End: added by Naras" loop < vmx=0.0; > if(vmx.le.0.0) exit; if(isc(kn).ne.0) exit; gsq=gsq-gk(kn)**2; g=sqrt(gsq)/xv; if(a(kn).lt.cl1) u=cl1; if(a(kn).gt.cl2) u=cl2; usq=usq+u**2; if usq.eq.0.0 < b=max(0d0,(g-al2p)/al1p);> else < b0=sqrt(asq-a(kn)**2); b=bnorm(b0,al1p,al2p,g,usq,kerr); if(kerr.ne.0) exit; > asq=usq+b**2; if asq.le.0.0 < a=0.0; exit;> a(kn)=u; isc(kn)=1; f=1.0/(xv*(al1p+al2p/sqrt(asq))); > if(kerr.ne.0) jerr=kerr; return; end; function bnorm(b0,al1p,al2p,g,usq,jerr); implicit double precision(a-h,o-z); data thr,mxit /1.0d-10,100/; b=b0; zsq=b**2+usq; if zsq.le.0.0 < bnorm=0.0; return;> z=sqrt(zsq); f=b*(al1p+al2p/z)-g; jerr=0; z=sqrt(zsq); f=b*(al1p+al2p/z)-g; if(abs(f).le.thr) exit; if b.le.0.0 < b=0.0; exit;> > bnorm=b; if(it.ge.mxit) jerr=90000; return; "Begin: Edited by Naras" entry chg_bnorm(arg,irg); bnorm = 0.0; thr=arg; mxit=irg; return; entry get_bnorm(arg,irg); bnorm = 0.0; arg=thr; irg=mxit; return; "End: Edited by Naras" end; subroutine multsolns(ni,nx,nr,lmu,a,ia,nin,b); implicit double precision(a-h,o-z); double precision a(nx,nr,lmu),b(ni,nr,lmu); integer ia(nx),nin(lmu); return; end; subroutine multuncomp(ni,nr,nx,ca,ia,nin,a); implicit double precision(a-h,o-z); double precision ca(nx,nr),a(ni,nr); integer ia(nx); a=0.0; if nin.gt.0 < > return; end; subroutine multmodval(nx,nr,a0,ca,ia,nin,n,x,f); implicit double precision(a-h,o-z); double precision a0(nr),ca(nx,nr),x(n,*),f(nr,n); integer ia(nx); if(nin.le.0) return; > return; end; subroutine multspelnet(parm,no,ni,nr,x,ix,jx,y,w,jd,vp,cl,ne,nx, nlam,flmin,ulam,thr,isd, jsd,intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(*),y(no,nr),w(no),vp(ni),ulam(nlam),cl(2,ni); double precision ca(nx,nr,nlam),a0(nr,nlam),rsq(nlam),alm(nlam); integer ix(*),jx(*),jd(*),ia(nx),nin(nlam); %fortran double precision, dimension (:), allocatable :: vq; %mortran if maxval(vp).le.0.0 < jerr=10000; return;> allocate(vq(1:ni),stat=jerr); if(jerr.ne.0) return; vq=max(0d0,vp); vq=vq*ni/sum(vq); call multspelnetn(parm,no,ni,nr,x,ix,jx,y,w,jd,vq,cl,ne,nx,nlam,flmin, ulam,thr,isd,jsd,intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr); deallocate(vq); return; end; subroutine multspelnetn(parm,no,ni,nr,x,ix,jx,y,w,jd,vp,cl,ne,nx,nlam,flmin, ulam,thr,isd,jsd,intr,maxit,lmu,a0,ca,ia,nin,rsq,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(*),vp(ni),y(no,nr),w(no),ulam(nlam),cl(2,ni); double precision ca(nx,nr,nlam),a0(nr,nlam),rsq(nlam),alm(nlam); integer ix(*),jx(*),jd(*),ia(nx),nin(nlam); %fortran double precision, dimension (:), allocatable :: xm,xs,xv,ym,ys integer, dimension (:), allocatable :: ju double precision, dimension (:,:,:), allocatable :: clt allocate(clt(1:2,1:nr,1:ni),stat=jerr) if(jerr.ne.0) return %mortran allocate(xm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xs(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ym(1:nr),stat=jerr); if(jerr.ne.0) return; allocate(ys(1:nr),stat=jerr); if(jerr.ne.0) return; allocate(ju(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(xv(1:ni),stat=jerr); if(jerr.ne.0) return; call spchkvars(no,ni,x,ix,ju); if(jd(1).gt.0) ju(jd(2:(jd(1)+1)))=0; if maxval(ju).le.0 < jerr=7777; return;> call multspstandard1(no,ni,nr,x,ix,jx,y,w,ju,isd,jsd,intr, xm,xs,ym,ys,xv,ys0,jerr); if(jerr.ne.0) return; >> if isd.gt.0 < >>> if jsd.gt.0 < >>> call multspelnet2(parm,ni,nr,y,w,no,ne,nx,x,ix,jx,ju,vp,clt,nlam,flmin, ulam,thr,maxit,xm,xs,xv,ys0,lmu,ca,ia,nin,rsq,alm,nlp,jerr); if(jerr.gt.0) return; if intr.eq.0 < a0(j,k)=0.0;> else < a0(j,k)=ym(j)-dot_product(ca(1:nk,j,k),xm(ia(1:nk)));> > > deallocate(xm,xs,ym,ys,ju,xv,clt); return; end; subroutine multspstandard1(no,ni,nr,x,ix,jx,y,w,ju,isd,jsd,intr, xm,xs,ym,ys,xv,ys0,jerr); implicit double precision(a-h,o-z); double precision x(*),y(no,nr),w(no),xm(ni),xs(ni),xv(ni),ym(nr),ys(nr); integer ix(*),jx(*),ju(ni); "Start: Naras Edit" jerr = jerr*1; "End: Naras Edit" w=w/sum(w); if intr.eq.0 < else < xs(j)=1.0; xv(j)=z;> > ys0=0.0; else < ys(j)=1.0; ys0=ys0+z;> > return; > if isd.eq.0 < xs=1.0;> else < xv=1.0;> ys0=0.0; else < ys0=ys0+z;> > if jsd.eq.0 < ys=1.0;> else < ys0=nr;> return; end; subroutine multspelnet2(beta,ni,nr,y,w,no,ne,nx,x,ix,jx,ju,vp,cl,nlam,flmin, ulam,thri,maxit,xm,xs,xv,ys0,lmu,ao,ia,kin,rsqo,almo,nlp,jerr); implicit double precision(a-h,o-z); double precision y(no,nr),w(no),x(*),vp(ni),ulam(nlam),cl(2,nr,ni); double precision ao(nx,nr,nlam),rsqo(nlam),almo(nlam),xm(ni),xs(ni),xv(ni); integer ix(*),jx(*),ju(ni),ia(nx),kin(nlam); %fortran double precision, dimension (:), allocatable :: g,gj,gk,del,o integer, dimension (:), allocatable :: mm,iy,isc double precision, dimension (:,:), allocatable :: a allocate(a(1:nr,1:ni),stat=jerr) if(jerr.ne.0) return %mortran call get_int_parms(sml,eps,big,mnlam,rsqmax,pmin,exmx,itrace); allocate(mm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(g(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(gj(1:nr),stat=jerr); if(jerr.ne.0) return; allocate(gk(1:nr),stat=jerr); if(jerr.ne.0) return; allocate(del(1:nr),stat=jerr); if(jerr.ne.0) return; allocate(o(1:nr),stat=jerr); if(jerr.ne.0) return; allocate(iy(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(isc(1:nr),stat=jerr); if(jerr.ne.0) return; bta=beta; omb=1.0-bta; alm=0.0; iy=0; thr=thri*ys0/nr; "Begin: added by Naras" alf=1.0; "End: added by Naras" if flmin.lt.1.0 < eqs=max(eps,flmin); alf=eqs**(1.0/(nlam-1));> rsq=ys0; a=0.0; mm=0; o=0.0; /nlp,nin/=0; iz=0; mnl=min(mnlam,nlam); g(j)=sqrt(g(j)); > elseif m.gt.2 < alm=alm*alf;> elseif m.eq.1 < alm=big;> else < alm0=0.0; alm0=alm0/max(bta,1.0d-3); alm=alf*alm0; > dem=alm*omb; ab=alm*bta; rsq0=rsq; jz=1; tlam=bta*(2.0*alm-alm0); loop < if(iz*jz.ne.0) go to :b:; :again:nlp=nlp+1; dlx=0.0; gkn=sqrt(gkn); u=1.0-ab*vp(k)/gkn; del=a(:,k); if u.le.0.0 < a(:,k)=0.0;> else < a(:,k)=gk*(u/(xv(k)+dem*vp(k))); call chkbnds(nr,gk,gkn,xv(k),cl(1,1,k), dem*vp(k),ab*vp(k),a(:,k),isc,jerr); if(jerr.ne.0) return; > del=a(:,k)-del; if(maxval(abs(del)).le.0.0) next; if mm(k).eq.0 < nin=nin+1; if(nin.gt.nx) exit; mm(k)=nin; ia(nin)=k; > > if(nin.gt.nx) exit; if dlx.lt.thr < ixx=0; g(j)=sqrt(g(j)); if g(j).gt.ab*vp(j) < iy(j)=1; ixx=1;> > if(ixx.eq.1) go to :again:; exit; > if nlp.gt.maxit < jerr=-m; return;> :b: iz=1; loop < nlp=nlp+1; dlx=0.0; gkn=sqrt(gkn); u=1.0-ab*vp(k)/gkn; del=a(:,k); if u.le.0.0 < a(:,k)=0.0;> else < a(:,k)=gk*(u/(xv(k)+dem*vp(k))); call chkbnds(nr,gk,gkn,xv(k),cl(1,1,k), dem*vp(k),ab*vp(k),a(:,k),isc,jerr); if(jerr.ne.0) return; > del=a(:,k)-del; if(maxval(abs(del)).le.0.0) next; > if(dlx.lt.thr) exit; if nlp.gt.maxit < jerr=-m; return;> > jz=0; > if nin.gt.nx < jerr=-10000-m; exit;> if nin.gt.0 < > kin(m)=nin; rsqo(m)=1.0-rsq/ys0; almo(m)=alm; lmu=m; if(m.lt.mnl) next; if(flmin.ge.1.0) next; me=0; if(me.gt.ne) exit; if(rsq0-rsq.lt.sml*rsq) exit; if(rsqo(m).gt.rsqmax) exit; > deallocate(a,mm,g,iy,gj,gk,del,o); return; end; subroutine multlognetn(parm,no,ni,nc,x,y,g,w,ju,vp,cl,ne,nx,nlam,flmin,ulam, shri,intr,maxit,xv,lmu,a0,a,m,kin,dev0,dev,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(no,ni),y(no,nc),g(no,nc),w(no),vp(ni),ulam(nlam),cl(2,ni); double precision a(nx,nc,nlam),a0(nc,nlam),dev(nlam),alm(nlam),xv(ni); integer ju(ni),m(nx),kin(nlam); %fortran double precision, dimension (:,:), allocatable :: q,r,b,bs double precision, dimension (:), allocatable :: sxp,sxpl,ga,gk,del integer, dimension (:), allocatable :: mm,is,ixx,isc allocate(b(0:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(bs(0:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(q(1:no,1:nc),stat=jerr) if(jerr.ne.0) return allocate(r(1:no,1:nc),stat=jerr) if(jerr.ne.0) return %mortran call get_int_parms(sml,eps,big,mnlam,devmax,pmin,exmx,itrace); exmn=-exmx; allocate(mm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(is(1:max(nc,ni)),stat=jerr); if(jerr.ne.0) return; allocate(sxp(1:no),stat=jerr); if(jerr.ne.0) return; allocate(sxpl(1:no),stat=jerr); if(jerr.ne.0) return; allocate(ga(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ixx(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(gk(1:nc),stat=jerr); if(jerr.ne.0) return; allocate(del(1:nc),stat=jerr); if(jerr.ne.0) return; allocate(isc(1:nc),stat=jerr); if(jerr.ne.0) return; pmax=1.0-pmin; emin=pmin/pmax; emax=1.0/emin; bta=parm; omb=1.0-bta; dev1=0.0; dev0=0.0; if q0.ge.pmax < jerr =9000+ic; return;> if intr.eq.0 < q0=1.0/nc; b(0,ic)=0.0;> else < b(0,ic)=log(q0); dev1=dev1-q0*b(0,ic);> b(1:ni,ic)=0.0; > if(intr.eq.0) dev1=log(float(nc)); ixx=0; al=0.0; if nonzero(no*nc,g).eq.0 < b(0,:)=b(0,:)-sum(b(0,:))/nc; sxp=0.0; > else < sxp=0.0; if intr.eq.0 < b(0,:)=0.0;> else < call kazero(nc,no,y,g,w,b(0,:),jerr); if(jerr.ne.0) return;> dev1=0.0; sxpl=w*log(sxp); > > dev0=dev0+dev1; "Begin: added by Naras" alf=1.0; "End: added by Naras" if flmin.lt.1.0 < eqs=max(eps,flmin); alf=eqs**(1.0/(nlam-1));> m=0; mm=0; nin=0; nlp=0; mnl=min(mnlam,nlam); bs=0.0; shr=shri*dev0; ga=0.0; > ga=sqrt(ga); elseif ilm.gt.2 < al=al*alf;> elseif ilm.eq.1 < al=big;> else < al0=0.0; al0=al0/max(bta,1.0d-3); al=alf*al0; > al2=al*omb; al1=al*bta; tlam=bta*(2.0*al-al0); :again:continue; loop < /ix,jx,kx/=0; t=0.0; if t.lt.eps < kx=1; exit;> t=2.0*t; alt=al1/t; al2t=al2/t; > loop < nlp=nlp+nc; dlx=0.0; gkn=sqrt(gkn); u=1.0-alt*vp(k)/gkn; del=b(k,:); if u.le.0.0 < b(k,:)=0.0;> else < b(k,:)=gk*(u/(xv(k)+vp(k)*al2t)); call chkbnds1(nc,gk,gkn,xv(k),cl(1,k), cl(2,k),vp(k)*al2t,alt*vp(k),b(k,:),isc,jerr); if(jerr.ne.0) return; > del=b(k,:)-del; if(maxval(abs(del)).le.0.0) next; if mm(k).eq.0 < nin=nin+1; if nin.gt.nx < jx=1; exit;> mm(k)=nin; m(nin)=k; > > if(jx.gt.0) exit; if(dlx.lt.shr) exit; if nlp.gt.maxit < jerr=-ilm; return;> loop < nlp=nlp+nc; dlx=0.0; gkn=sqrt(gkn); u=1.0-alt*vp(k)/gkn; del=b(k,:); if u.le.0.0 < b(k,:)=0.0;> else < b(k,:)=gk*(u/(xv(k)+vp(k)*al2t)); call chkbnds1(nc,gk,gkn,xv(k),cl(1,k), cl(2,k),vp(k)*al2t,alt*vp(k),b(k,:),isc,jerr); if(jerr.ne.0) return; > del=b(k,:)-del; if(maxval(abs(del)).le.0.0) next; > if(dlx.lt.shr) exit; if nlp.gt.maxit < jerr=-ilm; return;> > > if(jx.gt.0) exit; > > > s=-sum(b(0,:))/nc; b(0,:)=b(0,:)+s; if(jx.gt.0) exit; if ix.eq.0 < > ga=sqrt(ga); > if(ix.eq.1) go to :again:; exit; > > if kx.gt.0 < jerr=-20000-ilm; exit;> if jx.gt.0 < jerr=-10000-ilm; exit;> devi=0.0; > kin(ilm)=nin; alm(ilm)=al; lmu=ilm; dev(ilm)=(dev1-devi)/dev0; if(ilm.lt.mnl) next; if(flmin.ge.1.0) next; me=0; if(me.gt.ne) exit; if(dev(ilm).gt.devmax) exit; if(dev(ilm)-dev(ilm-1).lt.sml) exit; > g=log(q); deallocate(sxp,b,bs,r,q,mm,is,ga,ixx,gk,del,sxpl); return; end; subroutine multsprlognetn(parm,no,ni,nc,x,ix,jx,y,g,w,ju,vp,cl,ne,nx,nlam, flmin,ulam,shri,intr,maxit,xv,xb,xs,lmu,a0,a,m,kin,dev0,dev,alm,nlp,jerr); implicit double precision(a-h,o-z); double precision x(*),y(no,nc),g(no,nc),w(no),vp(ni); double precision ulam(nlam),xb(ni),xs(ni),xv(ni); double precision a(nx,nc,nlam),a0(nc,nlam),dev(nlam),alm(nlam),cl(2,ni); integer ix(*),jx(*),ju(ni),m(nx),kin(nlam); %fortran double precision, dimension (:,:), allocatable :: q,r,b,bs double precision, dimension (:), allocatable :: sxp,sxpl,ga,gk double precision, dimension (:), allocatable :: del,sc,svr integer, dimension (:), allocatable :: mm,is,iy,isc allocate(b(0:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(bs(0:ni,1:nc),stat=jerr) if(jerr.ne.0) return allocate(q(1:no,1:nc),stat=jerr) if(jerr.ne.0) return allocate(r(1:no,1:nc),stat=jerr) if(jerr.ne.0) return %mortran call get_int_parms(sml,eps,big,mnlam,devmax,pmin,exmx,itrace); exmn=-exmx; allocate(mm(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(ga(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(gk(1:nc),stat=jerr); if(jerr.ne.0) return; allocate(del(1:nc),stat=jerr); if(jerr.ne.0) return; allocate(iy(1:ni),stat=jerr); if(jerr.ne.0) return; allocate(is(1:max(nc,ni)),stat=jerr); if(jerr.ne.0) return; allocate(sxp(1:no),stat=jerr); if(jerr.ne.0) return; allocate(sxpl(1:no),stat=jerr); if(jerr.ne.0) return; allocate(svr(1:nc),stat=jerr); if(jerr.ne.0) return; allocate(sc(1:no),stat=jerr); if(jerr.ne.0) return; allocate(isc(1:nc),stat=jerr); if(jerr.ne.0) return; pmax=1.0-pmin; emin=pmin/pmax; emax=1.0/emin; bta=parm; omb=1.0-bta; dev1=0.0; dev0=0.0; if q0.ge.pmax < jerr =9000+ic; return;> b(1:ni,ic)=0.0; if intr.eq.0 < q0=1.0/nc; b(0,ic)=0.0;> else < b(0,ic)=log(q0); dev1=dev1-q0*b(0,ic);> > if(intr.eq.0) dev1=log(float(nc)); iy=0; al=0.0; if nonzero(no*nc,g).eq.0 < b(0,:)=b(0,:)-sum(b(0,:))/nc; sxp=0.0; > else < sxp=0.0; if intr.eq.0 < b(0,:)=0.0;> else < call kazero(nc,no,y,g,w,b(0,:),jerr); if(jerr.ne.0) return;> dev1=0.0; sxpl=w*log(sxp); > > dev0=dev0+dev1; "Begin: added by Naras" alf=1.0; "End: added by Naras" if flmin.lt.1.0 < eqs=max(eps,flmin); alf=eqs**(1.0/(nlam-1));> m=0; mm=0; nin=0; nlp=0; mnl=min(mnlam,nlam); bs=0.0; shr=shri*dev0; ga=0.0; > ga=sqrt(ga); elseif ilm.gt.2 < al=al*alf;> elseif ilm.eq.1 < al=big;> else < al0=0.0; al0=al0/max(bta,1.0d-3); al=alf*al0; > al2=al*omb; al1=al*bta; tlam=bta*(2.0*al-al0); :again:continue; loop < /ixx,jxx,kxx/=0; t=0.0; if t.lt.eps < kxx=1; exit;> t=2.0*t; alt=al1/t; al2t=al2/t; > loop < nlp=nlp+nc; dlx=0.0; gkn=sqrt(gkn); u=1.0-alt*vp(k)/gkn; if u.le.0.0 < b(k,:)=0.0;> else < b(k,:)=gk*(u/(xv(k)+vp(k)*al2t)); call chkbnds1(nc,gk,gkn,xv(k),cl(1,k),cl(2,k), vp(k)*al2t,alt*vp(k),b(k,:),isc,jerr); if(jerr.ne.0) return; > del=b(k,:)-del; if(maxval(abs(del)).le.0.0) next; if mm(k).eq.0 < nin=nin+1; if nin.gt.nx < jxx=1; exit;> mm(k)=nin; m(nin)=k; > > if(jxx.gt.0) exit; if(dlx.lt.shr) exit; if nlp.gt.maxit < jerr=-ilm; return;> loop < nlp=nlp+nc; dlx=0.0; gkn=sqrt(gkn); u=1.0-alt*vp(k)/gkn; if u.le.0.0 < b(k,:)=0.0;> else < b(k,:)=gk*(u/(xv(k)+vp(k)*al2t)); call chkbnds1(nc,gk,gkn,xv(k),cl(1,k),cl(2,k), vp(k)*al2t,alt*vp(k),b(k,:),isc,jerr); if(jerr.ne.0) return; > del=b(k,:)-del; if(maxval(abs(del)).le.0.0) next; > if(dlx.lt.shr) exit; if nlp.gt.maxit < jerr=-ilm; return;> > > if(jxx.gt.0) exit; > > sc=b(0,ic)+g(:,ic); b0=0.0; sc=min(max(exmn,sc+b0),exmx); sxp=sxp-q(:,ic); q(:,ic)=min(max(emin*sxp,exp(sc)),emax*sxp); sxp=sxp+q(:,ic); > s=sum(b(0,:))/nc; b(0,:)=b(0,:)-s; if(jxx.gt.0) exit; if