doBy/0000755000176200001440000000000015030323372011145 5ustar liggesusersdoBy/tests/0000755000176200001440000000000014551451413012314 5ustar liggesusersdoBy/tests/testthat/0000755000176200001440000000000015030323372014147 5ustar liggesusersdoBy/tests/testthat/test-vtools.R0000644000176200001440000000220715024546751016611 0ustar liggesuserstest_that("vparse handles different input formats correctly", { expect_equal(vparse(Treatment, Type), c("Treatment", "Type")) expect_equal(vparse(c("Treatment", "Type")), c("Treatment", "Type")) expect_equal(vparse(~Treatment + Type), c("Treatment", "Type")) }) test_that("vselect selects correct columns from CO2", { data(CO2) selected <- vselect(CO2, Treatment, Type) expect_true(all(c("Treatment", "Type") %in% names(selected))) expect_equal(ncol(selected), 2) }) test_that("vcheck confirms variables exist", { data(CO2) expect_true(vcheck(CO2, Treatment, Type)) expect_true(vcheck(CO2, ~Treatment + Type)) }) test_that("vcheck throws error for missing variables", { data(CO2) expect_error(vcheck(CO2, Nonexistent), "Missing variables") }) test_that("vmap applies function to variable names", { result <- vmap(~Treatment + Type, toupper) expect_equal(result, list("TREATMENT", "TYPE")) }) test_that("vrename renames columns correctly", { data(CO2) renamed <- vrename(CO2, c(Treatment = "Trt", Type = "Group")) expect_true(all(c("Trt", "Group") %in% names(renamed))) expect_false("Treatment" %in% names(renamed)) }) doBy/tests/testthat/test-split_by.R0000644000176200001440000000114115024542242017073 0ustar liggesuserstest_that("split_by works with different input types", { data(CO2) # unquoted out1 <- split_by(CO2, Treatment) expect_s3_class(out1, "splitByData") expect_true(all(sapply(out1, function(df) !("Treatment" %in% names(df))))) # formula out2 <- split_by(CO2, ~Treatment + Type) expect_equal(length(out2), length(unique(paste(CO2$Treatment, CO2$Type)))) # character vector out3 <- split_by(CO2, c("Treatment", "Type")) expect_equal(length(out3), length(out2)) # do not omit group vars out4 <- split_by(CO2, Treatment, omit = FALSE) expect_true("Treatment" %in% names(out4[[1]])) }) doBy/tests/testthat/test-restrict.R0000644000176200001440000000137314551451413017117 0ustar liggesuserstest_that("section_fun", { g0 = function(x,y,z) { x + y + z + u } g1 <- section_fun_sub(g0, list(z=100)) g2 <- section_fun_sub(g1, list(y=10)) g3 <- section_fun_sub(g2, list(x=1)) u <- 1000 expect_equal(g0(x=1, y=10, z=100), 1111) expect_equal(g1(x=1, y=10), 1111) expect_equal(g2(x=1), 1111) expect_equal(g3(), 1111) f0 = function(x,y,z) { x + y + z + u } ## f1 <- section_fun(f0, list(z=200)) ## f2 <- section_fun(f1, list(y=20)) ## f3 <- section_fun(f2, list(x=2)) ## u <- 2000 ## expect_equal(f0(x=2, y=20, z=200), 2222) ## expect_equal(f1(x=2, y=20), 2222) ## expect_equal(f2(x=2), 2222) ## expect_equal(f3(), 2222) }) doBy/tests/testthat.R0000755000176200001440000000006614551451413014304 0ustar liggesuserslibrary(testthat) 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liggesusersdoBy/R/formula_ops.R0000644000176200001440000001642114634270266014037 0ustar liggesusers## Move to doBy ## ## https://stackoverflow.com/questions/70735907/how-to-evaluate-in-a-formula-in-r ##' @title Formula operations and coercion. ##' ##' @description Formula operations and coercion as a supplement to `update.formula()` ##' ##' @name formula_ops ##' ##' @param chr1 Character vector to be coerced to formulas. ##' @param frm1,frm2 Formulas to be coerced to character vectors. ##' @param y Response ##' @param terms Character string. ##' @param op Either "+" (default) or "-". ##' @param object Character vector or formula. ##' @param noint Boolean. ##' @param string Boolean. ##' @param n Positive integer. ##' ##' @examples ##' ##' formula_poly("z", 2) ##' formula_poly("z", 