iwrlars/0000755000723300071640000000000010726361617012474 5ustar brenardMultBildiwrlars/R/0000755000723300071640000000000010726361617012675 5ustar brenardMultBildiwrlars/R/larfuncs.R0000644000723300071640000004557510555652240014650 0ustar brenardMultBild"coef.lars" <- function(object, ...) { predict(object, type = "coefficient", ...)$coef } "cv.folds" <- function(n, folds = 10) { split(sample(1:n), rep(1:folds, length = n)) } "cv.lars" <- function(x, y, K = 10, fraction = seq(from = 0, to = 1, length = 100), trace = FALSE, plot.it = TRUE, se = TRUE, ...) { all.folds <- cv.folds(length(y), K) residmat <- matrix(0, length(fraction), K) for(i in seq(K)) { omit <- all.folds[[i]] fit <- lars(x[ - omit, ], y[ - omit], trace = trace, ...) fit <- predict(fit, x[omit, ,drop=FALSE], mode = "fraction", s = fraction )$fit if(length(omit)==1)fit<-matrix(fit,nrow=1) residmat[, i] <- apply((y[omit] - fit)^2, 2, mean) if(trace) cat("\n CV Fold", i, "\n\n") } cv <- apply(residmat, 1, mean) cv.error <- sqrt(apply(residmat, 1, var)/K) object<-list(fraction = fraction, cv = cv, cv.error = cv.error) if(plot.it) plotCVLars(object,se=se) invisible(object) } "downdateR" <- function(R, k = p) { p <- dim(R)[1] if(p == 1) return(NULL) R <- delcol(R, rep(1, p), k)[[1]][ - p, , drop = FALSE] attr(R, "rank") <- p - 1 R # Built-in Splus utility } "error.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) } "lars" <- function(x, y, type = c("lasso","plasso", "lar", "forward.stagewise"), trace = FALSE, Gram, eps = .Machine$double.eps, max.steps, use.Gram = TRUE) { ### program automatically centers and standardizes predictors. ### ### Original program by Brad Efron September 2001 ### Recoded by Trevor Hastie November 2001 ### Computational efficiency December 22, 2001 ### Bug fixes and singularities February 2003 ### Conversion to R April 2003 ### Copyright Brad Efron and Trevor Hastie ### ### Extension for "plasso" by Bernhard Renard, Marc Kirchner, January 2007 call <- match.call() type <- match.arg(type) TYPE <- switch(type, lasso = "LASSO", plasso = "PLASSO", lar = "LAR", forward.stagewise = "Forward Stagewise") if(trace) cat(paste(TYPE, "sequence\n")) nm <- dim(x) n <- nm[1] m <- nm[2] im <- inactive <- seq(m) one <- rep(1, n) vn <- dimnames(x)[[2]] ### Center x and y, and scale x, and save the means and sds meanx <- drop(one %*% x)/n x <- scale(x, meanx, FALSE) # centers x normx <- sqrt(drop(one %*% (x^2))) nosignal<-normx/sqrt(n) < eps if(any(nosignal))# ignore variables with too small a variance { ignores<-im[nosignal] inactive<-im[-ignores] normx[nosignal]<-eps*sqrt(n) if(trace) cat("LARS Step 0 :\t", sum(nosignal), "Variables with Variance < \eps; dropped for good\n") # } else ignores <- NULL #singularities; augmented later as well names(normx) <- NULL x <- scale(x, FALSE, normx) # scales x if(use.Gram & missing(Gram)) { if(m > 500 && n < m) cat("There are more than 500 variables and n= Cmax - eps } else { new <- abs(C) >= Cmax - eps } C <- C[!new] # for later new <- inactive[new] # Get index numbers print(paste("Cmax:", Cmax, "new:", paste(as.character(new)))) ### We keep the choleski R of X[,active] (in the order they enter) for(inew in new) { if(use.Gram) { R <- updateR(Gram[inew, inew], R, drop(Gram[ inew, active]), Gram = TRUE,eps=eps) } else { R <- updateR(x[, inew], R, x[, active], Gram = FALSE,eps=eps) } if(attr(R, "rank") == length(active)) { ##singularity; back out nR <- seq(length(active)) R <- R[nR, nR, drop = FALSE] attr(R, "rank") <- length(active) ignores <- c(ignores, inew) action <- c(action, - inew) if(trace) cat("LARS Step", k, ":\t Variable", inew, "\tcollinear; dropped for good\n") # } else { if(first.in[inew] == 0) first.in[inew] <- k active <- c(active, inew) #NN# if added if (type=="plasso"){ Sign <- c(Sign, 1) } else { Sign <- c(Sign, sign(Cvec[inew])) } action <- c(action, inew) if(trace) cat("LARS Step", k, ":\t