ixx.eq.0 < > ga=sqrt(ga); > if(ixx.eq.1) go to :again:; exit; > > if kxx.gt.0 < jerr=-20000-ilm; exit;> if jxx.gt.0 < jerr=-10000-ilm; exit;> devi=0.0; > kin(ilm)=nin; alm(ilm)=al; lmu=ilm; dev(ilm)=(dev1-devi)/dev0; if(ilm.lt.mnl) next; if(flmin.ge.1.0) next; me=0; if(me.gt.ne) exit; if(dev(ilm).gt.devmax) exit; if(dev(ilm)-dev(ilm-1).lt.sml) exit; > g=log(q); deallocate(sxp,b,bs,r,q,mm,is,sc,ga,iy,gk,del,sxpl); return; end; %fortran subroutine psort7(v,a,ii,jj) implicit double precision(a-h,o-z) c c puts into a the permutation vector which sorts v into c increasing order. the array v is not modified. c only elements from ii to jj are considered. c arrays iu(k) and il(k) permit sorting up to 2**(k+1)-1 elements c c this is a modification of cacm algorithm #347 by r. c. singleton, c which is a modified hoare quicksort. c dimension a(jj),v(jj),iu(20),il(20) integer t,tt integer a double precision v m=1 i=ii j=jj 10 if (i.ge.j) go to 80 20 k=i ij=(j+i)/2 t=a(ij) vt=v(t) if (v(a(i)).le.vt) go to 30 a(ij)=a(i) a(i)=t t=a(ij) vt=v(t) 30 l=j if (v(a(j)).ge.vt) go to 50 a(ij)=a(j) a(j)=t t=a(ij) vt=v(t) if (v(a(i)).le.vt) go to 50 a(ij)=a(i) a(i)=t t=a(ij) vt=v(t) go to 50 40 a(l)=a(k) a(k)=tt 50 l=l-1 if (v(a(l)).gt.vt) go to 50 tt=a(l) vtt=v(tt) 60 k=k+1 if (v(a(k)).lt.vt) go to 60 if (k.le.l) go to 40 if (l-i.le.j-k) go to 70 il(m)=i iu(m)=l i=k m=m+1 go to 90 70 il(m)=k iu(m)=j j=l m=m+1 go to 90 80 m=m-1 if (m.eq.0) return i=il(m) j=iu(m) 90 if (j-i.gt.10) go to 20 if (i.eq.ii) go to 10 i=i-1 100 i=i+1 if (i.eq.j) go to 80 t=a(i+1) vt=v(t) if (v(a(i)).le.vt) go to 100 k=i 110 a(k+1)=a(k) k=k-1 if (vt.lt.v(a(k))) go to 110 a(k+1)=t go to 100 end %% glmnet/inst/mortran/README.Rmd0000644000176200001440000000521513542237467015620 0ustar liggesusers# Changes in Mortran and R ## Tracing progress fixes in Mortran and R 1. Changed the function definition ``` subroutine get_int_parms(sml,eps,big,mnlam,rsqmax,pmin,exmx); ``` to ``` subroutine get_int_parms(sml,eps,big,mnlam,rsqmax,pmin,exmx,itrace); ``` taking on itrace as a switch for tracing progress. Added an `entry chg_itrace` to change `itrace`. 2. Changed all calls to `get_int_parms` to include extra parameter. 3. Corresponding changes to `glmnet.control.R` to include `itrace` as a parameter. 4. Fixes in `relax.glmnet.R` for displaying the progress by temporarily turning off and on tracing, if it was on. ## Unused variable fixes in Mortran. 1. The subroutines `spstandard`, `spstandard1`, `multspstandard1` had `jerr` as an unused argument. 2. The subroutines `coxnet1`, `fishnet1`, `spfishnet1` had `isd` as an unused argument. 3. The subroutine `risk` had `ni` as an unusued argument. In all these cases, I merely added a _no-op_ statement to the function/subroutine (e.g. `jerr = jerr * 1` or `ni = ni * 1`) to stop compiler warnings. More advanced compilers may detect these shenanigans. We can address that later if it comes up. ## Some extraneous tabs in Mortran. 1. The subroutine `multstandard1` has an extra tabl between the arguments `ju` and `xm` that I removed. This is most likely because Emacs replaces 8 spaces with tabs. ## Uninitialized variable in Mortran 1. The `gfortran` compiler warns that the variable `alf` is uninitialized in `elnet1`. The logic is correct, but the compiler is unable to detect it. So I initialized `alf=1` and combined it with a previous fix for `alm=0.0` in `elnet1`. I am reasonably certain this will not affect any logic. ## Generating Fortran from Mortran Note that it is Mortran processing that generates unusued labels. So each time you run Mortran to generate the Fortran, you will have to deal with the problem of unused labels. No worries, however, if you follow this procedure. 1. Install the latest [`SUtools`](https://github.com/bnaras/SUtools) package, after deleting your old one, if you had it. 2. Execute the following, assuming `GLMNET_DIR` is your `glmnet` package directory. ```{r, eval = FALSE} library(SUtools) GLMNET_DIR <- "~/GitHub/glmnet" mortran <- readLines(file.path(GLMNET_DIR, "inst", "mortran", "glmnet5dpclean.m")) fortran <- generate_fortran(mortran) fixed_fortran <- fix_unused_labels(fortran) ``` The fixed fortran file can now be written out to a file. For example, you may choose to overwrite the old fortran with the new fortran. ```{r, eval = FALSE} writeLines(fixed_fortran, file.path(GLMNET_DIR, "src", "glmnet5dpclean.f")) ``` glmnet/inst/doc/0000755000176200001440000000000013574046177013300 5ustar liggesusersglmnet/inst/doc/relax.R0000644000176200001440000001067113574046174014540 0ustar liggesusers## ------------------------------------------------------------------------ library(glmnet) data(QuickStartExample) fit=glmnet(x,y, relax=TRUE) print(fit) ## ------------------------------------------------------------------------ par(mfrow=c(1,3)) plot(fit) plot(fit,gamma=0.5) plot(fit,gamma=0) ## ------------------------------------------------------------------------ cfit=cv.glmnet(x,y,relax=TRUE) plot(cfit) ## ---- eval=FALSE--------------------------------------------------------- # predict(cvfit,newx) ## ------------------------------------------------------------------------ print(cfit) ## ----`relaxed`----------------------------------------------------------- fit=glmnet(x,y) fitr=relax.glmnet(fit,x=x,y=y) ## ------------------------------------------------------------------------ print(cfit) print.cv.glmnet(cfit) ## ------------------------------------------------------------------------ fitr=cv.glmnet(x,y,gamma=0,relax=TRUE) plot(fitr) ## ---- eval=FALSE--------------------------------------------------------- # fit=glmnet(x,y,trace=TRUE) ## ---- eval=FALSE--------------------------------------------------------- # fit=cv.glmnet(x,y,trace=TRUE) ## ---- eval=FALSE--------------------------------------------------------- # glmnet.control(itrace=1) ## ------------------------------------------------------------------------ data(CoxExample) ## ------------------------------------------------------------------------ cvfit=cv.glmnet(x,y,family="cox",type.measure="C") plot(cvfit) ## ------------------------------------------------------------------------ data(BinomialExample) itrain=1:70 fit=glmnet(x[itrain,],y[itrain],family="binomial",nlambda=20) assess.glmnet(fit,newx=x[-itrain,],newy=y[-itrain]) ## ---- eval=FALSE--------------------------------------------------------- # pred=predict(fit,newx=x[-itrain,]) # assess.glmnet(pred,newy=y[-itrain],family="binomial") ## ------------------------------------------------------------------------ glmnet.measures() ## ------------------------------------------------------------------------ cfit=cv.glmnet(x[itrain,],y[itrain],family="binomial", nlambda = 30) assess.glmnet(cfit,newx=x[-itrain,],newy=y[-itrain]) ## ------------------------------------------------------------------------ assess.glmnet(cfit,newx=x[-itrain,],newy=y[-itrain], s="lambda.min") ## ------------------------------------------------------------------------ cfit=cv.glmnet(x,y,family="binomial",keep=TRUE, nlambda = 30) assess.glmnet(cfit$fit.preval,newy=y,family="binomial") ## ------------------------------------------------------------------------ cfit=cv.glmnet(x,y,family="binomial", type.measure="auc", keep=TRUE) rocs=roc.glmnet(cfit$fit.preval,newy=y) which=match(cfit$lambda.min,cfit$lambda) plot(rocs[[which]],type="l") nopr=sapply(rocs,lines,col="grey") lines(rocs[[which]],lwd=2,col="red") ## ------------------------------------------------------------------------ data(MultinomialExample) set.seed(101) itrain=sample(1:500,400,replace=FALSE) cfit=cv.glmnet(x[itrain,],y[itrain],family="multinomial") cnf=confusion.glmnet(cfit,newx=x[-itrain,],newy=y[-itrain]) print(cnf) ## ------------------------------------------------------------------------ cfit=cv.glmnet(x,y,family="multinomial",type="class",keep=TRUE) cnf=confusion.glmnet(cfit$fit.preval,newy=y,family="multinomial") which=match(cfit$lambda.min,cfit$lambda) print(cnf[[which]]) ## ------------------------------------------------------------------------ data(BinomialExample) fit=bigGlm(x,y,family="binomial",lower.limits=-1) print(fit) ## ------------------------------------------------------------------------ set.seed(101) X = matrix(rnorm(20),10,2) X3=sample(letters[1:3],10,replace=TRUE) X4=sample(LETTERS[1:3],10,replace=TRUE) df=data.frame(X,X3,X4) makeX(df) ## ------------------------------------------------------------------------ makeX(df,sparse=TRUE) ## ------------------------------------------------------------------------ Xn=X Xn[3,1]=NA;Xn[5,2]=NA X3n=X3; X3n[6]=NA X4n=X4 X4n[9]=NA dfn=data.frame(Xn,X3n,X4n) makeX(dfn) ## ------------------------------------------------------------------------ makeX(dfn,na.impute=TRUE,sparse=TRUE) ## ------------------------------------------------------------------------ X = matrix(rnorm(10),5,2) X3=sample(letters[1:3],5,replace=TRUE) X4=sample(LETTERS[1:3],5,replace=TRUE) Xn=X Xn[3,1]=NA;Xn[5,2]=NA X3n=X3; X3n[1]=NA X4n=X4 X4n[2]=NA dftn=data.frame(Xn,X3n,X4n) makeX(dfn,dftn,na.impute=TRUE, sparse=TRUE) glmnet/inst/doc/Coxnet.Rmd0000644000176200001440000000677113556622666015222 0ustar liggesusers--- title: "Coxnet: Regularized Cox Regression" author: "Noah Simon, Jerome Friedman, Trevor Hastie and Rob Tibshirani" date: '`r Sys.Date()`' bibliography: assets/glmnet_refs.bib link-citations: true output: pdf_document: fig_caption: yes toc: yes toc_depth: 3 vignette: > %\VignetteIndexEntry{Coxnet: Regularized Cox Regression} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} --- ## Introduction We will give a short tutorial on using coxnet. Coxnet is a function which fits the Cox Model regularized by an elastic net penalty. It is used for underdetermined (or nearly underdetermined systems) and chooses a small number of covariates to include in the model. Because the Cox Model is rarely used for actual prediction, we will rather focus on finding and interpretating an appropriate model. We give a simple example of how to format data and run the Cox Model in glmnet with cross validation. Further details may be found in @coxnet, @strongrules and @block. ## Example We first load our data and set up the response. In this case $x$ must be an $n$ by $p$ matrix of covariate values --- each row corresponds to a patient and each column a covariate. $y$ is an $n$ length vector of failure/censoring times, and status is an $n$ length vector with each entry, a $1$ or a $0$, indicating whether the corresponding entry in $y$ is indicative of a failure time or right censoring time ($1$ for failure, $0$ for censoring) ```{r} library("glmnet") library("survival") patient.data <- readRDS("assets/coxnet.RDS") ``` We then call our functions to fit with the lasso penalty ($\alpha=1$), and cross validate. We set maxit = 1000 (increasing the maximum number of iterations to $1000$) because our data is relatively high dimensional, so more iterations are needed for convergence. In practice, the function will spit out an error if convergence isn't reached by the maximum number of iterations. ```{r, warning = TRUE} cv.fit <- cv.glmnet(patient.data$x, Surv(patient.data$time, patient.data$status), family="cox", maxit = 1000) fit <- glmnet(patient.data$x, Surv(patient.data$time,patient.data$status), family = "cox", maxit = 1000) ``` The Surv function packages the survival data into the form expected by glmnet. Once fit, we can view the optimal $\lambda$ value and a cross validated error plot to help evaluate our model. ```{r} plot(cv.fit) cv.fit$lambda.min ``` The left vertical line in our plot shows us where the CV-error curve hits its minimum. The right vertical line shows us the most regularized model with CV-error within$1$standard deviation of the minimum. In this case, we see that the minimum was achieved by a fairly regularized model, however the right line indicates that the null model (no coefficients included) is within$1$sd of the minimum. This might lead us to believe that in actuality the covariates are not explaining any variability. For the time being we will concern ourselves with the minimum CV-error model. We can check which covariates our model chose to be active, and see the coefficients of those covariates. ```{r} Coefficients <- coef(fit, s = cv.fit$lambda.min) Active.Index <- which(Coefficients != 0) Active.Coefficients <- Coefficients[Active.Index] ``` `coef(fit, s = cv.fit\$lambda.min)` returns the $p$ length coefficient vector of the solution corresponding to $\lambda =$`cv.fit$lambda.min`. ```{r} Active.Index Active.Coefficients ``` We see that our optimal model chose 2 active covariates ($X80$ and $X394$) each with a small positive effect on hazard. ## References glmnet/inst/doc/relax.Rmd0000644000176200001440000003516613560660761015066 0ustar liggesusers--- title: "Relaxed fits and other additions in `glmnet` 3.0" author: "Trevor Hastie, Balasubramanian Narasimhan and Rob Tibshirani" date: "October 15, 2019" bibliography: assets/glmnet_refs.bib link-citations: true output: pdf_document: fig_caption: yes toc: yes toc_depth: 3 vignette: > %\VignetteIndexEntry{Relaxed fits} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} --- ## Introduction In our vignette "glmnet" we give details for fitting lasso and elastic-net regularized models, for CV and various aspects of glmnet modeling. In this vignette, we highlight some of the new tools and features in the major revision glmnet 3.0. The main edition is the introduction of the *relaxed lasso*. The idea is to take a glmnet fitted object, and then for each lambda, refit the variables in the active set without any penalization. This gives the `relaxed` fit (note, there have been other definitions of a relaxed fit, but this is the one we prefer). This could of course be done for elastic net fits as well as lasso. However, if the number of variables gets too close to the sample size N, the relaxed path will be truncated. Furthermore, for binomial and other nonlinear GLMs convergence can be an issue with our current implementation if the number of variables is too large, and perversely if the relaxed fit is too strong. Suppose the `glmnet` fitted linear predictor at $\lambda$ is $\hat\eta_\lambda(x)$ and the relaxed version is $\tilde \eta_\lambda(x)$. We also allow for shrinkage between the two: $$\tilde \eta_{\lambda,\gamma}=(1-\gamma)\tilde \eta_\lambda(x)+\gamma\hat\eta_\lambda(x).