2, noint=TRUE) ##' ##' as_rhs_chr(c("a", "b", "z")) ##' as_rhs_chr(c("a*b", "z")) ##' ##' as_rhs_chr(y~a+b+z) ##' as_rhs_chr(y~a+b+z, string=TRUE) ##' as_rhs_chr(y~a+b+z) ##' as_rhs_chr(y~a*b+z) ##' as_rhs_chr(y~a*b+z, string=TRUE) ##' ##' as_lhs_chr(y~a*b+z) ##' as_lhs_chr(log(y)~a*b+z) ## Not what one might expect ##' as_lhs_chr(cbind(y, u)~a*b+z) ## Not what one might expect ##' ##' formula_chr_to_form(c("a*b", "z")) ##' formula_chr_to_form(c("a*b", "z"), "y") ##' formula_chr_to_form(c("a*b", "z"), "log(y)") ##' ##' formula_add(y~a*b+z, ~-1) ##' formula_add(y~a*b+z, ~a:b) ##' ##' formula_add_str(y~x1 + x2, "x3") ##' formula_add_str(y~x1 + x2, "x1") ##' formula_add_str(y~x1 + x2, "x1", op="-") ##' ##' @rdname formula_ops #' @export formula_add_str <- function(frm1, terms, op="+"){ ## FIXME: Need to handle case where frm1 is a rhs-formula ch <- as.character(frm1) ch[3] <- paste0(ch[3], op, terms) frm12 <- as.formula(paste(ch[2], ch[1], ch[3])) frm1 <- update(frm1, frm12) return(frm1) } ##' @rdname formula_ops ##' @export formula_add <- function(frm1, frm2){ stopifnot_formula(frm1) stopifnot_formula(frm2) ## Right-hand-side frm1_rhs <- as_rhs_chr(frm1) frm2_rhs <- as_rhs_chr(frm2) o_rhs <- c(frm1_rhs, frm2_rhs) o_rhs <- o_rhs|> paste0(collapse="+") ## str(o_rhs) ## Remove redunancies o_rhs <- simplify_rhs(o_rhs) ## Left-hand-side frm1_lhs <- as_lhs_chr(frm1) frm2_lhs <- as_lhs_chr(frm2) if ((length(frm1_lhs) > 0) && (length(frm2_lhs) > 0)) stop("Can not handle two left-sides\n") o_lhs <- c(frm1_lhs, frm2_lhs) formula_chr_to_form(o_rhs, o_lhs) } ##' @rdname formula_ops ##' @export formula_poly <- function(chr1, n, noint=FALSE, y=NULL){ if (n > 1){ b <- paste0(chr1, "^", 2:n) o <- paste0("I(", b, ")", collapse = "+") o <- paste0(chr1, "+", o) } else { o <- chr1 } if (noint){ o <- paste0(y, "-1 +", o) } formula(paste(y, "~", o)) } ##' @rdname formula_ops ##' @export formula_nth <- function(frm1, n){ fs <- as_rhs_chr(frm1, string=TRUE) fs2 <- paste0("(", fs, ")^", n) o <- as_rhs_frm(fs2) o } ##' @rdname formula_ops ##' @export formula_to_interaction_matrix <- function(frm1){ ## aa <- as_rhs_chr(frm1) |> strsplit(":") aa <- terms_labels(frm1) aa <- aa |> strsplit(":") nms <- unique(unlist(aa)) mm <- matrix(0, nrow=length(nms), ncol=length(nms), dimnames=list(nms, nms)) for(i in 1:length(aa)){ g <- aa[[i]] mm[g, g] <- 1 } mm } ##' @rdname formula_ops ##' @param rhs,lhs right-hand-side and left-hand-side for formula (as characters) ##' @export formula_chr_to_form <- function(rhs, lhs=character(0)){ rhs <- to_str(rhs) o <- paste0(lhs, "~", rhs) as.formula(o) } ## UTILITIES ##' @rdname formula_ops ##' @param collapse Character to use as separator. ##' @export to_str <- function(chr1, collapse="+"){ paste0(chr1, collapse=collapse) } ##' @rdname formula_ops ##' @export terms_labels <- function(frm1){ terms(frm1) |> attr("term.labels") |> sort() } ##' @rdname formula_ops ##' @export simplify_rhs <- function(object){ UseMethod("simplify_rhs") } ##' @rdname formula_ops ##' @export simplify_rhs.formula <- function(object){ l <- terms_labels(object) to_str(l) |> as_rhs_frm() } ##' @rdname formula_ops ##' @export simplify_rhs.character <- function(object){ o <- as_rhs_frm(object) |> terms_labels() |> to_str() o } ## Bad name formula_rhs_to_chr <- function(frm1, string=TRUE){ frm1 <- terms_labels(frm1) if (length(frm1) == 0) frm1 <- "-1" if (string) frm1 <- frm1 |> to_str() frm1 } is_rhsf <- function(object){ is(object, "formula") && ((object |> terms() |> attr("response")) == 0) } stopifnot_rhsf <- function(object){ if (!is_rhsf(object)) stop("argument is not a right-hand-sided formula\n") } stopifnot_formula <- function(a){ if (!is(a, "formula")) stop("argument is not a formula") } stopifnot_chr <- function(a){ if (!is(a, "character")) stop("argument is not a character") } ## RETURNING FORMULAS ##' @export as_rhs_frm.character <- function(object){ object <- object |> to_str() paste0("~", object) |> as.formula() } ##' @export as_rhs_frm.formula <- function(frm1){ formula(delete.response(terms(frm1))) } ##' @export as_lhs_frm.character <- function(object){ stopifnot_chr(object) paste0(object, "~ 1") |> as.formula() } formula_lhs_to_chr <- function(frm1){ if (is(frm1, "formula")){ r <- terms(frm1) |> attr("response") if (r > 0) frm1 <- (terms(frm1) |> attr("variables"))[[r+1]] |> as.character() else frm1 <- character(0) } frm1 } ##' @export as_lhs_frm.formula <- function(frm1){ o <- frm1 |> formula_lhs_to_chr() as.formula(paste0(o, "~1")) } ## RETURNING CHARACTERS ##' @export as_rhs_chr.character <- function(object, string=TRUE){ ob <- object |> to_str(collapse = "") rev(strsplit(ob,"\\s~\\s")[[1]])[1] } ##' @export as_rhs_chr.formula <- function(object, string=TRUE){ formula_rhs_to_chr(object, string=string) } ##' @export as_lhs_chr.character <- function(object, string=TRUE){ object2 <- strsplit(object,"\\s~\\s")[[1]] if (length(object2) == 2) object2[1] else character(0) } ##' @export as_lhs_chr.formula <- function(object, string=FALSE){ r <- terms(object) |> attr("response") if (r > 0) object <- (terms(object) |> attr("variables"))[[r+1]] |> as.character() else object <- character(0) object } ## EXPORTED ##' @rdname formula_ops ##' @export as_rhs_frm <- function(object){ UseMethod("as_rhs_frm") } ##' @rdname formula_ops ##' @export as_lhs_frm <- function(object){ UseMethod("as_lhs_frm") } ##' @rdname formula_ops ##' @export as_rhs_chr <- function(object, string=FALSE){ UseMethod("as_rhs_chr") } ##' @rdname formula_ops ##' @export as_lhs_chr <- function(object, string=FALSE){ UseMethod("as_lhs_chr") } ##' @rdname formula_ops ##' @param list_of_formulas list of formulas ##' @export unique_formula <- function(list_of_formulas){ l2 <- lapply(list_of_formulas, function(x){ environment(x) <- NULL x } ) o <- list_of_formulas[!duplicated(l2)] return(o) } ## ##' @export ## formula_to_rhs <- function(frm1){ ## terms(frm1) |> delete.response() |> formula() ## } ## FIXME: REPLACE ## formula_chr_to_rhs <- function(chr1){ ## o <- to_str(chr1) ## as.formula(paste("~", o)) ## } doBy/R/linear_modelling.R0000644000176200001440000001203115023576671015010 0ustar liggesusers ##' @title Add predicted values of different types to dataframe ##' ##' @param data dataframe or tibble ##' @param model model object ##' @param var name of new variable in dataframe / tibble ##' @param type type of predicted value ##' @param transformation A possible transformation of predicted variable, e.g. reciprocal(), log() etc ##' @return dataframe / tibble ##' @author Søren Højsgaard ##' @examples ##' data(cars) ##' lm1 <- lm(dist ~ speed + I(speed^2), data=cars) ##' lm1 |> response() |> head() ##' cars <- cars |> add_pred(lm1) ##' cars |> head() ##' cars <- cars |> add_resid(lm1) ##' cars ##' ##' @export add_pred <- function (data, model, var = "pred", type = NULL, transformation=NULL) { pred2 <- function (model, data, type = NULL) { if (is.null(type)) { stats::predict(model, data) } else { stats::predict(model, data, type = type) } } pp <- pred2(model, data, type = type) if (!is.null(transformation)){ pp <- transformation(pp) } data[[var]] <- pp data } ##' @title Reciprocal function ##' @description A function returning the reciprocal of its argument ##' @param x An R object for which 1/x makes sense ##' @author Søren Højsgaard ##' @export reciprocal <- function(x){ 1/x } ##' @title Power function ##' @description A function returning x raised to the power p. ##' @param x An object for which x^p makes sense ##' @param p A power ##' @author Søren Højsgaard ##' @export pow <- function(x, p){ x^p } ##' @title Add residuals of different types to dataframe ##' ##' @param data dataframe or tibble ##' @param model model object ##' @param var name of new variable in dataframe / tibble ##' @param type type of residual value ##' @return dataframe / tibble ##' @author Søren Højsgaard ##' @examples ##' data(cars) ##' lm1 <- lm(dist ~ speed + I(speed^2), data=cars) ##' lm1 |> response() |> head() ##' cars <- cars |> add_pred(lm1) ##' cars |> head() ##' cars <- cars |> add_resid(lm1) ##' cars ##' ##' @export add_resid <- function (data, model, var = "resid", type) { resid2 <- function(model, type){ UseMethod("resid2") } resid2.lm <- function(model, type=c("working", "response", "deviance", "pearson", "partial", "rstandard", "rstudent")){ type <- match.arg(type) if (identical(type, "rstandard")){ return(stats::rstandard(model)) } if (identical(type, "rstudent")){ return(stats::rstudent(model)) } return(stats::residuals(model, type=type)) } resid2.merMod <- function(model, type=c("deviance", "response")){ return(residuals(model, type=type)) } if (missing(type)) type="working" data[[var]] <- resid2(model, type) data } ##' @title Get response variable from model ##' @param object lm or glm object ## ' @param data dataframe or tibble ## ' @param model model object ## ' @param var name of new variable in dataframe / tibble ## ' @param type type of residual value ##' @examples ##' data(cars) ##' lm1 <- lm(dist ~ speed + I(speed^2), data=cars) ##' lm1 |> response() |> head() ##' cars <- cars |> add_pred(lm1) ##' cars |> head() ##' cars <- cars |> add_resid(lm1) ##' cars ##' @export response <- function(object){ is_lm <- function(object) { identical(class(object), "lm") } is_glm <- function(object) { cls <- class(object) (all(c("lm", "glm") %in% cls)) && (length(cls) == 2) } obs <- function(object){ UseMethod("obs") } obs.lm <- function(object) { obs <- model.response(model.frame(object)) if (is_glm(object)){ wgt <- unname(model.weights(model.frame(object))) if (!is.null(wgt)) { obs <- obs * wgt } } return(obs) } return(obs(object)) } ##' @title Add interaction columns to data frame ##' @param .data dataframe ##' @param .formula right hand sided formula ##' @return dataframe ##' @author Søren Højsgaard ##' @export add_int <- function(.data, .formula) { ff <- rhsf2list(.formula) lapply(ff, function(g){ if (length(g)>1){ var <- paste(g, collapse="_") ia <- apply(.data[,g], 1, function(r) paste(r, collapse="_")) .data[[var]] <<- ia }}) .data } rhsf2list <- function (.formula) { if (is.character(.formula)) return(list(.formula)) if (is.numeric(.formula)) return(lapply(list(.formula), "as.character")) if (is.list(.formula)) return(lapply(.formula, "as.character")) .formula0 <- .formula[[length(.formula)]] .formula1 <- unlist(strsplit(paste(deparse(.formula0), collapse = ""), " *\\+ *")) .formula2 <- unlist(lapply(.formula1, strsplit, " *\\* *| *: *| *\\| *"), recursive = FALSE) .formula2 } doBy/R/a_handle_dots_and_symbols.R0000644000176200001440000000163515023770054016661 0ustar liggesuserstochar <- function(...){ dots <- match.call(expand.dots = FALSE)$... dots2char(dots) } dots2char <- function(dots){ ## dots <- match.call(expand.dots = FALSE)$... dots1 <- dots[[1]] if (inherits(dots1, "call")){ dots1 <- eval(dots1) } if (inherits(dots1, c("formula", "call"))){ varRHS <- all.vars(dots1[[2]]) return(varRHS) } if (is.character(dots1)){ varRHS <- dots1 return(varRHS) } varRHS <- lapply(seq_along(dots), function(i) { y <- if (is.symbol(dots[[i]])) { deparse(dots[[i]]) } else { dots[[i]] } return(y) }) varRHS <- unlist(varRHS) return(varRHS) } doBy/R/microbenchmark.R0000644000176200001440000000676614551451413014501 0ustar liggesusers#' @title Fast summary of microbenchmark object #' #' @description Fast summary of microbenchmark object. The default #' summary method from the microbenchmark package is fairly slow #' in producing a summary (due to a call to a function from the #' multcomp package.) #' #' @name mb_summary #' #' @param object A microbenchmark object #' @param unit The time unit to be used #' @param add.unit Should time unit be added as column