Variable", inew, "\tadded\n") # } } } else action <- - dropid Gi1 <- backsolve(R, backsolvet(R, Sign)) ### Now we have to do the forward.stagewise dance ### This is equivalent to NNLS dropouts<-NULL if(type == "forward.stagewise") { directions <- Gi1 * Sign if(!all(directions > 0)) { if(use.Gram) { nnls.object <- nnls.lars(active, Sign, R, directions, Gram[active, active], trace = trace, use.Gram = TRUE,eps=eps) } else { nnls.object <- nnls.lars(active, Sign, R, directions, x[, active], trace = trace, use.Gram = FALSE,eps=eps) } positive <- nnls.object$positive dropouts <-active[-positive] action <- c(action, -dropouts) active <- nnls.object$active Sign <- Sign[positive] Gi1 <- nnls.object$beta[positive] * Sign R <- nnls.object$R C <- Cvec[ - c(active, ignores)] } } A <- 1/sqrt(sum(Gi1 * Sign)) w <- A * Gi1 # note that w has the right signs if(!use.Gram) u <- drop(x[, active, drop = FALSE] %*% w) ### ### Now we see how far we go along this direction before the ### next competitor arrives. There are several cases ### ### If the active set is all of x, go all the way if(length(active) >= min(n-1, m - length(ignores) ) ) { gamhat <- Cmax/A } else { if(use.Gram) { a <- drop(w %*% Gram[active, - c(active,ignores), drop = FALSE]) } else { a <- drop(u %*% x[, - c(active, ignores), drop=FALSE]) } #NN# if plasso if (type=="plasso"){ gam <- c((Cmax - C)/(A - a)) #browser(); } else { gam <- c((Cmax - C)/(A - a), (Cmax + C)/(A + a)) } ### Any dropouts will have gam=0, which are ignored here #NN# if plasso if (type=="plasso"){ gamhat <- min(gam[gam > eps]) } else { gamhat <- min(gam[gam > eps], Cmax/A) } } if(type == "lasso"|type=="plasso") { dropid <- NULL b1 <- beta[k, active] # beta starts at 0 z1 <- - b1/w zmin <- min(z1[z1 > eps], gamhat) if(zmin < gamhat) { gamhat <- zmin drops <- z1 == zmin } else drops <- FALSE } beta[k + 1, ] <- beta[k, ] beta[k + 1, active] <- beta[k + 1, active] + gamhat * w if(use.Gram) { Cvec <- Cvec - gamhat * Gram[, active, drop = FALSE] %*% w } else { residuals <- residuals - gamhat * u Cvec <- drop(t(residuals) %*% x) } Gamrat <- c(Gamrat, gamhat/(Cmax/A)) arc.length <- c(arc.length, gamhat) ### Check if we have to drop any guys if((type == "lasso"|type=="plasso") && any(drops)) { dropid <- seq(drops)[drops] #turns the TRUE, FALSE vector into numbers for(id in rev(dropid)) { if(trace) cat("Lasso Step", k+1, ":\t Variable", active[ id], "\tdropped\n") R <- downdateR(R, id) } dropid <- active[drops] # indices from 1:m beta[k+1,dropid]<-0 # added to make sure dropped coef is zero active <- active[!drops] Sign <- Sign[!drops] } if(!is.null(vn)) names(action) <- vn[abs(action)] actions[[k]] <- action inactive <- im[ - c(active, ignores)] } beta <- beta[seq(k + 1), ] # dimnames(beta) <- list(paste(0:k), vn) ### Now compute RSS and R2 if(trace) cat("Computing residuals, RSS etc .....\n") residuals <- y - x %*% t(beta) beta <- scale(beta, FALSE, normx) RSS <- apply(residuals^2, 2, sum) #Problem with RSS for plasso by the fact that the last estimates do give some weired results R2 <- 1 - RSS/RSS[1] Cp <- ((n - k - 1) * RSS)/rev(RSS)[1] - n + 2 * seq(k + 1) object <- list(call = call, type = TYPE, R2 = R2, RSS = RSS, Cp = Cp, actions = actions[seq(k)], entry = first.in, Gamrat = Gamrat, arc.length = arc.length, Gram = if(use.Gram) Gram else NULL, beta = beta, mu = mu, normx = normx, meanx = meanx) class(object) <- "lars" object } "nnls.lars" <- function(active, Sign, R, beta, Gram, eps = 1e-10, trace = FALSE, use.Gram = TRUE) { ### Modified 05/15/03 to allow for more than one addition to the set ### Lawson and Hanson page 161 ### Go back to the first positive coefficent vector; can assume its in order ### Note that X'y is constant for all these guys; ### we assume WOLOG this constant is 1 ### We also assume we have come into this because we have a negative coeff ### If use.Gram is FALSE, then Gram comes in as x if(!use.Gram) x <- Gram # to avoid confusion M<-m <- length(active) im <- seq(m) positive <- im zero <- NULL ### Get to the stage where beta.old is all positive while(m>1) { zero.old<-c(m,zero) R.old <- downdateR(R, m) beta0 <- backsolve(R.old, backsolvet(R.old, Sign[ - zero.old]))*Sign[-zero.old] beta.old <- c(beta0,rep(0,length(zero.old))) if(all(beta0 >0))break m <-m-1 zero<-zero.old positive<-im[-zero] R<-R.old beta<-beta.old } ### Now we do the NNLS backtrack dance while(TRUE) { while(!all(beta[positive] > 0)) { alpha0 <- beta.old/(beta.old - beta) alpha <- min(alpha0[positive][(beta <= 0)[positive]]) beta.old <- beta.old + alpha * (beta - beta.old) dropouts<-match(alpha,alpha0[positive],0) ### Used to have the following line, but this failed occasionally ### dropouts <- seq(positive)[abs(beta.old[positive]) < eps] for(i in rev(dropouts)) R <- downdateR(R, i) positive <- positive[ - dropouts] # there is an order in R zero <- im[ - positive] beta0 <- backsolve(R, backsolvet(R, Sign[positive])) * Sign[positive] beta <- beta.old * 0 beta[positive] <- beta0 } ### Now all those in have a positive coefficient if(use.Gram) { w <- 1 - Sign * drop(Gram %*% (Sign * beta)) #should be zero for some } else { jw <- x %*% (Sign * beta) w <- 1 - Sign * drop(t(jw) %*% x) } if((length(zero) == 0) || all(w[zero] <= 0)) break add <- order(w)[M] if(use.Gram) { R <- updateR(Gram[add, add], R, drop(Gram[add, positive]), Gram = TRUE,eps=eps) } else { R <- updateR(x[, add], R, x[, positive], Gram = FALSE,eps=eps) } positive <- c(positive, add) zero <- setdiff(zero, add) beta0 <- backsolve(R, backsolvet(R, Sign[positive])) * Sign[ positive] beta[positive] <- beta0 } if(trace) { dropouts<-active[-positive] for(i in dropouts){ cat("NNLS Step:\t Variable", i, "\tdropped\n") } } list(active = active[positive], R = R, beta = beta, positive = positive ) } "plotCVLars" <- function(cv.lars.object,se=TRUE){ attach(cv.lars.object) plot(fraction, cv, type = "b", ylim = range(cv, cv + cv.error, cv - cv.error)) if(se) error.bars(fraction, cv + cv.error, cv - cv.error, width = 1/length(fraction)) detach(cv.lars.object) invisible() } "plot.lars" <- function(x, xvar=c("norm","df","arc.length"), breaks = TRUE, plottype = c("coefficients", "Cp"), omit.zeros = TRUE, eps = 1e-10, ...) { object <- x plottype <- match.arg(plottype) xvar <- match.arg(xvar) coef1 <- object$beta ### Get rid of many zero coefficients coef1 <- scale(coef1, FALSE, 1/object$normx) if(omit.zeros) { c1 <- drop(rep(1, nrow(coef1)) %*% abs(coef1)) nonzeros <- c1 > eps cnums <- seq(nonzeros)[nonzeros] coef1 <- coef1[, nonzeros] } else cnums <- seq(ncol(coef1)) s1<-switch(xvar, norm={ s1 <- apply(abs(coef1), 1, sum) s1/max(s1) }, df=seq(length(object$arc.length)+1), arc.length=cumsum(c(0,object$arc.length)) ) xname<-switch(xvar, norm="|beta|/max|beta|", df="Df", arc.length="Arc Length" ) if(plottype == "Cp") { Cp <- object$Cp plot(s1, Cp, type = "b", xlab=xname,main = object$type, ...) } else { matplot(s1, coef1, xlab = xname, ..., type = "b", pch = "*", ylab = "Standardized Coefficients") title(object$type,line=2.5) abline(h = 0, lty = 3) axis(4, at = coef1[nrow(coef1), ], label = paste(cnums ), cex = 0.80000000000000004, adj = 0) if(breaks) { axis(3, at = s1, labels = paste(seq(s1)-1),cex=.8) abline(v = s1) } } invisible() } "predict.lars" <- function(object, newx, s, type = c("fit", "coefficients"), mode = c("step", "fraction", "norm"), ...) { mode <- match.arg(mode) type <- match.arg(type) if(missing(newx) & type == "fit") { warning("Type=fit with no newx argument; type switched to coefficients" ) type <- "coefficients" } betas <- object$beta sbetas <- scale(betas, FALSE, 1/object$normx) #scaled for unit norm x kp <- dim(betas) k <- kp[1] p <- kp[2] steps <- seq(k) if(missing(s)) { s <- steps mode <- "step" } sbeta <- switch(mode, step = { if(any(s < 0) | any(s > k)) stop("Argument s out of range") steps } , fraction = { if(any(s > 1) | any(s < 0)) stop("Argument s out of range") nbeta <- drop(abs(sbetas) %*% rep(1, p)) nbeta/nbeta[k] } , norm = { nbeta <- drop(abs(sbetas) %*% rep(1, p)) if(any(s > nbeta[k]) | any(s < 0)) stop("Argument s out of range") nbeta } ) sfrac <- (s - sbeta[1])/(sbeta[k] - sbeta[1]) sbeta <- (sbeta - sbeta[1])/(sbeta[k] - sbeta[1]) usbeta<-unique(sbeta) useq<-match(usbeta,sbeta) sbeta<-sbeta[useq] betas<-betas[useq,] coord <- approx(sbeta, seq(sbeta), sfrac)$y left <- floor(coord) right <- ceiling(coord) newbetas <- ((sbeta[right] - sfrac) * betas[left, , drop = FALSE] + (sfrac - sbeta[left]) * betas[right, , drop = FALSE])/(sbeta[right] - sbeta[ left]) newbetas[left == right, ] <- betas[left[left == right], ] robject <- switch(type, coefficients = list(s = s, fraction = sfrac, mode = mode, coefficients = drop(newbetas)), fit = list(s = s, fraction = sfrac, mode = mode, fit = drop( scale(newx, object$meanx, FALSE) %*% t(newbetas)) + object$ mu)) robject } "print.lars" <- function(x, ...) { cat("\nCall:\n") dput(x$call) cat("R-squared:", format(round(rev(x$R2)[1], 3)), "\n") actions <- x$actions jactions <- unlist(actions) jsteps <- rep(seq(along = actions), sapply(actions, length)) actmat <- rbind(jsteps, jactions) vn <- names(jactions) if(is.null(vn)) vn <- rep("", length(jactions)) dimnames(actmat) <- list(c("Step", "Var"), vn) cat(paste("Sequence of", x$type, "moves:\n")) print(actmat[2:1, ]) invisible(x) } "updateR" <- function(xnew, R = NULL, xold, eps = .Machine$double.eps, Gram = FALSE) { ###Gram argument determines the nature of xnew and xold xtx <- if(Gram) xnew else sum(xnew^2) norm.xnew <- sqrt(xtx) if(is.null(R)) { R <- matrix(norm.xnew, 1, 1) attr(R, "rank") <- 1 return(R) } Xtx <- if(Gram) xold else drop(t(xnew) %*% xold) r <- backsolvet(R, Xtx) rpp <- norm.xnew^2 - sum(r^2) rank <- attr(R, "rank") ### check if R is machine singular if(rpp <= eps) rpp <- eps else { rpp <- sqrt(rpp) rank <- rank + 1 } R <- cbind(rbind(R, 0), c(r, rpp)) attr(R, "rank") <- rank R } "backsolvet"<- function(r, x, k=ncol(r)) { backsolve(r,x,k,transpose=TRUE) } "delcol" <- function(r, z, k = p) { p <- dim(r)[1] r <- r[, - k, drop = FALSE] z <- as.matrix(z) dz <- dim(z) storage.mode(r) <- storage.mode(z) <- "double" .Fortran("delcol", r, as.integer(p), as.integer(k), z, as.integer(dz[1]), as.integer(dz[2]), PACKAGE="iwrlars")[c(1, 4)] } ".First.lib" <- function (lib, pkg) library.dynam("iwrlars", pkg, lib) iwrlars/src/0000755000723300071640000000000010726361617013263 5ustar brenardMultBildiwrlars/src/delcol.r0000644000723300071640000000144610555652241014711 0ustar brenardMultBildsubroutine delcol(r,p,k,z,n,nz) # the p by p-1 matrix r has been formed from a # p by p upper-triangular by deleting column k integer p,k,n,nz; double precision r(p,1), z(n,nz) integer p1,i,j; double precision a,b,c,s,tau p1 = p-1 for(i = k; idabs(a)) { tau = -a/b; s = 1/dsqrt(1d0+tau*tau); c = s * tau } else { tau = -b/a; c = 1/dsqrt(1d0+tau*tau); s = c * tau } # update r and z r(i,i) = c*a - s*b # just for checking; then r(i+1,i)=0 r(i+1,i) = s*a + c*b for(j = i +1; j<=p1; j = j+1) { a = r(i,j); b = r(i+1,j) r(i,j) = c*a - s *b r(i+1,j) = s*a + c * b } for(j=1; j<=nz; j = j+1) { a = z(i,j); b = z(i+1,j) z(i,j) = c*a - s*b z(i+1,j) = s*a + c*b } } return end iwrlars/src/delcol.f0000644000723300071640000000202610555652241014670 0ustar brenardMultBild subroutine delcol(r,p,k,z,n,nz) integer p,k,n,nz double precision r(p,1), z(n,nz) integer p1,i,j double precision a,b,c,s,tau p1 = p-1 i = k 23000 if(.not.(i .lt. p))goto 23002 a = r(i,i) b = r(i+1,i) if(.not.(b .eq. 0d0))goto 23003 goto 23001 23003 continue if(.not.(dabs(b) .gt. dabs(a)))goto 23005 tau = -a/b s = 1/dsqrt(1d0+tau*tau) c = s * tau goto 23006 23005 continue tau = -b/a c = 1/dsqrt(1d0+tau*tau) s = c * tau 23006 continue r(i,i) = c*a - s*b r(i+1,i) = s*a + c*b j = i +1 23007 if(.not.