$$ $\gamma\in[0,1]$ is an additional tuning parameter which can be selected by cross validation. The debiasing will potentially improve prediction performance, and CV will typically select a model with a smaller number of variables. This procedure is very competitive with forward-stepwise and best-subset regression, and has a considerable speed advantage when the number of variables is large. This is especially true for best-subset, but even so for forward stepwise. The latter has to plod through the variables one-at-a-time, while glmnet will just plunge in and find a good active set. Further details may be found in @glmnet, @coxnet, @strongrules, @block and @best_subset. ## Simple relaxed fit To get things going, we show the most basic use. We use the same data used in the `glmnet` vignette. ```{r} library(glmnet) data(QuickStartExample) fit=glmnet(x,y, relax=TRUE) print(fit) ``` There is an extra column `%Dev R` where the `R` stands for "relaxed", which is the percent deviance explained by the relaxed fit. This is always higher than its neighboring column, which is the same for the penalized fit (on the training data). The fit object is class `relaxed`, which inherits from class `glmnet`. One can plot it, with additional flexibility. ```{r} par(mfrow=c(1,3)) plot(fit) plot(fit,gamma=0.5) plot(fit,gamma=0) ``` So again, `gamma=1` is the traditional `glmnet` fit, while `gamma=0` is the unpenalized fit, and `gamma=0.5` is a mixture of the two (at the coefficient level, and hence also the linear predictors). We can also select `gamma` using `cv.glmnet`, which by default uses the 5 values `c(0, 0.25, 0.5, 0.75, 1)`. ```{r} cfit=cv.glmnet(x,y,relax=TRUE) plot(cfit) ``` The plot command has an `se.bands` option if you don't like the default shading of these bands. Just like before, you can make predictions from a CV object, and it uses the selected values for `lambda` and `gamma`. ```{r, eval=FALSE} predict(cvfit,newx) ``` A new feature in `glmnet` is a print method for `cv.glmnet` and a `cv.relaxed` object. ```{r} print(cfit) ``` ## More details on relaxed fitting Although `glmnet` has a `relax` option, you can created a relaxed version by post-processing a `glmnet` object. ```{r `relaxed`} fit=glmnet(x,y) fitr=relax.glmnet(fit,x=x,y=y) ``` This will rarely need to be done; one use case is if the original fit took a long time, and the user wanted to avoid refitting it. Note that in the call the arguments are named, since they are passed in via the `...` argument to `relax.glmnet`. Needless to say, *any* of the families fit by `glmnet` can also be fit with the `relaxed` option. As mentioned, a `relaxed` object is also a `glmnet` object. Apart from the class modification, it has an additional componet named `relaxed` which is itself a `glmnet` object, but with the relaxed coefficients. The default behavior of extractor functions like `predict` and `coef`, as well as `plot` will be to present results from the `glmnet` fit, unless a value of `gamma` is given different from the default value `gamma=1` (see the plots above). The `print` method gives additional info on the relaxed fit. Likewise, a `cv.relaxed` object inherits from class `cv.glmnet`. Here the `predict` method by default uses the optimal relaxed fit; if predictions from the CV-optimal *original* `glmnet` fit are desired, one can directly use `predict.cv.glmnet`. Similarly for the `print` command, which we illustrate here. ```{r} print(cfit) print.cv.glmnet(cfit) ``` ## Relaxed fits and glms `glmnet` itself is used to fit the relaxed fits, by using a single value of zero for `lambda`. However, for nonlinear models such as binomial, multinomial and poisson, there can be convergence issues. This is because `glmnet` does not do stepsize optimization, rather relying on the pathwise fit to stay in the "quadratic" zone of the log likelihood. We have an optional `path=TRUE` option for `relax.glmnet`, which actually fits a regurized path toward the `lambda=0` solution, and thus avoids the issue. The default is `path=FALSE` since this option adds to the computing time. ### Forward stepwise and relaxed fit One use case for a relaxed fit is as a faster version of forward stepwise regression. With a large number `p` of variables, forward-stepwise regression can be tedious. Lasso on the other hand, because of its convexity, can plunge in and identify good candidate sets of variables over 100 values of `lambda`, even though `p` could be in the 10s of thousands. In a case like this, one can have `cv.glmnet` do the selection. ```{r} fitr=cv.glmnet(x,y,gamma=0,relax=TRUE) plot(fitr) ``` Notice that we only allow `gamma=0`, so in this case we are not considering the blended fits. ## Progress bar We finally have a progress bar for `glmnet` and `cv.glmnet`. Ever run a job on a big dataset, and wonder how long it will take? Now you can use the `trace.it = TRUE` argument to these functions. ```{r, eval=FALSE} fit=glmnet(x,y,trace=TRUE) ``` ``##`` `` |================================== |65%`` Here we abbreviated the argument to `trace`. This display changes in place as the fit is produced. Also very helpful with `cv.glmnet` ```{r, eval=FALSE} fit=cv.glmnet(x,y,trace=TRUE) ``` ``##`` `` Training`` `` |=============================================| 100%`` `` Fold: 1/10`` `` |=============================================| 100%`` `` Fold: 2/10`` `` |=============================================| 100%`` `` Fold: 3/10`` `` |=============================================| 100%`` `` Fold: 4/10`` `` |=============================================| 100%`` `` Fold: 5/10`` `` |=============================================| 100%`` `` Fold: 6/10`` `` |============================= | 70%`` Tracing of the folds works a little differently when distributed computing is used. Here the `trace` argument should be used in each call to `glmnet` or `cv.glmnet`. One can set this option session wide via a call to `glmnet.control` with its new `itrace` argument: ```{r, eval=FALSE} glmnet.control(itrace=1) ``` To reset it, one makes a similar call and sets `itrace=0`. ## C index for Cox models We have a new performance measure for the Cox model: the Harrel *C index*. This is like the AUC measure of concordance for survival data, but only considers comparable pairs. Pure concordance would record the fraction of pairs for which the order of the death times agree with the order of the predicted risk. But with survival data, if an observation is right censored at a time *before* another observation's death time, they are not comparable. ```{r} data(CoxExample) ``` ```{r} cvfit=cv.glmnet(x,y,family="cox",type.measure="C") plot(cvfit) ``` ## Assessing models on test data Once we have fit a series of models using `glmnet`, we often assess their performance on a set of evaluation or test data. We usually go through the process of building a prediction matrix, and then deciding on the measure, and computing the values for a series of values for `lambda` and now `gamma`. Here we provide three functions for making these tasks easier. ### Performance measures The function `assess.glmnet` computes the same performance measures produced by `cv.glmnet`, but on a validation or test dataset. ```{r} data(BinomialExample) itrain=1:70 fit=glmnet(x[itrain,],y[itrain],family="binomial",nlambda=20) assess.glmnet(fit,newx=x[-itrain,],newy=y[-itrain]) ``` This produces a list with *all* the measures suitable for a binomial model, computed for the entire sequence of lambdas in the fit object. Here the function identifies the model family from the fit object. A second use case builds the prediction matrix first ```{r, eval=FALSE} pred=predict(fit,newx=x[-itrain,]) assess.glmnet(pred,newy=y[-itrain],family="binomial") ``` Here we have to provide the `family` as an argument; the results (not shown) are the same. Users can see the various measures suitable for each family via ```{r} glmnet.measures() ``` The assess function can also take the result of `cv.glmnet` as input. In this case the predictions are made at the optimal values for the parameter(s). ```{r} cfit=cv.glmnet(x[itrain,],y[itrain],family="binomial", nlambda = 30) assess.glmnet(cfit,newx=x[-itrain,],newy=y[-itrain]) ``` This used the default value of `s=lambda.1se`, just like `predict` would have done. Users can provide additional arguments that get passed on to predict: ```{r} assess.glmnet(cfit,newx=x[-itrain,],newy=y[-itrain], s="lambda.min") ``` One interesting use case is to get the results of CV using other measures, via the `keep` argument. In this case the `fit.preval` object is a matrix of prevalidated predictions made using the folds `foldid` ```{r} cfit=cv.glmnet(x,y,family="binomial",keep=TRUE, nlambda = 30) assess.glmnet(cfit$fit.preval,newy=y,family="binomial") ``` Users can verify that the first measure here `deviance` is identical to the component `cvm` on the `cfit` object. ### ROC curves for binomial data In the special case of binomial models, users often would like to see the ROC curve for validation or test data. Here the function `roc.glmnet` provides the goodies. Its first argument is as in `assess.glmnet`. Here we illustrate one use case, using the prevlidated CV fit as before. ```{r} cfit=cv.glmnet(x,y,family="binomial", type.measure="auc", keep=TRUE) rocs=roc.glmnet(cfit$fit.preval,newy=y) which=match(cfit$lambda.min,cfit$lambda) plot(rocs[[which]],type="l") nopr=sapply(rocs,lines,col="grey") lines(rocs[[which]],lwd=2,col="red") ``` In this case `roc.glmnet` returns a list of cross-validated ROC data, one for each model along the path. In the third line we identify the CV winner. Then we plot all the curves in grey, and the winner in red. ### Confusion matrices for classification For binomial and multinomial models, we often which to examine the classification performance on new data. The function `confusion.glmnet` will do that. ```{r} data(MultinomialExample) set.seed(101) itrain=sample(1:500,400,replace=FALSE) cfit=cv.glmnet(x[itrain,],y[itrain],family="multinomial") cnf=confusion.glmnet(cfit,newx=x[-itrain,],newy=y[-itrain]) print(cnf) ``` It produces a table of class `confusion.table` which inherits from calss `table`, and we also provide a print method. The first argument to `confusion.glmnet` should be either a `glmnet` object, or a `cv.glmnet` object, from which predictions can be made, or a matrix/array of predictions, such as the *kept* `fit.predval` object from `cv.glmnet`. In the second case we need to specify the `family`, otherwise *confusion* can exist between `binomial` and `multinomial` prediction matrices. Here we show a multinomial example ```{r} cfit=cv.glmnet(x,y,family="multinomial",type="class",keep=TRUE) cnf=confusion.glmnet(cfit$fit.preval,newy=y,family="multinomial") which=match(cfit$lambda.min,cfit$lambda) print(cnf[[which]]) ``` Since the `fit.preval` object has predictions for the whole path, the result of `confusion.glmnet` here is a list of confusion tables. We identify and print the one corresponding to the minimum classification error. ## Fitting big and/or sparse GLMs We include a function `bigGlm` for fitting a single GLM model (unpenalized), but allowing all the options of `glmnet`. In other words, coefficient upper and/or lower bounds and sparse `x` matrices. This is not too much more than fitting a model with a single value of `lambda=0` (with some protection from edge cases). There is also a `predict` and `print` method. ```{r} data(BinomialExample) fit=bigGlm(x,y,family="binomial",lower.limits=-1) print(fit) ``` ## Producing x from mixed variables, and missing data We have created a function `makeX` that makes it easy to create the model matrix `x` needed as input to `glmnet`. It takes as input a data frame, which can contain vectors, matrices and factors. Some of the features are * Factors are *one-hot* encoded to form indicator matrices * Missing values in the resultant matrix can be replaced by the column means * The `sparse` option returns a matrix in column-sparse format. This is useful if the data are large, and factors have many levels. * Two dataframes can be provided, `train` and `test`. This ensures the factor levels correspond, and also imputes missing data in the test data from means in the training data. We start with a simple case with some factors. ```{r} set.seed(101) X = matrix(rnorm(20),10,2) X3=sample(letters[1:3],10,replace=TRUE) X4=sample(LETTERS[1:3],10,replace=TRUE) df=data.frame(X,X3,X4) makeX(df) ``` Or if a sparse output was desired: ```{r} makeX(df,sparse=TRUE) ``` And now some missing values ```{r} Xn=X Xn[3,1]=NA;Xn[5,2]=NA X3n=X3; X3n[6]=NA X4n=X4 X4n[9]=NA dfn=data.frame(Xn,X3n,X4n) makeX(dfn) ``` which we can replace with column-mean imputations (and make sparse, if we like) ```{r} makeX(dfn,na.impute=TRUE,sparse=TRUE) ``` Finally if a test set is available as well ```{r} X = matrix(rnorm(10),5,2) X3=sample(letters[1:3],5,replace=TRUE) X4=sample(LETTERS[1:3],5,replace=TRUE) Xn=X Xn[3,1]=NA;Xn[5,2]=NA X3n=X3; X3n[1]=NA X4n=X4 X4n[2]=NA dftn=data.frame(Xn,X3n,X4n) makeX(dfn,dftn,na.impute=TRUE, sparse=TRUE) ``` ## References glmnet/inst/doc/relax.pdf0000644000176200001440000035312613574046201015104 0ustar liggesusers%PDF-1.5 % 1 0 obj << /Type /ObjStm /Length 2930 /Filter /FlateDecode /N 66 /First 524 >> stream xZr۶}?_ӉFL'3'Mֵ&m'Dl%ёwm,̜) %L12l120˒,e Xa9KtfX"XbB~$KrFŤH1D3)%&U~I#3ւrdFsVbS .P]2PgŴMQ`Ir̂Hʬ=A?2%X*f*A bJ+jU6&RI-J,V,XPf藳L @fŲ8dCP<,O0XC1rtҠr:Gy`E$,7)z&X-Ks< xAfKEk:9%Al:7)q1ۋSTaP,aIVOÔ1'a@l)}@byOV&+IMbLrLD * g%Ec:G1XᾬG Ge4Y5e RB<P %)VYr}Dn&Ѡ#8~#kF>ۏvH1.ݖ7WO;;٫ňaV[;;+FB]|PPs}E DbӾEZOiU[z_",I#wu{ N)}o0la\jmA8Jʷ`*= 9OSC5Ņ=q^;JpՔܿ-;rT7IU7~W7;`P./y*~+>5#>~1 ['nf<ykCeHb'S1(Jȯ-t^ǐ)#T .