to resulting #' dataframe. #' @param ... Additional arguments; currently not used. NULL #' @rdname mb_summary #' @export mb_summary <- function (object, unit, add.unit=TRUE, ...) { if (missing(unit)) { unit <- if (!is.null(attr(object, "unit"))) attr(object, "unit") else getOption("microbenchmark.unit", "t") } if (unit != "relative") object$time <- .convert_to_unit(object$time, unit) res <- aggregate(time ~ expr, object, function(z) { tmp <- c(stats::fivenum(z), mean(z), length(z)) tmp[c(1, 2, 6, 3, 4, 5, 7)] }) res <- cbind(res$expr, as.data.frame(res$time)) colnames(res) <- c("expr", "min", "lq", "mean", "median", "uq", "max", "neval") if (unit == "relative") { min <- res[which.min(res$median), , drop = FALSE] min$neval <- 1 res[-1] <- res[-1]/as.list(min[-1]) attr(res, "unit") <- "relative" } else { attr(res, "unit") <- attr(object$time, "unit") } ## if (requireNamespace("multcomp", quietly = TRUE) && nrow(res) > ## 1 && all(res["neval"] > 1)) { ## tryCatch({ ## ops <- options(warn = -1) ## mdl <- lm(time ~ expr, object) ## comp <- multcomp::glht(mdl, multcomp::mcp(expr = "Tukey")) ## res$cld <- multcomp::cld(comp)$mcletters$monospacedLetters ## }, error = function(e) FALSE, finally = options(ops)) ## } if (add.unit) res$unit <- attr(res, "unit") res } .convert_to_unit <- function (x, unit = c("ns", "us", "ms", "s", "t", "hz", "khz", "mhz", "eps", "f")) { unit <- match.arg(unit) switch(unit, t = unit <- sprintf("%ss", .find_prefix(x * 1e-09, minexp = -9, maxexp = 0, mu = FALSE)), f = unit <- sprintf("%shz", .find_prefix(1e+09/x, minexp = 0, maxexp = 6, mu = FALSE))) unit <- tolower(unit) switch(unit, ns = { attr(x, "unit") <- "nanoseconds" unclass(x) }, us = { attr(x, "unit") <- "microseconds" unclass(x/1000) }, ms = { attr(x, "unit") <- "milliseconds" unclass(x/1e+06) }, s = { attr(x, "unit") <- "seconds" unclass(x/1e+09) }, eps = { attr(x, "unit") <- "evaluations per second" unclass(1e+09/x) }, hz = { attr(x, "unit") <- "hertz" unclass(1e+09/x) }, khz = { attr(x, "unit") <- "kilohertz" unclass(1e+06/x) }, mhz = { attr(x, "unit") <- "megahertz" unclass(1000/x) }, stop("Unknown unit '", unit, "'.")) } .find_prefix <- function (x, f = min, minexp = -Inf, maxexp = Inf, mu = TRUE) { prefixes <- c("y", "z", "a", "f", "p", "n", "u", "m", "", "k", "M", "G", "T", "P", "E", "Z", "Y") if (mu) prefixes[7] <- "mu" if (is.numeric(minexp)) minexp <- floor(minexp/3) if (is.numeric(minexp)) maxexp <- floor(maxexp/3) e3 <- floor(log10(f(x))/3) e3 <- max(e3, minexp, -8) e3 <- min(e3, maxexp, 8) prefixes[e3 + 9] } #' @rdname mb_summary #' @export summary_mb <- mb_summary doBy/R/linest_compute.R0000644000176200001440000002365714744034254014551 0ustar liggesusers#' @title Compute linear estimates #' #' @description Compute linear estimates, i.e. `L %*% beta` for a range of models. One example of #' linear estimates is population means (also known as LSMEANS). #' #' @name linest #' #' @aliases linest linest.lm linest.glm linest.geeglm linest.lmerMod #' linest.merMod linest.default #' #' @param object Model object #' @param L Either \code{NULL} or a matrix with p columns where p is #' the number of parameters in the systematic effects in the #' model. If \code{NULL} then \code{L} is taken to be the p times #' p identity matrix #' @param level The level of the (asymptotic) confidence interval. #' @param confint Should confidence interval appear in output. #' @param \dots Additional arguments; currently not used. #' @return A dataframe with results from computing the contrasts. #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @seealso \code{\link{LSmeans}}, \code{\link{LE_matrix}} #' @keywords utilities #' #' @examples #' #' ## Make balanced dataset #' dat.bal <- expand.grid(list(AA=factor(1:2), BB=factor(1:3), CC=factor(1:3))) #' dat.bal$y <- rnorm(nrow(dat.bal)) #' #' ## Make unbalanced dataset #' # 'BB' is nested