(j .le. p1))goto 23009 a = r(i,j) b = r(i+1,j) r(i,j) = c*a - s *b r(i+1,j) = s*a + c * b j = j+1 goto 23007 23009 continue j=1 23010 if(.not.(j .le. nz))goto 23012 a = z(i,j) b = z(i+1,j) z(i,j) = c*a - s*b z(i+1,j) = s*a + c*b j = j+1 goto 23010 23012 continue 23001 i=i+1 goto 23000 23002 continue return end iwrlars/data/0000755000723300071640000000000010726361617013405 5ustar brenardMultBildiwrlars/data/diabetes.RData0000644000723300071640000243443310555652241016113 0ustar brenardMultBildRDX2 X  diabetesD?~iRO^տCH?&,+͸ ?v D( VH++=?AV2?Z-}俲&2?Ԡ=g?8?v D(?60GyjCvne|?楈ǿػ]$J+IzIz?60GyJd 8tHLs# ꀿ>*3?.x*3?׺?̺I?8??D?뿂^տCH^տCH?v D(Yo뿮:n?d!.?60Gy?Ԡ=g܊vU ؿB,?/ï|l g6?׺?m1οjC$J+l g6l g6]?Z-}?AV2&l g6]&AY?/ï|H++=?Z-}?~iRO?8埿^տCH^տCH?AV2?D??D?뿂X8獈2?v D(25?楈?D?뿐ZS޿ػ?\8Q^?60Gy&?60GyjCH++=?D??60Gy$J+B,&?N*3ػ??60Gy?60Gy?8埿hῴh=v8tHL?v D(^տCH?v0vne|?N*3?jC?Z-}?N*3Yo뿛]?̺I25?/ï|jC]ػ?v0?D??/ï|?60Gy?/ï|?Ԡ=g?N*3?v0?8?d!.?6*3?Uy}?`?楈?̺I^տCH?60Gy? bػ,5'݇h?v0?K?̺I?Ԡ=g:n?v D(?d!.ʿJd ?Ԡ=gZS?̺I?N*3hvne|:n?~iRO?8?Z-}?d!.ʿ$J+?\8Q^?׺H++=ZS޿?d!.?.x<]hΏs# ?60Gy^տCH?d!.?8埿^տCH?8埿&??v D(?׺?`?d!.ʿH++=?.x*3l g6h?׺:nIz?.x*3?8?XYrZS?܊vU ?Ԡ=g?AV2jC?v D(? bjC?Kvne|]jC?v0?`?~iROB,?v0?\8Q^B,?̺I&AY?l g6?楈?v0]?8埿,5'݇h܊vU ?Z-}vne|?Z-}俧H++=H++=?zDĜ?zDĜDĜDĜ?z?z?zDĜDĜ?zDĜ?zDĜ?zDĜ?zDĜDĜDĜ?zDĜ?zDĜ?zDĜDĜDĜ?zDĜDĜ?zDĜDĜ?zDĜDĜ?z?z?zDĜ?zDĜ?z?zDĜDĜ?z?zDĜ?zDĜDĜDĜDĜDĜDĜDĜ?zDĜ?z?zDĜ?z?z?z?z?zDĜDĜDĜ?z?z?z?z?zDĜDĜDĜDĜ?zDĜDĜDĜDĜ?zDĜ?zDĜDĜDĜDĜDĜDĜDĜ?zDĜ?zDĜDĜ?zDĜ?zDĜ?zDĜDĜ?z?z?zDĜDĜ?zDĜ?z?zDĜ?zDĜDĜDĜ?z?zDĜ?zDĜ?zDĜDĜ?zDĜ?z?zDĜDĜDĜDĜ?zDĜ?zDĜ?zDĜ?zDĜ?zDĜ?z?zDĜDĜ?z?z?z?zDĜ?zDĜDĜDĜ?zDĜ?zDĜDĜ?zDĜ?zDĜ?zDĜ?z?z?zDĜ?zDĜDĜDĜ?zDĜ?z?zDĜ?z?zDĜDĜDĜDĜ?z?z?zDĜ?zDĜ?zDĜDĜDĜ?z?z?z?zDĜ?zDĜDĜ?zDĜDĜDĜDĜDĜ?z?zDĜDĜDĜ?zDĜ?zDĜDĜ?z?z?zDĜ?z?zDĜ?zDĜDĜ?zDĜDĜ?zDĜ?z?z?z?z?zDĜDĜDĜDĜDĜDĜ?zDĜDĜ?zDĜDĜ?z?zDĜDĜDĜ?z?z?z?z?zDĜ?zDĜ?z?zDĜ?zDĜ?zDĜDĜ?z?z?z?zDĜDĜ?zDĜDĜDĜ?z?z?zDĜDĜDĜ?z?zDĜDĜ?z?zDĜ?zDĜDĜ?z?z?z?zDĜ?zDĜDĜDĜ?zDĜDĜ?zDĜDĜDĜDĜDĜ?z?zDĜDĜ?zDĜDĜ?z?z?zDĜ?z?zDĜDĜ?zDĜDĜDĜ?z?zDĜDĜ?z?z?zDĜ?z?z?z?zDĜ?z?z?z?zDĜ?zDĜDĜ?z?z?zDĜ?z?z?zDĜDĜ?z?zDĜDĜ?zDĜ?z?zDĜDĜ?zDĜ?z?z?zDĜDĜ?z?z?zDĜ?zDĜDĜDĜ?zDĜDĜ?zDĜDĜ?zDĜDĜ?zDĜDĜDĜ?z?zDĜDĜDĜ?zDĜDĜDĜDĜDĜDĜ?z?z?z?zDĜ?z?z?zDĜDĜ?zDĜDĜDĜDĜDĜ?z?z?zDĜDĜ?*ZZ9ץ?O7s ųп/+ տ%f[_ ?*Z?[njtxޚ?Vgۿ<'U__ ꫿8枿~ж?x?и:U< c$~ж)Omp-b? _Z[+?X$=\ 5̧7?|l"pKE0ykj @c?O7sê\/? hE͉Cx6dNҿQ3n?qǍ1?vU?G |?:4݅ *P﻽. c$o)?s"9N( ųп)ȵc^E?:4݅}Օ?҇?zT bI:{a? `b@D$ʯI?q翰6dNҿ}d꿚8枿p-b?rC}Օ迣.I gQO8枿-=~ж^``Ĉ t#򽿱5W^?=~⤿p-b3?cZsQ3n?}Ex/oP.I gQO?и:U<mN(?f@Hy?G |?bIҿykj @c+ տœ8?4ѝ?rC}d꿑dEoP?f@?5u?' }d?Vgۿ .^?}Ex?q?:Uk1Qlg8?.[?OP@T bk1Qlg?rC?<?:4݅?5s}?U 0? 2οpKE0 c$%f[?rC?Vg?=-L?$v?RKZ  *R?<. 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tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch tch ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg ltg 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tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 tc^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 ldl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 hdl^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 tch^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 ltg^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 glu^2 age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex age:sex 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ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu ltg:glu 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 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99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 data.frameiwrlars/man/0000755000723300071640000000000010726361617013247 5ustar brenardMultBildiwrlars/man/lars-internal.Rd0000644000723300071640000000157510555652242016316 0ustar brenardMultBild\name{lars-internal} \title{Internal lars functions} \alias{backsolvet} \alias{cv.folds} \alias{delcol} \alias{downdateR} \alias{error.bars} \alias{nnls.lars} \alias{plotCVLars} \alias{print.lars} \alias{updateR} \alias{.First.lib} \description{Internal lars functions} \usage{ backsolvet(r,x,k = ncol(r)) cv.folds(n, folds = 10) delcol(r, z, k = p) downdateR (R, k = p) error.bars(x, upper, lower, width = 0.02, ...) nnls.lars(active, Sign, R, beta, Gram, eps = 1e-10, trace = FALSE, use.Gram = TRUE) plotCVLars(cv.lars.object, se = TRUE) updateR(xnew, R = NULL, xold, eps = .Machine$double.eps, Gram = FALSE) .First.lib(lib, pkg) } \author{Trevor Hastie} \details{ These are not to be called by the user. \code{backsolvet} is included to make the R code compatible with the Splus code, since \code{backsolve} in R has a \code{transpose=TRUE} option already. } \keyword{internal} iwrlars/man/plot.lars.Rd0000644000723300071640000000363210555652242015455 0ustar brenardMultBild\name{plot.lars} \alias{plot.lars} \title{Plot method for lars objects} \description{ Produce a plot of a lars fit. The default is a complete coefficient path. } \usage{ plot.lars(x, xvar= c("norm", "df", "arc.length"), breaks = TRUE, plottype = c("coefficients", "Cp"), omit.zeros = TRUE, eps = 1e-10, ...) } \arguments{ \item{x}{lars object} \item{xvar}{The type of x variable against which to plot. \code{xvar=norm} plots against the L1 norm of the coefficient vector (default). \code{xvar=df} plots against the step number (which is essentially degrees of freedom for LAR; not for LASSO or Forward Stagewise). \code{xvar=arc.length} plots against the arc.length of the fitted vector; this is useful for a LAR object, because the L1 norm of its coefficient vector need not be monotone in the steps.} \item{breaks}{If \code{TRUE}, then vertical lines are drawn at each break point in the piecewise linear coefficient paths} \item{plottype}{Either \code{coefficients} (default) or \code{Cp}. The coefficient plot shows the path of each coefficient as a function of the norm fraction or Df. The Cp plot shows the Cp curve.} \item{omit.zeros}{When the number of variables is much greater than the number of observations, many coefficients will never be nonzero; this logical (default \code{TRUE}) avoids plotting these zero coefficents} \item{eps}{Definition of zero above, default is \code{1e-10}} \item{\dots}{Additonal arguments for generic plot. Can be used to set xlims, change colors, line widths, etc} } \value{NULL} \references{ Efron, Hastie, Johnstone and Tibshirani (2003) "Least Angle Regression" (with discussion) \emph{Annals of Statistics}; see also \url{http://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.ps} } \author{Trevor Hastie} \examples{ data(diabetes) attach(diabetes) object <- lars(x,y) plot(object) detach(diabetes) } \keyword{hplot} \keyword{methods} iwrlars/man/diabetes.Rd0000644000723300071640000000146010555652242015314 0ustar brenardMultBild\name{diabetes} \alias{diabetes} \title{Blood and other measurements in diabetics} \description{ The \code{diabetes} data frame has 442 rows and 3 columns. These are the data used in the Efron et al "Least Angle Regression" paper. } \format{ This data frame contains the following columns: \describe{ \item{x}{a matrix with 10 columns} \item{y}{a numeric vector} \item{x2}{a matrix with 64 columns} } } \details{ The x matrix has been standardized to have unit L2 norm in each column and zero mean. The matrix x2 consists of x plus certain interactions. } \source{ \url{http://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.ps} } \references{ Efron, Hastie, Johnstone and Tibshirani (2003) "Least Angle Regression" (with discussion) \emph{Annals of Statistics} } \keyword{datasets} iwrlars/man/lars.Rd0000644000723300071640000000552510555652242014503 0ustar brenardMultBild\name{lars} \alias{lars} \title{ Fits Least Angle Regression, Lasso and Infinitesimal Forward Stagewise regression models } \description{ These are all variants of Lasso, and provide the entire sequence of coefficients and fits, starting from zero, to the least squares fit. } \usage{ lars(x, y, type = c("lasso", "plasso", "lar", "forward.stagewise"), trace = FALSE, Gram, eps = .Machine$double.eps, max.steps, use.Gram = TRUE) } \arguments{ \item{x}{ matrix of predictors } \item{y}{ response } \item{type}{ One of "lasso", "plasso", "lar" or "forward.stagewise". The names can be abbreviated to any unique substring. Default is "lasso". } \item{trace}{ If TRUE, lars prints out its progress } \item{Gram}{ The X'X matrix; useful for repeated runs (bootstrap) where a large X'X stays the same. } \item{eps}{ An effective zero } \item{max.steps}{ Limit the number of steps taken; the default is \code{8 * min(m, n-1)}, with m the number of variables, and n the number of samples. For \code{type="lar"}, the maximum number of steps is \code{min(m,n-1)}. For \code{type="lasso"}, \code{type="plasso"} and especially \code{type="forward.stagewise"}, there can be many more terms, because although no more than \code{min(m,n-1)} variables can be active during any step, variables are frequently droppped and added as the algorithm proceeds. Although the default usually guarantees that the algorithm has proceeded to the saturated fit, users should check. } \item{use.Gram}{ When the number m of variables is very large, i.e. larger than N, then you may not want LARS to precompute the Gram matrix. Default is use.Gram=TRUE }} \value{ A "lars" object is returned, for which print, plot and predict methods exist. } \details{ LARS is described in detail in Efron, Hastie, Johnstone and Tibshirani (2002). With the "lasso" option, it computes the complete lasso solution simultaneously for ALL values of the shrinkage parameter in the same computational cost as a least squares fit. The "plasso" option computes the lasso solution under nonnegativity constraints, see section 3.4 of [Efron et al., 2003]. } \references{ Efron, Hastie, Johnstone and Tibshirani (2003) "Least Angle Regression" (with discussion) \emph{Annals of Statistics}; see also \url{http://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.ps}. Hastie, Tibshirani and Friedman (2002) Elements of Statistical Learning, Springer, NY. } \author{Brad Efron and Trevor Hastie (original package), Marc Kirchner and Bernhard Renard (plasso extension)} \seealso{ print, plot, and predict methods for lars, and cv.lars } \examples{ data(diabetes) par(mfrow=c(2,2)) attach(diabetes) object <- lars(x,y) plot(object) object2 <- lars(x,y,type="lar") plot(object2) object3 <- lars(x,y,type="for") # Can use abbreviations plot(object3) detach(diabetes) } \keyword{regression} % Converted by Sd2Rd version 1.21. iwrlars/man/cv.lars.Rd0000644000723300071640000000261310555652242015105 0ustar brenardMultBild\name{cv.lars} \alias{cv.lars} \title{Computes K-fold cross-validated error curve for lars} \description{ Computes the K-fold cross-validated mean squared prediction error for lars, lasso, or forward stagewise.} \usage{ cv.lars(x, y, K = 10, fraction = seq(from = 0, to = 1, length = 100), trace = FALSE, plot.it = TRUE, se = TRUE, ...) } %- maybe also `usage' for other objects documented here. \arguments{ \item{x}{Input to lars} \item{y}{Input to lars} \item{K}{Number of folds} \item{fraction}{Abscissa values at which CV curve should be computed, as a fraction of the saturated |beta|. Default is \code{seq(from = 0, to = 1, length = 100)}} \item{trace}{Show computations?} \item{plot.it}{Plot it?} \item{se}{Include standard error bands?