ߝf/IoLaݒEE0jdt9sR7]Z^ W4OГJ%XOEDq8 rf:*grq xA"BmOHR ;}GuSB;=W9ahǔ.o_f2 u_;z {T'sPk+wv3B["5!uFɠ/z駛.ciRIe3tJwi]9>:=%='져;xUw}2 zMٽmŏp5E9 nFkJɺ|NE}^W;s:~aڳ/$lyEbMZԨ5NdCmSie:'c{UHeVx6E zK^{ hSXd#*/dgGEdUb튣M*1[%}Qҥ,GOm ui !LSzQ'byz j 3p˷|.Sp; ȥT!8> \mZй"Iӎj|oQ;ݻ-eOSSyzr)՗W:NpeTCr_WS.‡eHם-EN͉Nr9YGe9KQ%xLAn~{C_0-G㗹a*] wrgۦ >g*$^n=uwu M(tF1Wf(m7>FFsC{Rz%M>$d sM}4qrŽ@Ĵa>GDZ6:v1⽞t_7 ,zehοVY_s3@endstream endobj 68 0 obj << /Subtype /XML /Type /Metadata /Length 1663 >> stream GPL Ghostscript 9.23 2019-12-10T17:55:12-08:00 2019-12-10T17:55:12-08:00 LaTeX with hyperref package Relaxed fits and other additions in glmnet 3.0Trevor Hastie, Balasubramanian Narasimhan and Rob Tibshirani endstream endobj 69 0 obj << /Type /ObjStm /Length 2688 /Filter /FlateDecode /N 65 /First 554 >> stream xZr7}߯cR[~rJ"eN(ǹhj$1+*r=ÙHt9 pAwC/`^2񥘎y͜4g޲p昔V142h# P 0 S:[30-S1DGԚϴB'!0me-DEtp #D`g&ÌEˌ1=3^G3ȬаOfZ!@1k"]:aNHXJsVPcϜ8.RYsХp9DO]̠a-6+%|+N J?ht&1LrE+:0+@WгB>]Aρ,@&ъ>BIQԻV 7TIf=cq9_?XfߡN#5ĊwDZ8s٬|ua.ٳBps!+±WqK|Mu߹YoϛHc ;TmF*z@t+X &S>ڻY潘\*ER=WPIdmtQy2KelzjA2rSmdܞǍ+%Lᢚ:<߯V~BmzK(e @g\9}ȐX`F,9tkL:qaͮcG@ilNLžߜ-)N)7-؋"7O`@TZw+g-͆LEmPQEA)ЎIkv 3% pzRC G3~NRMx[m;Z-5ɳOtfcrkq{ź6 mpÌdA& úiy>Ma1Qm)lTb(STJc @8/˖C;M_LʕKʠu%9¬0'UB Ji@`K}"ږZdteIXt3 ikb6ŔZ^!2iǕ+δm@eG8@*h[*}Jk~JD:D":TQU*9m.2L{e GyD@Xp U鼆dP@XdAy7~{: ' ˻ 2eHTB;2/pLl|r&5 L5/$ylS7js/ny\hݺ%6ﺏ`Ӽ|P-/ wф(qqUbRLr6b^,լ,l}ROQ8^VwrtW;_ oʇ4z1'Xڀ|$ {Q|L/hL7TRZۍL㇏~zOT=Zf&xua%Jz?Uk*mEͿ]~6hN,%& ߁Zz%eA' nt|zKᨃj3С/Hՙ Q30ƙOyyӺR|=0]1PeiXuQxWG8M-_:'I3ukZ_&Fpߎ0hLXZmnTʮVʮB N޿1\\UcIћ2Ux]y -9odQp;pL̪._SW@t\.u\\ !]o>sB_ Q&ݳXP-e2(dG}M ȗO/NT-A".]'JE$įm0w.e|?и?vzo>O"Ppw趻 Rinh^jn$mۈD5Ԣ*{wp7ց**^WtR"6V>gJIdS|Oîc@kA| 3U'zTk =#؝>Wn_Zџ~xKwas ^/x:.}wXS1. pК>!D 7O<,jErA]; | np8gRsgQx>Γ`_'\JHx_ ac,Q֓S- '6[*9irtB Hs%Z~<ЊqQw(x\ (&;W+&VzaDIu~d@lyPz Iss>풛oAiA-c+#ݛR6!H[{ UO{,HLh(8Y-U8 YT"4v!^}?@~ J%J: &7ϟ]3 3o[x;IN&pޠP}dG¢LZ$z:  aaRr$UB(ja5k:tc[HG0O=3wGqsCὅ\g1`WA͈Zw(J~qU k zyFATZ(WkT-RET~6|&)O/f}L-%Գtݟ7Lơ_bj1i|^uWWcq;7P?V:*% _>EmD8˷zRxy ?~ K-N_"$:~M9)2,sR=A8Zpe!rbןjhhūÉp }w}5TUwwΘ`#䵧?R vnjXqq_2@4* GvRP9iuN霐Ƚe Fޯ6朹R9Q?h8|oZ*TZ` hUH*-*!uFti|^?ReFGiHRb.Έ۞t\#A STvsb(`wfm¤'-'Gs^X>orG ωôj%' hIے+8 <TIEHH41C2X؂P{^  3AYsg2lZ( 5+yq|[$XsϳxU ,R#R%2rm?{^pQT?^ :S̊= މ^y'q2+8 XHG_8^Ԋ0Kq]9rLoK`SNɪ]ʤM0 C3tPyL:0>šx/Nd;L=cbn:ߡ{P83FMĬ42aR,u,lLTYk>z5\-Va _nd*Re)mlM#me+hL)ؾsrˡW{}7ycզ %dmNoh#Yi0{Z&4mH&yFR[eb+2 Jk,jԦ%pvdnp$@<0.j d> -XdPO:Y>F&z*tHR+TyȬvyy;^8"i:aVߴj}-P|$} 2 H;7ImyCMl4Ssp!S q4 Ol&h2RcVDp=/P@yuStMYUuO+"QA/ړ{H]ARu۬YL {Qi䬊Ku٘E>7p[,p?TG^ɆˢDe#-ئZ4qz jm-H,ޘ$):Hq(o bnU1s!Ovg "a{$ܷl  nČa@&B }s` 4Np&hߤElY#!6lqG2#B%hq4"rJ`M^ mP6rP? 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The regularization path is computed for the lasso or elasticnet penalty at a grid of values for the regularization parameter lambda. The algorithm is extremely fast, and can exploit sparsity in the input matrix `x`. It fits linear, logistic and multinomial, poisson, and Cox regression models. A variety of predictions can be made from the fitted models. It can also fit multi-response linear regression. The authors of glmnet are Jerome Friedman, Trevor Hastie, Rob Tibshirani and Noah Simon, and the R package is maintained by Trevor Hastie. The matlab version of glmnet is maintained by Junyang Qian. This vignette describes the usage of glmnet in R. There is an additional vignette for the new `relaxed` features in `glmnet`, along with some new capabilities. There is as well a vignette devoted to Cox models in `glmnet`. `glmnet` solves the following problem $$ \min_{\beta_0,\beta} \frac{1}{N} \sum_{i=1}^{N} w_i l(y_i,\beta_0+\beta^T x_i) + \lambda\left[(1-\alpha)||\beta||_2^2/2 + \alpha ||\beta||_1\right], $$ over a grid of values of $\lambda$ covering the entire range. Here $l(y,\eta)$ is the negative log-likelihood contribution for observation $i$; e.g. for the Gaussian case it is $\frac{1}{2}(y-\eta)^2$. The _elastic-net_ penalty is controlled by $\alpha$, and bridges the gap between lasso ($\alpha=1$, the default) and ridge ($\alpha=0$). The tuning parameter $\lambda$ controls the overall strength of the penalty. It is known that the ridge penalty shrinks the coefficients of correlated predictors towards each other while the lasso tends to pick one of them and discard the others. The elastic-net penalty mixes these two; if predictors are correlated in groups, an $\alpha=0.5$ tends to select the groups in or out together. This is a higher level parameter, and users might pick a value upfront, else experiment with a few different values. One use of $\alpha$ is for numerical stability; for example, the elastic net with $\alpha = 1 - \epsilon$ for some small $\epsilon > 0$ performs much like the lasso, but removes any degeneracies and wild behavior caused by extreme correlations. The `glmnet` algorithms use cyclical coordinate descent, which successively optimizes the objective function over each parameter with others fixed, and cycles repeatedly until convergence. The package also makes use of the strong rules for efficient restriction of the active set. Due to highly efficient updates and techniques such as warm starts and active-set convergence, our algorithms can compute the solution path very fast. The code can handle sparse input-matrix formats, as well as range constraints on coefficients. The core of `glmnet` is a set of fortran subroutines, which make for very fast execution. The package also includes methods for prediction and plotting, and a function that performs K-fold cross-validation. The theory and algorithms in this implementation are described in @glmnet, @coxnet, @strongrules and @block . ## Installation Like many other R packages, the simplest way to obtain `glmnet` is to install it directly from CRAN. Type the following command in R console: ```{r, eval=FALSE} install.packages("glmnet", repos = "https://cran.us.r-project.org") ``` Users may change the `repos` options depending on their locations and preferences. Other options such as the directories where to install the packages can be altered in the command. For more details, see `help(install.packages)`. Here the R package has been downloaded and installed to the default directories. Alternatively, users can download the package source from [CRAN](https://cran.r-project.org/package=glmnet) and type Unix commands to install it to the desired location. ## Quick Start The purpose of this section is to give users a general sense of the package, including the components, what they do and some basic usage. We will briefly go over the main functions, see the basic operations and have a look at the outputs. Users may have a better idea after this section what functions are available, which one to choose, or at least where to seek help. More details are given in later sections. First, we load the `glmnet` package: ```{r} library(glmnet) ``` The default model used in the package is the Guassian linear model or "least squares", which we will demonstrate in this section. We load a set of data created beforehand for illustration. Users can either load their own data or use those saved in the workspace. ```{r} data(QuickStartExample) ``` The command loads an input matrix `x` and a response vector `y` from this saved R data archive. We fit the model using the most basic call to `glmnet`. ```{r} fit = glmnet(x, y) ``` "fit" is an object of class `glmnet` that contains all the relevant information of the fitted model for further use. We do not encourage users to extract the components directly. Instead, various methods are provided for the object such as `plot`, `print`, `coef` and `predict` that enable us to execute those tasks more elegantly. We can visualize the coefficients by executing the `plot` function: ```{r} plot(fit) ``` Each curve corresponds to a variable. It shows the path of its coefficient against the $\ell_1$-norm of the whole coefficient vector at as $\lambda$ varies. The axis above indicates the number of nonzero coefficients at the current $\lambda$, which is the effective degrees of freedom (_df_) for the lasso. Users may also wish to annotate the curves; this can be done by setting `label = TRUE` in the plot command. A summary of the `glmnet` path at each step is displayed if we just enter the object name or use the `print` function: ```{r height = 4} print(fit) ``` It shows from left to right the number of nonzero coefficients (`Df`), the percent (of null) deviance explained (`%dev`) and the value of $\lambda$ (`Lambda`). Although by default `glmnet` calls for 100 values of `lambda` the program stops early if `%dev% does not change sufficently from one lambda to the next (typically near the end of the path.) We can obtain the actual coefficients at one or more $\lambda$'s within the range of the sequence: ```{r} coef(fit,s=0.1) ``` (why `s` and not `lambda`? In case later we want to allow one to specify the model size in other ways.) Users can also make predictions at specific $\lambda$'s with new input data: ```{r} set.seed(29) nx = matrix(rnorm(10*20),10,20) predict(fit,newx=nx,s=c(0.1,0.05)) ``` The function `glmnet` returns a sequence of models for the users to choose from. In many cases, users may prefer the software to select one of them. Cross-validation is perhaps the simplest and most widely used method for that task. `cv.glmnet` is the main function to do cross-validation here, along with various supporting methods such as plotting and prediction. We still act on the sample data loaded before. ```{r} cvfit = cv.glmnet(x, y) ``` `cv.glmnet` returns a `cv.glmnet` object, which is "cvfit" here, a list with all the ingredients of the cross-validation fit. As for `glmnet`, we do not encourage users to extract the components directly except for viewing the selected values of $\lambda$. The package provides well-designed functions for potential tasks. We can plot the object. ```{r} plot(cvfit) ``` It includes the cross-validation curve (red dotted line), and upper and lower standard deviation curves along the $\lambda$ sequence (error bars). Two selected $\lambda$'s are indicated by the vertical dotted lines (see below). We can view the selected $\lambda$'s and the corresponding coefficients. For example, ```{r} cvfit$lambda.min ``` `lambda.min` is the value of $\lambda$ that gives minimum mean cross-validated error. The other $\lambda$ saved is `lambda.1se`, which gives the most regularized model such that error is within one standard error of the minimum. To use that, we only need to replace `lambda.min` with `lambda.1se` above. ```{r} coef(cvfit, s = "lambda.min") ``` Note that the coefficients are represented in the sparse matrix format. The reason is that the solutions along the regularization path are often sparse, and hence it is more efficient in time and space to use a sparse format. If you prefer non-sparse format, pipe the output through `as.matrix()`. Predictions can be made based on the fitted `cv.glmnet` object. Let's see a toy example. ```{r} predict(cvfit, newx = x[1:5,], s = "lambda.min") ``` `newx` is for the new input matrix and `s`, as before, is the value(s) of $\lambda$ at which predictions are made. That is the end of `glmnet` 101. With the tools introduced so far, users are able to fit the entire elastic net family, including ridge regression, using squared-error loss. In the package, there are many more options that give users a great deal of flexibility. To learn more, move on to later sections. ## Linear Regression Linear regression here refers to two families of models. One is `gaussian`, the Gaussian family, and the other is `mgaussian`, the multiresponse Gaussian family. We first discuss the ordinary Gaussian and the multiresponse one after that. ### Gaussian Family `gaussian ` is the default family option in the function `glmnet`. Suppose we have observations $x_i \in \mathbb{R}^p$ and the responses $y_i \in \mathbb{R}, i = 1, \ldots, N$. The objective function for the Gaussian family is $$ \min_{(\beta_0, \beta) \in \mathbb{R}^{p+1}}\frac{1}{2N} \sum_{i=1}^N (y_i -\beta_0-x_i^T \beta)^2+\lambda \left[ (1-\alpha)||\beta||_2^2/2 + \alpha||\beta||_1\right], $$ where $\lambda \geq 0$ is a complexity parameter and $0 \leq \alpha \leq 1$ is a compromise between ridge ($\alpha = 0$) and lasso ($\alpha = 1$). Coordinate descent is applied to solve the problem. Specifically, suppose we have current estimates $\tilde{\beta_0}$ and $\tilde{\beta}_\ell$ $\forall j\in 1,]\ldots,p$. By computing the gradient at $\beta_j = \tilde{\beta}_j$ and simple calculus, the update is $$ \tilde{\beta}_j \leftarrow \frac{S(\frac{1}{N}\sum_{i=1}^N x_{ij}(y_i-\tilde{y}_i^{(j)}),\lambda \alpha)}{1+\lambda(1-\alpha)}, $$ where $\tilde{y}_i^{(j)} = \tilde{\beta}_0 + \sum_{\ell \neq j} x_{i\ell} \tilde{\beta}_\ell$, and $S(z, \gamma)$ is the soft-thresholding operator with value $\text{sign}(z)(|z|-\gamma)_+$. This formula above applies when the `x` variables are standardized to have unit variance (the default); it is slightly more complicated when they are not. Note that for "family=gaussian", `glmnet` standardizes $y$ to have unit variance before computing its lambda sequence (and then unstandardizes the resulting coefficients); if you wish to reproduce/compare results with other software, best to supply a standardized $y$ first (Using the "1/N" variance formula). `glmnet` provides various options for users to customize the fit. We introduce some commonly used options here and they can be specified in the `glmnet` function. * `alpha` is for the elastic-net mixing parameter $\alpha$, with range $\alpha \in [0,1]$. $\alpha = 1$ is the lasso (default) and $\alpha = 0$ is the ridge. * `weights` is for the observation weights. Default is 1 for each observation. (Note: `glmnet` rescales the weights to sum to N, the sample size.) * `nlambda` is the number of $\lambda$ values in the sequence. Default is 100. * `lambda` can be provided, but is typically not and the program constructs a sequence. When automatically generated, the $\lambda$ sequence is determined by `lambda.max` and `lambda.min.ratio`. The latter is the