within 'CC' so BB=1 is only found when CC=1 #' # and BB=2,3 are found in each CC=2,3,4 #' dat.nst <- dat.bal #' dat.nst$CC <-factor(c(1,1,2,2,2,2,1,1,3,3,3,3,1,1,4,4,4,4)) #' #' mod.bal <- lm(y ~ AA + BB * CC, data=dat.bal) #' mod.nst <- lm(y ~ AA + BB : CC, data=dat.nst) #' #' L <- LE_matrix(mod.nst, effect=c("BB", "CC")) #' linest( mod.nst, L ) #' #' @export linest #' @export linest <- function(object, L=NULL, level=0.95, ...){ UseMethod("linest") } #' @export linest.lm <- function(object, L=NULL, level=0.95, ...){ bhat <- coef(object) L <- .constructL(L, bhat) is.est <- is_estimable(L, null_basis(model.matrix(object))) VV0 <- vcov(object, complete=FALSE) ddf.vec <- rep(object$df.residual, nrow( L )) res <- .getLb2( L, bhat, VV0, ddf.vec, is.est) p.value <- 2 * pt(abs(res[,"statistic"]), df=res[,"df"], lower.tail=FALSE) res <- as.data.frame(cbind(res, p.value)) .finalize_linest(res, L, level) } #' @export linest.glm <- function(object, L=NULL, level=0.95, ...){ bhat <- coef(object) L <- .constructL(L, bhat) is.est <- is_estimable(L, null_basis(model.matrix(object))) VV0 <- vcov(object, complete=FALSE) ddf.vec <- rep(object$df.residual, nrow( L )) res <- .getLb2(L, bhat, VV0, ddf.vec, is.est) if( family(object)[1] %in% c("poisson", "binomial", "Gamma", "quasipoisson", "quasibinomial")){ p.value <- 2*pnorm(abs(res[,"statistic"]), lower.tail=FALSE) res <- res[, -4] # NO df's ## qq <- qnorm(1-(1-level)/2) } else { p.value <- 2*pt(abs(res[,"statistic"]), df=res[,"df"], lower.tail=FALSE) ## qq <- qt(1-(1-level)/2, df=res[,"df"]) } res <- as.data.frame(cbind(res, p.value)) .finalize_linest(res, L, level) } #' @export linest.geeglm <- function(object, L=NULL, level=0.95, ...){ bhat <- coef(object) L <- .constructL(L, bhat) is.est <- is_estimable(L, null_basis(model.matrix(object))) VV0 <- summary(object)$cov.scaled ddf.vec <- rep(1, nrow(L)) res <- .getLb2( L, bhat, VV0, ddf.vec, is.est) res <- res[, 1:3] p.value <- 2 * pnorm(abs(res[,"statistic"]), lower.tail=FALSE) res <- as.data.frame(cbind(res, p.value)) .finalize_linest(res, L, level) } #' @export linest.lmerMod <- function(object, L=NULL, level=0.95, adjust.df=TRUE, ...){ bhat <- lme4::fixef(object) L <- .constructL(L, bhat) is.est <- is_estimable(L, null_basis(model.matrix(object))) if (adjust.df){ if (requireNamespace("pbkrtest", quietly=TRUE)){ VVu <- vcov(object, complete=FALSE) VV <- pbkrtest::vcovAdj(object) ddf.vec <- unlist(lapply(1:nrow(L), function(ii) pbkrtest::ddf_Lb(VV , L[ii,], VVu))) } else { stop("adjustment of degrees of freedom requires that 'pbkrtest' is installed") } } else { a <- logLik(object) ddf <- attributes(a)$nall - attributes(a)$df ddf.vec <- rep(ddf, length(bhat)) VV <- vcov(object, complete=FALSE) } res <- .getLb2( L, bhat, VV, ddf.vec, is.est) p.value <- 2 * pt(abs(res[,"statistic"]), df=res[,"df"], lower.tail=FALSE) res <- as.data.frame(cbind(res, p.value)) .finalize_linest(res, L, level) } #' @export linest.merMod <- function(object, L=NULL, level=0.95, ...){ cl <- match.call() cl[[1]] <- as.name("linest.lmerMod") cl$adjust.df <- FALSE eval(cl) } .finalize_linest <- function(.coef, L, level){ ## cat(".finalize_linest\n") ## print(.coef) rownames(.coef) <- NULL df <- .coef$df qq <- 1-(1-level)/2 if (!is.null(df)) { crit <- qt(qq, df=df) } else { crit <- qnorm(qq) } .coef <- cbind(.coef, cbind(lwr=.coef$estimate -crit*.coef$std.error, upr=.coef$estimate +crit*.coef$std.error)) gr <- attr(L, "grid") ## gr <- lapply(gr, as.character) ## print(gr) if (!is.null(gr)){ .coef <- cbind(gr, .coef) } ## print(.coef) ## .coef$estimate <- format(.coef$estimate, digits = 3, nsmall = 2) if (!is.null(rownames(L))) rownames(.coef) <- rownames(L) ## .coef <- as.data.frame(.coef) attr(.coef, "L") <- L #res <- list(coef=.coef, grid=attr(L, "grid"), L=L) class(.coef) <- c("linest_class", "data.frame") .coef } .getLb2 <- function(L, bhat, VV, ddf.vec, is.est, level=0.95){ off <- attr(L, "offset") ## print(L) ## print(VV) notna <- !is.na(bhat) used <- which(!is.na(bhat)) bhat.used <- bhat[used] ## L <- L[, used, drop=FALSE] res <- matrix(NA, nrow=nrow(L), ncol=3) ## print("ddddddddddddddddddddddd") ## print(bhat) ## cat("-------------\n") for (ii in 1:nrow(res)){ kk <- L[ii,] ## print(kk) ## kk <<- kk ## bhat <<- bhat zz <- kk[is.na(bhat)] ## values in kk for which beta is NA ## print(zz) if (length(zz) == 0) is.zero <- TRUE else is.zero <- max(abs(zz)) < 1e-3 ## print(is.zero) if (is.est[ii] && is.zero){ kk <- kk[used] est <- sum(kk * bhat.used) se <- sqrt(sum(kk * (VV %*% kk))) df2 <- ddf.vec[ii] res[ii,] <- c(est, se, df2) } } if (!is.null(off)) res[,1] <- res[,1] + off[[1]] colnames(res) <- c("estimate", "std.error", "df") statistic <- res[,"estimate"] / res[,"std.error"] res <- cbind(res[,1:2, drop=FALSE], statistic, df=res[,3]) rownames(res) <- NULL ##print(res) res } ## .getLb2 <- function(L, bhat, VV, ddf.vec, is.est, level=0.95){ ## off <- attr(L, "offset") ## used <- which(!is.na(bhat)) ## bhat.used <- bhat[used] ## L <- L[, used, drop=FALSE] ## res <- matrix(NA, nrow=nrow(L), ncol=3) ## for (ii in 1:nrow(res)){ ## kk <- L[ii,] ## if (is.est[ii]){ ## est <- sum(kk * bhat.used) ## se <- sqrt(sum(kk * (VV %*% kk))) ## df2 <- ddf.vec[ii] ## res[ii,] <- c(est, se, df2) ## } ## } ## if (!is.null(off)) ## res[,1] <- res[,1] + off[[1]] ## colnames(res) <- c("estimate", "std.error", "df") ## statistic <- res[,"estimate"] / res[,"std.error"] ## res <- cbind(res[,1:2, drop=FALSE], statistic, df=res[,3]) ## rownames(res) <- NULL ## ##print(res) ## res ## } .ci_fun <- function (object, parm, level = 0.95, ...) { ##object <- coef(object) cf <- object$estimate #cf <- coef(object) pnames <- 1:nrow(object) ##names(cf) if (missing(parm)) parm <- pnames else if (is.numeric(parm)) parm <- pnames[parm] a <- (1 - level)/2 a <- c(a, 1 - a) pct <- a DF <- object$df if (!is.null(DF)) fac <- t(sapply(DF, function(d) qt(a, d))) else fac <- matrix(rep(qnorm(a), length(parm)), nrow=2) ci <- array(NA, dim = c(length(parm), 2L), dimnames = list(parm, pct)) ses <- object$std.error[parm] #print(list(a=a, DF=DF, ses=ses, fac=fac, cf=cf[parm], ci=ci)) #ci[] <- cf[parm] + ses %o% fac ci[] <- cf[parm] + fac * ses ci } ### UTILITIES ### #' @export #' @rdname linest #' @param parm Specification of the parameters estimates for which #' confidence intervals are to be calculated. confint.linest_class <- function (object, parm, level = 0.95, ...) { co <- coef(object) .ci_fun(co, parm, level, ...) } .constructL <- function(L, bhat){ .defineL <- function( bhat ){ L <- diag(1, length(bhat)) rownames(L) <- names(bhat) L } if (is.null(L)) L <- .defineL(bhat) if (!is.matrix(L)) L <- matrix(L, nrow=1) L } #' @export #' @rdname linest coef.linest_class <- function (object, ...) { object$coef } #' @export print.linest_class <- function (x, ...){ ## cat("Coefficients:\n") ##printCoefmat(x$coef) old <- options("digits")$digits options("digits"=3) print.data.frame(x) options("digits"=old) return(invisible(x)) } #' @export #' @rdname linest summary.linest_class <- function (object, ...) { print(object) ## cat("Coefficients:\n") ## printCoefmat(object$coef) ## cat("\n") ## # if (!is.null(object$grid)){ ## cat("Grid:\n") ## print(object$grid) ## cat("\n") ## # } ## cat("L:\n") ## print(object$L) ## cat("\n") invisible(object) } setOldClass("linest_class") setAs("linest_class", "data.frame", function(from) { out <- as.data.frame(coef(from)) if (!is.null(from$grid)) out <- cbind(out, from$grid) attr(out, "K") <- from$K out }) doBy/R/functional_section_fun.R0000644000176200001440000002516614613655261016254 0ustar liggesusers#' @title Section a function and set default values in function #' #' @description Section a functions domain by fixing certain #' arguments of a function call. #' #' @name