} \item{\dots}{Additional arguments to \code{lars}} } \value{ Invisibly returns a list with components (which can be plotted using \code{plotCVlars}) \item{fraction}{As above} \item{cv}{The CV curve at each value of fraction} \item{cv.error}{The standard error of the CV curve} } \references{ Efron, Hastie, Johnstone and Tibshirani (2003) "Least Angle Regression" (with discussion) \emph{Annals of Statistics}; see also \url{http://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.ps}. } \author{Trevor Hastie} \examples{ data(diabetes) attach(diabetes) cv.lars(x2,y,trace=TRUE,max.steps=80) detach(diabetes) } \keyword{regression} iwrlars/man/predict.lars.Rd0000644000723300071640000000477610555652242016143 0ustar brenardMultBild\name{predict.lars} \alias{predict.lars} \alias{coef.lars} \title{ Make predictions or extract coefficients from a fitted lars model } \description{ While lars() produces the entire path of solutions, predict.lars allows one to extract a prediction at a particular point along the path. } \usage{ predict.lars(object, newx, s, type = c("fit", "coefficients"), mode = c("step", "fraction", "norm"), ...) coef.lars(object, ...) } \arguments{ \item{object}{ A fitted lars object } \item{newx}{ If type="fit", then newx should be the x values at which the fit is required. If type="coefficients", then newx can be omitted. } \item{s}{ a value, or vector of values, indexing the path. Its values depends on the mode= argument. By default (mode="step"), s should take on values between 0 and p (e.g., a step of 1.3 means .3 of the way between step 1 and 2.) } \item{type}{ If type="fit", predict returns the fitted values. If type="coefficients", predict returns the coefficients. Abbreviations allowed. } \item{mode}{ Mode="step" means the s= argument indexes the lars step number, and the coefficients will be returned corresponding to the values corresponding to step s. If mode="fraction", then s should be a number between 0 and 1, and it refers to the ratio of the L1 norm of the coefficient vector, relative to the norm at the full LS solution. Mode="norm" means s refers to the L1 norm of the coefficient vector. Abbreviations allowed. } \item{\dots}{Any arguments for \code{predict.lars} should work for \code{coef.lars}} } \value{ Either a vector/matrix of fitted values, or a vector/matrix of coefficients. } \details{ LARS is described in detail in Efron, Hastie, Johnstone and Tibshirani (2002). With the "lasso" option, it computes the complete lasso solution simultaneously for ALL values of the shrinkage parameter in the same computational cost as a least squares fit. } \references{ Efron, Hastie, Johnstone and Tibshirani (2002) Hastie, Tibshirani and Friedman (2002) Elements of Statistical Learning, Springer, NY. } \author{Trevor Hastie} \seealso{ print, plot, lars, cv.lars } \examples{ data(diabetes) attach(diabetes) object <- lars(x,y,type="lasso") ### make predictions at the values in x, at each of the ### steps produced in object fits <- predict.lars(object, x, type="fit") ### extract the coefficient vector with L1 norm=4.1 coef4.1 <- coef(object, s=4.1, mode="norm") # or coef4.1 <- predict(object, s=4.1, type="coef", mode="norm") detach(diabetes) } \keyword{regression} \keyword{methods} % Converted by Sd2Rd version 1.21. iwrlars/DESCRIPTION0000644000723300071640000000163610726361617014210 0ustar brenardMultBildPackage: iwrlars Version: 0.9-5 Date: 2007-01-11 Title: Least Angle Regression, Lasso, positive Lasso and Forward Stagewise Author: Trevor Hastie , Brad Efron , Marc Kirchner , Bernhard Renard Maintainer: Marc Kirchner Depends: R Description: Efficient procedures for fitting an entire lasso sequence with the cost of a single least squares fit. Least angle regression and infinitessimal forward stagewise regression are related to the lasso, as described in the paper below. This is a modified version of the original lars package by Hastie and Efron, providing a LARS modification for non-negative lasso. License: GPL version 2 or newer URL: http://www-stat.stanford.edu/~hastie/Papers/#LARS Packaged: Sat Dec 8 00:21:35 2007; brenard iwrlars/INDEX0000644000723300071640000000044610555652242013267 0ustar brenardMultBild lars Compute the LAR, lasso, positive lasso or forward stagewise coefficient paths plot.lars Plot the coefficient paths predict.lars Predict from a lars model cv.lars Produce the K-fold cross-validation curve for lars coef.lars Extract coefficients from a lars model