ratio of smallest value of the generated $\lambda$ sequence (say `lambda.min`) to `lambda.max`. The program then generated `nlambda` values linear on the log scale from `lambda.max` down to `lambda.min`. `lambda.max` is not given, but easily computed from the input $x$ and $y$; it is the smallest value for `lambda` such that all the coefficients are zero. For `alpha=0` (ridge) `lambda.max` would be $\infty$; hence for this case we pick a value corresponding to a small value for `alpha` close to zero.) * `standardize` is a logical flag for `x` variable standardization, prior to fitting the model sequence. The coefficients are always returned on the original scale. Default is `standardize=TRUE`. For more information, type `help(glmnet)` or simply `?glmnet`. As an example, we set $\alpha = 0.2$ (more like a ridge regression), and give double weights to the latter half of the observations. To avoid too long a display here, we set `nlambda` to 20. In practice, however, the number of values of $\lambda$ is recommended to be 100 (default) or more. In most cases, it does not come with extra cost because of the warm-starts used in the algorithm, and for nonlinear models leads to better convergence properties. ```{r} fit = glmnet(x, y, alpha = 0.2, weights = c(rep(1,50),rep(2,50)), nlambda = 20) ``` We can then print the `glmnet` object. ```{r} print(fit) ``` This displays the call that produced the object `fit` and a three-column matrix with columns `Df` (the number of nonzero coefficients), `%dev` (the percent deviance explained) and `Lambda` (the corresponding value of $\lambda$). (Note that the `digits` option can used to specify significant digits in the printout.) Here the actual number of $\lambda$'s here is less than specified in the call. The reason lies in the stopping criteria of the algorithm. According to the default internal settings, the computations stop if either the fractional change in deviance down the path is less than $10^{-5}$ or the fraction of explained deviance reaches $0.999$. From the last few lines , we see the fraction of deviance does not change much and therefore the computation ends when meeting the stopping criteria. We can change such internal parameters. For details, see the Appendix section or type `help(glmnet.control)`. We can plot the fitted object as in the previous section. There are more options in the `plot` function. Users can decide what is on the X-axis. `xvar` allows three measures: "norm" for the $\ell_1$-norm of the coefficients (default), "lambda" for the log-lambda value and "dev" for %deviance explained. Users can also label the curves with variable sequence numbers simply by setting `label = TRUE`. Let's plot "fit" against the log-lambda value and with each curve labeled. ```{r} plot(fit, xvar = "lambda", label = TRUE) ``` Now when we plot against %deviance we get a very different picture. This is percent deviance explained on the training data. What we see here is that toward the end of the path this value are not changing much, but the coefficients are "blowing up" a bit. This lets us focus attention on the parts of the fit that matter. This will especially be true for other models, such as logistic regression. ```{r} plot(fit, xvar = "dev", label = TRUE) ``` We can extract the coefficients and make predictions at certain values of $\lambda$. Two commonly used options are: * `s` specifies the value(s) of $\lambda$ at which extraction is made. * `exact` indicates whether the exact values of coefficients are desired or not. That is, if `exact = TRUE`, and predictions are to be made at values of `s` not included in the original fit, these values of `s` are merged with `object$lambda`, and the model is refit before predictions are made. If `exact=FALSE` (default), then the predict function uses linear interpolation to make predictions for values of `s` that do not coincide with lambdas used in the fitting algorithm. A simple example is: ```{r} fit = glmnet(x, y) any(fit$lambda == 0.5) coef.apprx = coef(fit, s = 0.5, exact = FALSE) coef.exact = coef(fit, s = 0.5, exact = TRUE, x=x, y=y) cbind2(coef.exact, coef.apprx) ``` The left column is for `exact = TRUE` and the right for `FALSE`. We see from the above that 0.5 is not in the sequence and that hence there are some difference, though not much. Linear interpolation is mostly enough if there are no special requirements. Notice that with `exact=TRUE` we have to supply by named argument any data that was used in creating the original fit, in this case `x` and `y`. Users can make predictions from the fitted object. In addition to the options in `coef`, the primary argument is `newx`, a matrix of new values for `x`. The `type` option allows users to choose the type of prediction: * "link" gives the fitted values * "response" the sames as "link" for "gaussian" family. * "coefficients" computes the coefficients at values of `s` * "nonzero" retuns a list of the indices of the nonzero coefficients for each value of `s`. For example, ```{r} predict(fit, newx = x[1:5,], type = "response", s = 0.05) ``` gives the fitted values for the first 5 observations at $\lambda = 0.05$. If multiple values of `s` are supplied, a matrix of predictions is produced. Users can customize K-fold cross-validation. In addition to all the `glmnet` parameters, `cv.glmnet` has its special parameters including `nfolds` (the number of folds), `foldid` (user-supplied folds), `type.measure`(the loss used for cross-validation): * "deviance" or "mse" uses squared loss * "mae" uses mean absolute error As an example, ```{r} cvfit = cv.glmnet(x, y, type.measure = "mse", nfolds = 20) ``` does 20-fold cross-validation, based on mean squared error criterion (default though). Parallel computing is also supported by `cv.glmnet`. To make it work, users must register parallel beforehand. We give a simple example of comparison here. Unfortunately, the package `doMC` is not available on Windows platforms (it is on others), so we cannot run the code here, but we make it looks as if we have. ```{r, eval=FALSE} require(doMC) registerDoMC(cores=2) X = matrix(rnorm(1e4 * 200), 1e4, 200) Y = rnorm(1e4) ``` ```{r, eval=FALSE} system.time(cv.glmnet(X, Y)) ``` ```{r, echo=FALSE} structure(c(2.44, 0.08, 2.518, 0, 0), class = "proc_time", .Names = c("user.self", "sys.self", "elapsed", "user.child", "sys.child")) ``` ```{r, eval=FALSE} system.time(cv.glmnet(X, Y, parallel = TRUE)) ``` ```{r, echo=FALSE} structure(c(0.508999999999999, 0.057, 1.56699999999999, 1.941, 0.1), class = "proc_time", .Names = c("user.self", "sys.self", "elapsed", "user.child", "sys.child")) ``` As suggested from the above, parallel computing can significantly speed up the computation process especially for large-scale problems. Functions `coef` and `predict` on cv.glmnet object are similar to those for a `glmnet` object, except that two special strings are also supported by `s` (the values of $\lambda$ requested): * "lambda.1se": the largest $\lambda$ at which the MSE is within one standard error of the minimal MSE. * "lambda.min": the $\lambda$ at which the minimal MSE is achieved. ```{r} cvfit$lambda.min coef(cvfit, s = "lambda.min") predict(cvfit, newx = x[1:5,], s = "lambda.min") ``` Users can control the folds used. Here we use the same folds so we can also select a value for $\alpha$. ```{r} foldid=sample(1:10,size=length(y),replace=TRUE) cv1=cv.glmnet(x,y,foldid=foldid,alpha=1) cv.5=cv.glmnet(x,y,foldid=foldid,alpha=.5) cv0=cv.glmnet(x,y,foldid=foldid,alpha=0) ``` There are no built-in plot functions to put them all on the same plot, so we are on our own here: ```{r} par(mfrow=c(2,2)) plot(cv1);plot(cv.5);plot(cv0) plot(log(cv1$lambda),cv1$cvm,pch=19,col="red",xlab="log(Lambda)",ylab=cv1$name) points(log(cv.5$lambda),cv.5$cvm,pch=19,col="grey") points(log(cv0$lambda),cv0$cvm,pch=19,col="blue") legend("topleft",legend=c("alpha= 1","alpha= .5","alpha 0"),pch=19,col=c("red","grey","blue")) ``` We see that lasso (`alpha=1`) does about the best here. We also see that the range of lambdas used differs with alpha. #### Coefficient upper and lower bounds These are recently added features that enhance the scope of the models. Suppose we want to fit our model, but limit the coefficients to be bigger than -0.7 and less than 0.5. This is easily achieved via the `upper.limits` and `lower.limits` arguments: ```{r} tfit=glmnet(x,y,lower=-.7,upper=.5) plot(tfit) ``` These are rather arbitrary limits; often we want the coefficients to be positive, so we can set only `lower.limit` to be 0. (Note, the lower limit must be no bigger than zero, and the upper limit no smaller than zero.) These bounds can be a vector, with different values for each coefficient. If given as a scalar, the same number gets recycled for all. #### Penalty factors This argument allows users to apply separate penalty factors to each coefficient. Its default is 1 for each parameter, but other values can be specified. In particular, any variable with `penalty.factor` equal to zero is not penalized at all! Let $v_j$ denote the penalty factor for $j$ th variable. The penalty term becomes $$ \lambda \sum_{j=1}^p \boldsymbol{v_j} P_\alpha(\beta_j) = \lambda \sum_{j=1}^p \boldsymbol{v_j} \left[ (1-\alpha)\frac{1}{2} \beta_j^2 + \alpha |\beta_j| \right]. $$ Note the penalty factors are internally rescaled to sum to nvars. This is very useful when people have prior knowledge or preference over the variables. In many cases, some variables may be so important that one wants to keep them all the time, which can be achieved by setting corresponding penalty factors to 0: ```{r} p.fac = rep(1, 20) p.fac[c(5, 10, 15)] = 0 pfit = glmnet(x, y, penalty.factor = p.fac) plot(pfit, label = TRUE) ``` We see from the labels that the three variables with 0 penalty factors always stay in the model, while the others follow typical regularization paths and shrunken to 0 eventually. Some other useful arguments. `exclude` allows one to block certain variables from being the model at all. Of course, one could simply subset these out of `x`, but sometimes `exclude` is more useful, since it returns a full vector of coefficients, just with the excluded ones set to zero. There is also an `intercept` argument which defaults to `TRUE`; if `FALSE` the intercept is forced to be zero. #### Customizing plots Sometimes, especially when the number of variables is small, we want to add variable labels to a plot. Since `glmnet` is intended primarily for wide data, this is not supprted in `plot.glmnet`. However, it is easy to do, as the following little toy example shows. We first generate some data, with 10 variables, and for lack of imagination and ease we give them simple character names. We then fit a glmnet model, and make the standard plot. ```{r} set.seed(101) x=matrix(rnorm(1000),100,10) y=rnorm(100) vn=paste("var",1:10) fit=glmnet(x,y) plot(fit) ``` We wish to label the curves with the variable names. Here s a simple way to do this, using the `axis` command in R (and a little research into how to customize it). We need to have the positions of the coefficients at the end of the path, and we need to make some space using the `par` command, so that our labels will fit in. This requires knowing how long your labels are, but here they are all quite short. ```{r} par(mar=c(4.5,4.5,1,4)) plot(fit) vnat=coef(fit) vnat=vnat[-1,ncol(vnat)] # remove the intercept, and get the coefficients at the end of the path axis(4, at=vnat,line=-.5,label=vn,las=1,tick=FALSE, cex.axis=0.5) ``` We have done nothing here to avoid overwriting of labels, in the event that they are close together. This would be a bit more work, but perhaps best left alone, anyway. ### Multiresponse Gaussian Family The multiresponse Gaussian family is obtained using `family = "mgaussian"` option in `glmnet`. It is very similar to the single-response case above. This is useful when there are a number of (correlated) responses - the so-called "multi-task learning" problem. Here the sharing involves which variables are selected, since when a variable is selected, a coefficient is fit for each response. Most of the options are the same, so we focus here on the differences with the single response model. Obviously, as the name suggests, $y$ is not a vector, but a matrix of quantitative responses in this section. The coefficients at each value of lambda are also a matrix as a result. Here we solve the following problem: $$ \min_{(\beta_0, \beta) \in \mathbb{R}^{(p+1)\times K}}\frac{1}{2N} \sum_{i=1}^N ||y_i -\beta_0-\beta^T x_i||^2_F+\lambda \left[ (1-\alpha)||\beta||_F^2/2 + \alpha\sum_{j=1}^p||\beta_j||_2\right]. $$ Here $\beta_j$ is the jth row of the $p\times K$ coefficient matrix $\beta$, and we replace the absolute penalty on each single coefficient by a group-lasso penalty on each coefficient K-vector $\beta_j$ for a single predictor $x_j$. We use a set of data generated beforehand for illustration. ```{r} data(MultiGaussianExample) ``` We fit the data, with an object "mfit" returned. ```{r} mfit = glmnet(x, y, family = "mgaussian") ``` For multiresponse Gaussian, the options in `glmnet` are almost the same as the single-response case, such as `alpha`, `weights`, `nlambda`, `standardize`. A exception to be noticed is that `standardize.response` is only for `mgaussian` family. The default value is `FALSE`. If `standardize.response = TRUE`, it standardizes the response variables. To visualize the coefficients, we use the `plot` function. ```{r} plot(mfit, xvar = "lambda", label = TRUE, type.coef = "2norm") ``` Note that we set `type.coef = "2norm"`. Under this setting, a single curve is plotted per variable, with value equal to the $\ell_2$ norm. The default setting is `type.coef = "coef"`, where a coefficient plot is created for each response (multiple figures). `xvar` and `label` are two other options besides ordinary graphical parameters. They are the same as the single-response case. We can extract the coefficients at requested values of $\lambda$ by using the function `coef` and make predictions by `predict`. The usage is similar and we only provide an example of `predict` here. ```{r} predict(mfit, newx = x[1:5,], s = c(0.1, 0.01)) ``` The prediction result is saved in a three-dimensional array with the first two dimensions being the prediction matrix for each response variable and the third indicating the response variables. We can also do k-fold cross-validation. The options are almost the same as the ordinary Gaussian family and we do not expand here. ```{r} cvmfit = cv.glmnet(x, y, family = "mgaussian") ``` We plot the resulting `cv.glmnet` object "cvmfit". ```{r} plot(cvmfit) ``` To show explicitly the selected optimal values of $\lambda$, type ```{r} cvmfit$lambda.min cvmfit$lambda.1se ``` As before, the first one is the value at which the minimal mean squared error is achieved and the second is for the most regularized model whose mean squared error is within one standard error of the minimal. Prediction for `cv.glmnet` object works almost the same as for `glmnet` object. We omit the details here. ## Logistic Regression Logistic regression is another widely-used model when the response is categorical. If there are two possible outcomes, we use the binomial distribution, else we use the multinomial. ### Binomial Models For the binomial model, suppose the response variable takes value in $\mathcal{G}=\{1,2\}$. Denote $y_i = I(g_i=1)$. We model $$\mbox{Pr}(G=2|X=x)=\frac{e^{\beta_0+\beta^Tx}}{1+e^{\beta_0+\beta^Tx}},$$ which can be written in the following form $$\log\frac{\mbox{Pr}(G=2|X=x)}{\mbox{Pr}(G=1|X=x)}=\beta_0+\beta^Tx,$$ the so-called "logistic" or log-odds transformation. The objective function for the penalized logistic regression uses the negative binomial log-likelihood, and is $$ \min_{(\beta_0, \beta) \in \mathbb{R}^{p+1}} -\left[\frac{1}{N} \sum_{i=1}^N y_i \cdot (\beta_0 + x_i^T \beta) - \log (1+e^{(\beta_0+x_i^T \beta)})\right] + \lambda \big[ (1-\alpha)||\beta||_2^2/2 + \alpha||\beta||_1\big]. $$ Logistic regression is often plagued with degeneracies when $p > N$ and exhibits wild behavior even when $N$ is close to $p$; the elastic-net penalty alleviates these issues, and regularizes and selects variables as well. Our algorithm uses a quadratic approximation to the log-likelihood, and then coordinate descent on the resulting penalized weighted least-squares problem. These constitute an outer and inner loop. For illustration purpose, we load pre-generated input matrix `x` and the response vector `y` from the data file. ```{r} data(BinomialExample) ``` The input matrix $x$ is the same as other families. For binomial logistic regression, the response variable $y$ should be either a factor with two levels, or a two-column matrix of counts or proportions. Other optional arguments of `glmnet` for binomial regression are almost same as those for Gaussian family. Don't forget to set `family` option to "binomial". ```{r} fit = glmnet(x, y, family = "binomial") ``` Like before, we can print and plot the fitted object, extract the coefficients at specific $\lambda$'s and also make predictions. For plotting, the optional arguments such as `xvar` and `label` are similar to the Gaussian. We plot against the deviance explained and show the labels. ```{r} plot(fit, xvar = "dev", label = TRUE) ``` Prediction is a little different for logistic from Gaussian, mainly in the option `type`. "link" and "response" are never equivalent and "class" is only available for logistic regression. In summary, * "link" gives the linear predictors * "response" gives the fitted probabilities * "class" produces the class label corresponding to the maximum probability. * "coefficients" computes the coefficients at values of `s` * "nonzero" retuns a list of the indices of the nonzero coefficients for each value of `s`. For "binomial" models, results ("link", "response", "coefficients", "nonzero") are returned only for the class corresponding to the second level of the factor response. In the following example, we make prediction of the class labels at $\lambda = 0.05, 0.01$. ```{r} predict(fit, newx = x[1:5,], type = "class", s = c(0.05, 0.01)) ``` For logistic regression, `cv.glmnet` has similar arguments and usage as Gaussian. `nfolds`, `weights`, `lambda`, `parallel` are all available to users. There are some differences in `type.measure`: "deviance" and "mse" do not both mean squared loss and "class" is enabled. Hence, * "mse" uses squared loss. * "deviance" uses actual deviance. * "mae" uses mean absolute error. * "class" gives misclassification error. * "auc" (for two-class logistic regression ONLY) gives area under the ROC curve. For example, ```{r} cvfit = cv.glmnet(x, y, family = "binomial", type.measure = "class") ``` It uses misclassification error as the criterion for 10-fold cross-validation. We plot the object and show the optimal values of $\lambda$. ```{r} plot(cvfit) ``` ```{r} cvfit$lambda.min cvfit$lambda.1se ``` `coef` and `predict` are simliar to the Gaussian case and we omit the details. We review by some examples. ```{r} coef(cvfit, s = "lambda.min") ``` As mentioned previously, the results returned here are only for the second level of the factor response. ```{r} predict(cvfit, newx = x[1:10,], s = "lambda.min", type = "class") ``` Like other GLMs, glmnet allows for an "offset". This is a fixed vector of N numbers that is added into the linear predictor. For example, you may have fitted some other logistic regression using other variables (and data), and now you want to see if the present variables can add anything. So you use the predicted logit from the other model as an offset in. ### Multinomial Models For the multinomial model, suppose the response variable has $K$ levels ${\cal G}=\{1,2,\ldots,K\}$. Here we model $$\mbox{Pr}(G=k|X=x)=\frac{e^{\beta_{0k}+\beta_k^Tx}}{\sum_{\ell=1}^Ke^{\beta_{0\ell}+\beta_\ell^Tx}}.$$ Let ${Y}$ be the $N \times K$ indicator response matrix, with elements $y_{i\ell} = I(g_i=\ell)$. Then the elastic-net penalized negative log-likelihood function becomes $$ \ell(\{\beta_{0k},\beta_{k}\}_1^K) = -\left[\frac{1}{N} \sum_{i=1}^N \Big(\sum_{k=1}^Ky_{il} (\beta_{0k} + x_i^T \beta_k)- \log \big(\sum_{k=1}^K e^{\beta_{0k}+x_i^T \beta_k}\big)\Big)\right] +\lambda \left[ (1-\alpha)||\beta||_F^2/2 + \alpha\sum_{j=1}^p||\beta_j||_q\right]. $$ Here we really abuse notation! $\beta$ is a $p\times K$ matrix of coefficients. $\beta_k$ refers to the kth column (for outcome category k), and $\beta_j$ the jth row (vector of K coefficients for variable j). The last penalty term is $||\beta_j||_q$, we have two options for q: $q\in \{1,2\}$. When q=1, this is a lasso penalty on each of the parameters. When q=2, this is a grouped-lasso penalty on all the K coefficients for a particular variables, which makes them all be zero or nonzero together. The standard Newton algorithm can be tedious here. Instead, we use a so-called partial Newton algorithm by making a partial quadratic approximation to the log-likelihood, allowing only $(\beta_{0k}, \beta_k)$ to vary for a single class at a time. For each value of $\lambda$, we first cycle over all classes indexed by $k$, computing each time a partial quadratic approximation about the parameters of the current class. Then the inner procedure is almost the same as for the binomial case. This is the case for lasso (q=1). When q=2, we use a different approach, which we wont dwell on here. For the multinomial case, the usage is similar to logistic regression, and we mainly illustrate by examples and address any differences. We load a set of generated data. ```{r} data(MultinomialExample) ``` The optional arguments in `glmnet` for multinomial logistic regression are mostly similar to binomial regression except for a few cases. The response variable can be a `nc >= 2` level factor, or a `nc`-column matrix of counts or proportions. Internally glmnet will make the rows of this matrix sum to 1, and absorb the total mass into the weight for that observation. `offset` should be a `nobs x nc` matrix if there is one. A special option for multinomial regression is `type.multinomial`, which allows the usage of a grouped lasso penalty if `type.multinomial = "grouped"`. This will ensure that the multinomial coefficients for a variable are all in or out together, just like for the multi-response Gaussian. ```{r} fit = glmnet(x, y, family = "multinomial", type.multinomial = "grouped") ``` We plot the resulting object "fit". ```{r} plot(fit, xvar = "lambda", label = TRUE, type.coef = "2norm") ``` The options are `xvar`, `label` and `type.coef`, in addition to other ordinary graphical parameters. `xvar` and `label` are the same as other families while `type.coef` is only for multinomial regression and multiresponse Gaussian model. It can produce a figure of coefficients for each response variable if `type.coef = "coef"` or a figure showing the $\ell_2$-norm in one figure if `type.coef = "2norm"` We can also do cross-validation and plot the returned object. ```{r} cvfit=cv.glmnet(x, y, family="multinomial", type.multinomial = "grouped", parallel = TRUE) plot(cvfit) ``` Note that although `type.multinomial` is not a typical argument in `cv.glmnet`, in fact any argument that can be passed to `glmnet` is valid in the argument list of `cv.glmnet`. We also use parallel computing to accelerate the calculation. Users may wish to predict at the optimally selected $\lambda$: ```{r} predict(cvfit, newx = x[1:10,], s = "lambda.min", type = "class") ``` ## Poisson Models Poisson regression is used to model count data under the assumption of Poisson error, or otherwise non-negative data where the mean and variance are proportional. Like the Gaussian and binomial model, the Poisson is a member of the exponential family of distributions. We usually model its positive mean on the log scale: $\log \mu(x) = \beta_0+\beta' x$. The log-likelihood for observations $\{x_i,y_i\}_1^N$ is given my $$ l(\beta|X, Y) = \sum_{i=1}^N \left(y_i (\beta_0+\beta' x_i) - e^{\beta_0+\beta^Tx_i}\right). $$ As before, we optimize the penalized log-lielihood: $$ \min_{\beta_0,\beta} -\frac1N l(\beta|X, Y) + \lambda \left((1-\alpha) \sum_{i=1}^N \beta_i^2/2) +\alpha \sum_{i=1}^N |\beta_i|\right). $$ Glmnet uses an outer Newton loop, and an inner weighted least-squares loop (as in logistic regression) to optimize this criterion. First, we load a pre-generated set of Poisson data. ```{r} data(PoissonExample) ``` We apply the function `glmnet` with the `"poisson"` option. ```{r} fit = glmnet(x, y, family = "poisson") ``` The optional input arguments of `glmnet` for `"poisson"` family are similar to those for others. `offset` is a useful argument particularly in Poisson models. When dealing with rate data in Poisson models, the counts collected are often based on different exposures, such as length of time observed, area and years. A poisson rate $\mu(x)$ is relative to a unit exposure time, so if an observation $y_i$ was exposed for $E_i$ units of time, then the expected count would be $E_i\mu(x)$, and the log mean would be $\log(E_i)+\log(\mu(x))$. In a case like this, we would supply an *offset* $\log(E_i)$ for each observation. Hence `offset` is a vector of length `nobs` that is included in the linear predictor. Other families can also use options, typically for different reasons. (Warning: if `offset` is supplied in `glmnet`, offsets must also also be supplied to `predict` to make reasonable predictions.) Again, we plot the coefficients to have a first sense of the result. ```{r} plot(fit) ``` Like before, we can extract the coefficients and make predictions at certain $\lambda$'s by using `coef` and `predict` respectively. The optional input arguments are similar to those for other families. In function `predict`, the option `type`, which is the type of prediction required, has its own specialties for Poisson family. That is, * "link" (default) gives the linear predictors like others * "response" gives the fitted mean * "coefficients" computes the coefficients at the requested values for `s`, which can also be realized by `coef` function * "nonzero" returns a a list of the indices of the nonzero coefficients for each value of `s`. For example, we can do as follows. ```{r} coef(fit, s = 1) predict(fit, newx = x[1:5,], type = "response", s = c(0.1,1)) ``` We may also use cross-validation to find the optimal $\lambda$'s and thus make inferences. ```{r} cvfit = cv.glmnet(x, y, family = "poisson") ``` Options are almost the same as the Gaussian family except that for `type.measure`, * "deviance" (default) gives the deviance * "mse" stands for mean squared error * "mae" is for mean absolute error. We can plot the `cv.glmnet` object. ```{r} plot(cvfit) ``` We can also show the optimal $\lambda$'s and the corresponding coefficients. ```{r} opt.lam = c(cvfit$lambda.min, cvfit$lambda.1se) coef(cvfit, s = opt.lam) ``` The `predict` method is similar and we do not repeat it here. ## Cox Models The Cox proportional hazards model is commonly used for the study of the relationship beteween predictor variables and survival time. In the usual survival analysis framework, we have data of the form $(y_1, x_1, \delta_1), \ldots, (y_n, x_n, \delta_n)$ where $y_i$, the observed time, is a time of failure if $\delta_i$ is 1 or right-censoring if $\delta_i$ is 0. We also let $t_1 < t_2 < \ldots < t_m$ be the increasing list of unique failure times, and $j(i)$ denote the index of the observation failing at time $t_i$. The Cox model assumes a semi-parametric form for the hazard $$ h_i(t) = h_0(t) e^{x_i^T \beta}, $$ where $h_i(t)$ is the hazard for patient $i$ at time $t$, $h_0(t)$ is a shared baseline hazard, and $\beta$ is a fixed, length $p$ vector. In the classic setting $n \geq p$, inference is made via the partial likelihood $$ L(\beta) = \prod_{i=1}^m \frac{e^{x_{j(i)}^T \beta}}{\sum_{j \in R_i} e^{x_j^T \beta}}, $$ where $R_i$ is the set of indices $j$ with $y_j \geq t_i$ (those at risk at time $t_i$). Note there is no intercept in the Cox mode (its built into the baseline hazard, and like it, would cancel in the partial likelihood.) We penalize the negative log of the partial likelihood, just like the other models, with an elastic-net penalty. We use a pre-generated set of sample data and response. Users can load their own data and follow a similar procedure. In this case $x$ must be an $n\times p$ matrix of covariate values - each row corresponds to a patient and each column a covariate. $y$ is an $n \times 2$ matrix, with a column "time" of failure/censoring times, and "status" a 0/1 indicator, with 1 meaning the time is a failure time, and zero a censoring time. ```{r} data(CoxExample) y[1:5,] ``` The `Surv` function in the package `survival` can create such a matrix. Note, however, that the `coxph` and related linear models can handle interval and other forms of censoring, while glmnet can only handle right censoring in its present form. We apply the `glmnet` function to compute the solution path under default settings. ```{r} fit = glmnet(x, y, family = "cox") ``` All the standard options are available such as `alpha`, `weights`, `nlambda` and `standardize`. Their usage is similar as in the Gaussian case and we omit the details here. Users can also refer to the help file `help(glmnet)`. We can plot the coefficients. ```{r} plot(fit) ``` As before, we can extract the coefficients at certain values of $\lambda$. ```{r} coef(fit, s = 0.05) ``` Since the Cox Model is not commonly used for prediction, we do not give an illustrative example on prediction. If needed, users can refer to the help file by typing `help(predict.glmnet)`. Also, the function `cv.glmnet` can be used to compute $k$-fold cross-validation for the Cox model. The usage is similar to that for other families except for two main differences. One is that `type.measure` only supports "deviance"(also default), which gives the partial-likelihood. The other is in the option `grouped`. `grouped = TRUE` obtains the CV partial likelihood for the Kth fold by subtraction; by subtracting the log partial likelihood evaluated on the full dataset from that evaluated on the on the (K-1)/K dataset. This makes more efficient use of risk sets. With `grouped=FALSE` the log partial likelihood is computed only on the Kth fold, which is only reasonable if each fold has a large number of observations. ```{r} cvfit = cv.glmnet(x, y, family = "cox") ``` Once fit, we can view the optimal $\lambda$ value and a cross validated error plot to help evaluate our model. ```{r} plot(cvfit) ``` As previously, the left vertical line in our plot shows us where the CV-error curve hits its minimum. The right vertical line shows us the most regularized model with CV-error within 1 standard deviation of the minimum. We also extract such optimal $\lambda$'s. ```{r} cvfit$lambda.min cvfit$lambda.1se ``` We can check the active covariates in our model and see their coefficients. ```{r} coef.min = coef(cvfit, s = "lambda.min") active.min = which(coef.min != 0) index.min = coef.min[active.min] ``` ```{r} index.min coef.min ``` ## Sparse Matrices Our package supports sparse input matrices, which allow efficient storage and operations of large matrices but with only a few nonzero entries. It is available for all families except for the `cox` family. The usage of sparse matrices (inherit from class `"sparseMatrix"` as in package `Matrix`) in `glmnet ` is the same as if a regular matrix is provided. We load a set of sample data created beforehand. ```{r} data(SparseExample) ``` It loads `x`, a 100*20 sparse input matrix and `y`, the response vector. ```{r} class(x) ``` Users can create a sparse matrix with the function `sparseMatrix` by providing the locations and values of the nonzero entries. Alternatively, `Matrix` function can also be used to contruct a sparse matrix by setting `sparse = TRUE`, but this defeats the purpose somewhat. We can fit the model the same way as before. ```{r} fit = glmnet(x, y) ``` We also do the cross-validation and plot the resulting object. ```{r} cvfit = cv.glmnet(x, y) plot(cvfit) ``` The usage of other functions are similar and we do not expand here. Note that sparse matrices can also be used for `newx`, the new input matrix in the `predict` function. For example, ```{r} i = sample(1:5, size = 25, replace = TRUE) j = sample(1:20, size = 25, replace = TRUE) x = rnorm(25) nx = sparseMatrix(i = i, j = j, x = x, dims = c(5, 20)) predict(cvfit, newx = nx, s = "lambda.min") ``` ## Appendix 0: Convergence Criteria Glmnet uses a convergence criterion that focuses not on coefficient change but rather the impact of the change on the fitted values, and hence the loss part of the objective. The net result is a weighted norm of the coefficient change vector. For gaussian models it uses the following. Suppose observation $i$ has weight $w_i$. Let $v_j$ be the (weighted) sum-of-squares for variable $x_j$: $$v_j=\sum_{i=1}^Nw_ix_{ij}^2.$$ If there is an intercept in the model, these $x_j$ will be centered by the weighted mean, and hence this would be a weighted variance. After $\hat\beta_j^o$ has been updated to $\hat\beta_j^n$, we compute $\Delta_j=v_j(\hat\beta_j^o-\hat\beta_j^n)^2$. After a complete cycle of coordinate descent, we look at $\Delta_{max}=\max_j\Delta_j$. Why this measure? We can write $$\Delta_j=\frac1N\sum_{i=1}^N w_j(x_{ij}\hat\beta_j^o-x_{ij}\hat\beta_j^n)^2,$$ which measures the weighted sum of squares of changes in fitted values for this term. This measures the impact of the change in this coefficient on the fit. If the largest such change is negligible, we stop. For logistic regression, and other non-Gaussian models it is similar for the inner loop. Only now the weights for each observation are more complex. For example, for logisitic regression the weights are those that arise from the current Newton step, namely $w_i^*=w_i\hat p_i(1-\hat p_i)$. Here $\hat p_i$ are the fitted probabilities as we entered the current inner loop. The intuition is the same --- it measures the impact of the coefficient change on the current weighted least squares loss, or quadratic approximation to the log-likelihood loss. What about outer-loop convergence? We use the same measure, except now $\hat\beta^o$ is the coefficient vector before we entered this inner loop, and $\hat\beta^n$ the converged solution for this inner loop. Hence if this Newton step had no impact, we declare outer-loop convergence. ## Appendix 1: Internal Parameters Our package has a set of internal parameters which control some aspects of the computation of the path. The *factory default* settings are expected to serve in most cases, and users do not need to make changes unless there are special requirements. There are several parameters that users can change: `fdev` - minimum fractional change in deviance for stopping path; factory default = 1.0e-5 `devmax` - maximum fraction of explained deviance for stopping path; factory default = 0.999 * `eps` - minimum value of lambda.min.ratio (see glmnet); factory default= 1.0e-6 * `big` - large floating point number; factory default = 9.9e35. Inf in definition of upper.limit is set to big * `mnlam` - minimum number of path points (lambda values) allowed; factory default = 5 * `pmin` - minimum null probability for any class; factory default = 1.0e-5 * `exmx` - maximum allowed exponent; factory default = 250.0 * `prec` - convergence threshold for multi-response bounds adjustment solution; factory default = 1.0e-10 * `mxit` - maximum iterations for multiresponse bounds adjustment solution; factory default = 100 * `factory` - If `TRUE`, reset all the parameters to the factory default; default is `FALSE` We illustrate the usage by an example. Note that any changes made hold for the duration of the R session, or unless they are changed by the user with a subsequent call to `glmnet.control`. ```{r} data(QuickStartExample) fit = glmnet(x, y) print(fit) ``` We can change the minimum fractional change in deviance for stopping path and compare the results. ```{r} glmnet.control(fdev = 0) fit = glmnet(x, y) print(fit) ``` We set `fdev = 0` to continue all along the path, even without much change. The length of the sequence becomes 100, which is the default of `nlambda`. Users can also reset to the default settings. ```{r} glmnet.control(factory = TRUE) ``` The current settings are obtained as follows. ```{r} glmnet.control() ``` ## Appendix 2: Comparison with Other Packages Some people may want to use `glmnet` to solve the Lasso or elastic-net problem at a single $\lambda$. We compare here the solution by `glmnet` with other packages (such as CVX), and also as an illustration of parameter settings in this situation. __Warning__: Though such problems can be solved by `glmnet`, it is __not recommended__ and is not the spirit of the package. `glmnet` fits the __entire__ solution path for Lasso or elastic-net problems efficiently with various techniques such as warm start. Those advantages will disappear if the $\lambda$ sequence is forced to be only one value. Nevertheless, we still illustrate with a typical example in linear model in the following for the purpose of comparison. Given $X, Y$ and $\lambda_0 > 0$, we want to find $\beta$ such that $$ \min_{\beta} ||Y - X\beta||_2^2 + \lambda_0 ||\beta||_1, $$ where, say, $\lambda_0 = 8$. We first solve using `glmnet`. Notice that there is no intercept term in the objective function, and the columns of $X$ are not necessarily standardized. Corresponding parameters have to be set to make it work correctly. In addition, there is a $1/(2n)$ factor before the quadratic term by default, we need to adjust $\lambda$ accordingly. For the purpose of comparison, the `thresh` option is specified to be 1e-20. However, this is not necessary in many practical applications. ```{r, echo=FALSE} data(QuickStartExample) ``` ```{r,eval=FALSE} fit = glmnet(x, y, intercept = F, standardize = F, lambda = 8/(2*dim(x)[1]), thresh = 1e-20) ``` We then extract the coefficients (with no intercept). ```{r,eval=FALSE} beta_glmnet = as.matrix(predict(fit, type = "coefficients")[-1,]) ``` In linear model as here this approach worked because we were using squared error loss, but with any nonlinear family, it will probably fail. The reason is we are not using step length optimization, and so rely on very good warm starts to put us in the quadratic region of the loss function. Alternatively, a more stable and __strongly recommended__ way to perform this task is to first fit the entire Lasso or elastic-net path without specifying `lambda`, but then provide the requested $\lambda_0$ to `predict` function to extract the corresponding coefficients. In fact, if $\lambda_0$ is not in the $\lambda$ sequence generated by `glmnet`, the path will be refitted along a new $\lambda$ sequence that includes the requested value $\lambda_0$ and the old sequence, and the coefficients will be returned at $\lambda_0$ based on the new fit. Remember to set `exact = TRUE` in `predict` function to get the exact solution. Otherwise, it will be approximated by linear interpolation. ```{r} fit = glmnet(x, y, intercept = F, standardize = F, thresh = 1e-20) beta_glmnet = as.matrix(predict(fit, s = 8/(2*dim(x)[1]), type = "coefficients", exact = TRUE, x=x, y=y)[-1,]) ``` We also use CVX, a general convex optimization solver, to solve this specific Lasso problem. Users could also call CVX from R using the `CVXfromR` package and solve the problem as follows. ```{r, eval=FALSE} library(CVXfromR) setup.dir = "change/this/to/your/cvx/directory" n = dim(x)[1]; p = dim(x)[2] cvxcode = paste("variables beta(p)", "minimize(square_pos(norm(y - x * beta, 2)) + lambda * norm(beta, 1))", sep = ";") Lasso = CallCVX(cvxcode, const.var = list(p = p, x = x, y = y, lambda = 8), opt.var.names = "beta", setup.dir = setup.dir, matlab.call = "change/this/to/path/to/matlab") beta_CVX = Lasso$beta ``` For convenience here, the results were saved in `CVXResult.RData`, and we simply load in the results. ```{r} data(CVXResults) ``` In addition, we use `lars` to solve the same problem. ```{r,message=FALSE} require(lars) ``` ```{r} fit_lars = lars(x, y, type = "lasso", intercept = F, normalize = F) beta_lars = predict(fit_lars, s = 8/2, type = "coefficients", mode = "lambda")$coefficients ``` The results are listed below up to 6 decimal digits (due to convergence thresholds). ```{r} cmp = round(cbind(beta_glmnet, beta_lars, beta_CVX), digits = 6) colnames(cmp) = c("beta_glmnet", "beta_lars", "beta_CVX") cmp ``` ## References glmnet/inst/doc/glmnet.pdf0000644000176200001440000124045313574046200015255 0ustar liggesusers%PDF-1.5 % 1 0 obj << /Type /ObjStm /Length 4698 /Filter /FlateDecode /N 78 /First 654 >> stream x\Ys۸~o'SSSS8Nd̩y%D%k,7tu0$V6ݍޘ 30TfJaRƆ%%KbZj&hb2U1e(,AC5AaQSb>eJP{ƔN( Ei;&SDd$ QMRwł*"8f1VbZ*f@` m5KXLѤ" <JCcYxHcxBQwPk̑Vz3Gh OcO@3՘90Y@8p%a&fV)6Yi/! u}7Y+cc m̬SB?f6 H!W8Ö`ff4&!ưҤFaDuL& y9!yu9- digk+v3~5{N|],I>$p P0W_R$ƅ~&sMKCѮ.*kҏ2b~4,sߑQLH%Ŀ~7(o ֵ\v;ۛrbOP8]lG9}yv^,Vj,nK .W(y+'#UMclc1#\c-,lb=/K$kj 暔kƑA뤻̍&Buݥ/KWN`PޱrL/w\HщyPdk'~vw~I/8$0|( R9< ݷ%{2? 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ESr#(#Fx{o]|[Nŷβӄ3^bD| 4 `%,+4%zߘKMڐ 6>e95Mk[ei7j))u$3&ńFϱ΄rfSwh‚PrLZTUr-NX% ~qRgg? \KqϬF lϬyz{Y B㌐3U7YL{kL=-0Mi[ŤB?.cb׫U*YkB9ӗnHe > /W [ 1 3 1 ] /Info 3 0 R /Root 2 0 R /Size 309 /ID [<30b260927d55bbd48b1c73a919256eea>] >> stream xO+DQs3[׸ !{x (KW aKXlmNa$:＋۝ŧgsi5XUKM n5t/& -5>홫kpJe@r\H"λ%[eپ>de>dשӟ'-cV'$IWOٽv; ge KY?Q yEJ)ۗ#f#oLʴ|TerrX3xuy:{jNc< endstream endobj startxref 343785 %%EOF glmnet/inst/doc/Coxnet.R0000644000176200001440000000157213574046150014657 0ustar liggesusers## ------------------------------------------------------------------------ library("glmnet") library("survival") patient.data <- readRDS("assets/coxnet.RDS") ## ---- warning = TRUE----------------------------------------------------- cv.fit <- cv.glmnet(patient.data$x, Surv(patient.data$time, patient.data$status), family="cox", maxit = 1000) fit <- glmnet(patient.data$x, Surv(patient.data$time,patient.data$status), family = "cox", maxit = 1000) ## ------------------------------------------------------------------------ plot(cv.fit) cv.fit$lambda.min ## ------------------------------------------------------------------------ Coefficients <- coef(fit, s = cv.fit$lambda.min) Active.Index <- which(Coefficients != 0) Active.Coefficients <- Coefficients[Active.Index] ## ------------------------------------------------------------------------ Active.Index Active.Coefficients glmnet/inst/doc/glmnet.R0000644000176200001440000002776313574046164014724 0ustar liggesusers## ---- eval=FALSE--------------------------------------------------------- # install.packages("glmnet", repos = "https://cran.us.r-project.org") ## ------------------------------------------------------------------------ library(glmnet) ## ------------------------------------------------------------------------ data(QuickStartExample) ## ------------------------------------------------------------------------ fit = glmnet(x, y) ## ------------------------------------------------------------------------ plot(fit) ## ----height = 4---------------------------------------------------------- print(fit) ## ------------------------------------------------------------------------ coef(fit,s=0.1) ## ------------------------------------------------------------------------ set.seed(29) nx = matrix(rnorm(10*20),10,20) predict(fit,newx=nx,s=c(0.1,0.05)) ## ------------------------------------------------------------------------ cvfit = cv.glmnet(x, y) ## ------------------------------------------------------------------------ plot(cvfit) ## ------------------------------------------------------------------------ cvfit$lambda.min ## ------------------------------------------------------------------------ coef(cvfit, s = "lambda.min") ## ------------------------------------------------------------------------ predict(cvfit, newx = x[1:5,], s = "lambda.min") ## ------------------------------------------------------------------------ fit = glmnet(x, y, alpha = 0.2, weights = c(rep(1,50),rep(2,50)), nlambda = 20) ## ------------------------------------------------------------------------ print(fit) ## ------------------------------------------------------------------------ plot(fit, xvar = "lambda", label = TRUE) ## ------------------------------------------------------------------------ plot(fit, xvar = "dev", label = TRUE) ## ------------------------------------------------------------------------ fit = glmnet(x, y) any(fit$lambda == 0.5) coef.apprx = coef(fit, s = 0.5, exact = FALSE) coef.exact = coef(fit, s = 0.5, exact = TRUE, x=x, y=y) cbind2(coef.exact, coef.apprx) ## ------------------------------------------------------------------------ predict(fit, newx = x[1:5,], type = "response", s = 0.05) ## ------------------------------------------------------------------------ cvfit = cv.glmnet(x, y, type.measure = "mse", nfolds = 20) ## ---- eval=FALSE--------------------------------------------------------- # require(doMC) # registerDoMC(cores=2) # X = matrix(rnorm(1e4 * 200), 1e4, 200) # Y = rnorm(1e4) ## ---- eval=FALSE--------------------------------------------------------- # system.time(cv.glmnet(X, Y)) ## ---- echo=FALSE--------------------------------------------------------- structure(c(2.44, 0.08, 2.518, 0, 0), class = "proc_time", .Names = c("user.self", "sys.self", "elapsed", "user.child", "sys.child")) ## ---- eval=FALSE--------------------------------------------------------- # system.time(cv.glmnet(X, Y, parallel = TRUE)) ## ---- echo=FALSE--------------------------------------------------------- structure(c(0.508999999999999, 0.057, 1.56699999999999, 1.941, 0.1), class = "proc_time", .Names = c("user.self", "sys.self", "elapsed", "user.child", "sys.child")) ## ------------------------------------------------------------------------ cvfit$lambda.min coef(cvfit, s = "lambda.min") predict(cvfit, newx = x[1:5,], s = "lambda.min") ## ------------------------------------------------------------------------ foldid=sample(1:10,size=length(y),replace=TRUE) cv1=cv.glmnet(x,y,foldid=foldid,alpha=1) cv.5=cv.glmnet(x,y,foldid=foldid,alpha=.5) cv0=cv.glmnet(x,y,foldid=foldid,alpha=0) ## ------------------------------------------------------------------------ par(mfrow=c(2,2)) plot(cv1);plot(cv.5);plot(cv0) plot(log(cv1$lambda),cv1$cvm,pch=19,col="red",xlab="log(Lambda)",ylab=cv1$name) points(log(cv.5$lambda),cv.5$cvm,pch=19,col="grey") points(log(cv0$lambda),cv0$cvm,pch=19,col="blue") legend("topleft",legend=c("alpha= 1","alpha= .5","alpha 0"),pch=19,col=c("red","grey","blue")) ## ------------------------------------------------------------------------ tfit=glmnet(x,y,lower=-.7,upper=.5) plot(tfit) ## ------------------------------------------------------------------------ p.fac = rep(1, 20) p.fac[c(5, 10, 15)] = 0 pfit = glmnet(x, y, penalty.factor = p.fac) plot(pfit, label = TRUE) ## ------------------------------------------------------------------------ set.seed(101) x=matrix(rnorm(1000),100,10) y=rnorm(100) vn=paste("var",1:10) fit=glmnet(x,y) plot(fit) ## ------------------------------------------------------------------------ par(mar=c(4.5,4.5,1,4)) plot(fit) vnat=coef(fit) vnat=vnat[-1,ncol(vnat)] # remove the intercept, and get the coefficients at the end of the path axis(4, at=vnat,line=-.5,label=vn,las=1,tick=FALSE, cex.axis=0.5) ## ------------------------------------------------------------------------ data(MultiGaussianExample) ## ------------------------------------------------------------------------ mfit = glmnet(x, y, family = "mgaussian") ## ------------------------------------------------------------------------ plot(mfit, xvar = "lambda", label = TRUE, type.coef = "2norm") ## ------------------------------------------------------------------------ predict(mfit, newx = x[1:5,], s = c(0.1, 0.01)) ## ------------------------------------------------------------------------ cvmfit = cv.glmnet(x, y, family = "mgaussian") ## ------------------------------------------------------------------------ plot(cvmfit) ## ------------------------------------------------------------------------ cvmfit$lambda.min cvmfit$lambda.1se ## ------------------------------------------------------------------------ data(BinomialExample) ## ------------------------------------------------------------------------ fit = glmnet(x, y, family = "binomial") ## ------------------------------------------------------------------------ plot(fit, xvar = "dev", label = TRUE) ## ------------------------------------------------------------------------ predict(fit, newx = x[1:5,], type = "class", s = c(0.05, 0.01)) ## ------------------------------------------------------------------------ cvfit = cv.glmnet(x, y, family = "binomial", type.measure = "class") ## ------------------------------------------------------------------------ plot(cvfit) ## ------------------------------------------------------------------------ cvfit$lambda.min cvfit$lambda.1se ## ------------------------------------------------------------------------ coef(cvfit, s = "lambda.min") ## ------------------------------------------------------------------------ predict(cvfit, newx = x[1:10,], s = "lambda.min", type = "class") ## ------------------------------------------------------------------------ data(MultinomialExample) ## ------------------------------------------------------------------------ fit = glmnet(x, y, family = "multinomial", type.multinomial = "grouped") ## ------------------------------------------------------------------------ plot(fit, xvar = "lambda", label = TRUE, type.coef = "2norm") ## ------------------------------------------------------------------------ cvfit=cv.glmnet(x, y, family="multinomial", type.multinomial = "grouped", parallel = TRUE) plot(cvfit) ## ------------------------------------------------------------------------ predict(cvfit, newx = x[1:10,], s = "lambda.min", type = "class") ## ------------------------------------------------------------------------ data(PoissonExample) ## ------------------------------------------------------------------------ fit = glmnet(x, y, family = "poisson") ## ------------------------------------------------------------------------ plot(fit) ## ------------------------------------------------------------------------ coef(fit, s = 1) predict(fit, newx = x[1:5,], type = "response", s = c(0.1,1)) ## ------------------------------------------------------------------------ cvfit = cv.glmnet(x, y, family = "poisson") ## ------------------------------------------------------------------------ plot(cvfit) ## ------------------------------------------------------------------------ opt.lam = c(cvfit$lambda.min, cvfit$lambda.1se) coef(cvfit, s = opt.lam) ## ------------------------------------------------------------------------ data(CoxExample) y[1:5,] ## ------------------------------------------------------------------------ fit = glmnet(x, y, family = "cox") ## ------------------------------------------------------------------------ plot(fit) ## ------------------------------------------------------------------------ coef(fit, s = 0.05) ## ------------------------------------------------------------------------ cvfit = cv.glmnet(x, y, family = "cox") ## ------------------------------------------------------------------------ plot(cvfit) ## ------------------------------------------------------------------------ cvfit$lambda.min cvfit$lambda.1se ## ------------------------------------------------------------------------ coef.min = coef(cvfit, s = "lambda.min") active.min = which(coef.min != 0) index.min = coef.min[active.min] ## ------------------------------------------------------------------------ index.min coef.min ## ------------------------------------------------------------------------ data(SparseExample) ## ------------------------------------------------------------------------ class(x) ## ------------------------------------------------------------------------ fit = glmnet(x, y) ## ------------------------------------------------------------------------ cvfit = cv.glmnet(x, y) plot(cvfit) ## ------------------------------------------------------------------------ i = sample(1:5, size = 25, replace = TRUE) j = sample(1:20, size = 25, replace = TRUE) x = rnorm(25) nx = sparseMatrix(i = i, j = j, x = x, dims = c(5, 20)) predict(cvfit, newx = nx, s = "lambda.min") ## ------------------------------------------------------------------------ data(QuickStartExample) fit = glmnet(x, y) print(fit) ## ------------------------------------------------------------------------ glmnet.control(fdev = 0) fit = glmnet(x, y) print(fit) ## ------------------------------------------------------------------------ glmnet.control(factory = TRUE) ## ------------------------------------------------------------------------ glmnet.control() ## ---- echo=FALSE--------------------------------------------------------- data(QuickStartExample) ## ----eval=FALSE---------------------------------------------------------- # fit = glmnet(x, y, intercept = F, standardize = F, lambda = 8/(2*dim(x)[1]), thresh = 1e-20) ## ----eval=FALSE---------------------------------------------------------- # beta_glmnet = as.matrix(predict(fit, type = "coefficients")[-1,]) ## ------------------------------------------------------------------------ fit = glmnet(x, y, intercept = F, standardize = F, thresh = 1e-20) beta_glmnet = as.matrix(predict(fit, s = 8/(2*dim(x)[1]), type = "coefficients", exact = TRUE, x=x, y=y)[-1,]) ## ---- eval=FALSE--------------------------------------------------------- # library(CVXfromR) # setup.dir = "change/this/to/your/cvx/directory" # n = dim(x)[1]; p = dim(x)[2] # cvxcode = paste("variables beta(p)", # "minimize(square_pos(norm(y - x * beta, 2)) + lambda * norm(beta, 1))", # sep = ";") # Lasso = CallCVX(cvxcode, const.var = list(p = p, x = x, y = y, lambda = 8), opt.var.names = "beta", setup.dir = setup.dir, matlab.call = "change/this/to/path/to/matlab") # beta_CVX = Lasso$beta ## ------------------------------------------------------------------------ data(CVXResults) ## ----message=FALSE------------------------------------------------------- require(lars) ## ------------------------------------------------------------------------ fit_lars = lars(x, y, type = "lasso", intercept = F, normalize = F) beta_lars = predict(fit_lars, s = 8/2, type = "coefficients", mode = "lambda")$coefficients ## ------------------------------------------------------------------------ cmp = round(cbind(beta_glmnet, beta_lars, beta_CVX), digits = 6) colnames(cmp) = c("beta_glmnet", "beta_lars", "beta_CVX") cmp glmnet/inst/CITATION0000644000176200001440000000331513534017153013656 0ustar liggesuserscitHeader("To cite glmnet in publications use:") citEntry(entry = "Article", title = "Regularization Paths for Generalized Linear Models via Coordinate Descent", author = personList(as.person("Jerome Friedman"), as.person("Trevor Hastie"), as.person("Robert Tibshirani")), journal = "Journal of Statistical Software", year = "2010", volume = "33", number = "1", pages = "1--22", url = "http://www.jstatsoft.org/v33/i01/", textVersion = paste("Jerome Friedman, Trevor Hastie, Robert Tibshirani (2010).", "Regularization Paths for Generalized Linear Models via Coordinate Descent.", "Journal of Statistical Software, 33(1), 1-22.", "URL http://www.jstatsoft.org/v33/i01/.") ) citEntry(entry = "Article", title = "Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent", author = personList(as.person("Noah Simon"), as.person("Jerome Friedman"), as.person("Trevor Hastie"), as.person("Rob Tibshirani")), journal = "Journal of Statistical Software", year = "2011", volume = "39", number = "5", pages = "1--13", url = "http://www.jstatsoft.org/v39/i05/", textVersion = paste("Noah Simon, Jerome Friedman, Trevor Hastie, Rob Tibshirani (2011).", "Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent.", "Journal of Statistical Software, 39(5), 1-13.", "URL http://www.jstatsoft.org/v39/i05/."), header = "If coxnet is used, please also cite:" )