section_fun #' #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} based on code #' adapted from the curry package. #' #' @param fun Function to be sectioned #' @param nms Either a named list of the form name=value where each #' name is the name of an argument of the function (in which case #' `vls` is ignored) or a character vector of names of arguments. #' @param vls A vector or list of values of the arguments #' #' @param method One of the following: 1) "args" (default); based on #' substituting fixed values into the function argument list as #' default values). For backward compatibility can also be "def". #' 2) "body" for substituting fixed values into the function #' body. For backward compatibility can also be "sub". 3) "env": #' (for environment); using an auxillary argument for storing #' sectioned values. #' #' #' @param envir Environment #' @param object An object from section_fun (a scaffold object). #' #' @seealso [bquote_fun_list()] #' @details Let E be a subset of the cartesian product X x Y where X #' and Y are some sets. Consider a function f(x,y) defined on #' E. Then for any x in X, the section of E defined by x (denoted #' Ex) is the set of $y$s in Y such that (x, y) is in #' E. Correspondingly, the section of f(x,y) defined by x is the #' function $f_x$ defined on Ex given by $f_x(y)=f(x,y)$. #' #' `section_fun` is a wrapper for calling `set_default` (default #' method), `section_fun_env` or `section_fun_sub`. Notice that #' creating a sectioned function with `section_fun_sub` can be #' time consuming. #' #' @return A new function: The input function `fun` but with certain #' arguments fixed at specific values. #' #' @examples #' #' f <- function(x, y){x + y} #' #' f_ <- section_fun(f, list(y = 10), method="args") ## "def"" is default #' f_ <- section_fun(f, nms="y", vls=10, method="args") ## SAME AS ABOVE #' f_ #' f_(x=1) #' #' f_ <- section_fun(f, list(y = 10), method="body") ## #' f_ <- section_fun(f, nms="y", vls=10, method="body") ## SAME AS ABOVE #' f_ #' f_(x=1) #' #' f_ <- section_fun(f, list(y = 10), method="env") #' f_ <- section_fun(f, nms="y", vls=10, method="env") ## SAME AS ABOVE #' f_ #' f_(x=1) #' get_section(f_) #' get_fun(f_) #' #' #' ## With more complicated values: #' g <- function(A, B) { #' A + B #' } #' g_ <- section_fun(g, list(A = matrix(1:4, nrow=2))) #' g_ <- section_fun(g, "A", list(matrix(1:4, nrow=2))) #' g_(diag(1, 2)) #' #' g_ <- section_fun(g, list(A = matrix(1:4, nrow=2))) #' #' ## Using built in function #' set.seed(123) #' rnorm5 <- section_fun(rnorm, list(n=5)) #' rnorm5(0, 1) #' #' set.seed(123) #' rnorm(5) #' #' NULL ## FIXME: If nms is a list it should be reported if not all elements ## of nms have names. Guess the same applies to caracas. #' @rdname section_fun #' @export set_default <- function(fun, nms, vls=NULL){ args <- nms_vls_to_list(nms, vls) nms <- names(args) vls <- args fmls <- formals(fun) i <- match(nms, names(fmls)) j <- c(setdiff(seq_along(fmls), i), i) # new order fmls <- fmls[j] fmls[nms] <- vls formals(fun) <- fmls fun } #' @rdname section_fun #' @export section_fun <- function(fun, nms, vls=NULL, method="args") { method_ <- match.arg(method, c("args", "env", "body", "def", "sub")) args <- nms_vls_to_list(nms, vls) switch(method, "args"=, "def"={set_default(fun, args)}, "body"=, "sub"={section_fun_sub_worker(fun, args)}, "env"={section_fun_env_worker(fun, args)} ) } #' @rdname section_fun #' @export section_fun_sub <- function(fun, nms, vls=NULL, envir=parent.frame()){ args <- nms_vls_to_list(nms, vls) section_fun_sub_worker(fun, args) } section_fun_sub_worker <- function(fun, args, envir=parent.frame()){ fmls <- formals(fun) idx <- match(names(args), names(fmls)) idx <- idx[!is.na(idx)] if (length(idx) > 0){ fmls <- fmls[-idx] } nn <- names(fmls) vv <- paste0(fmls) nc <- nchar(vv) fmls2 <- "" if (length(fmls)>0){ fmls2 <- sapply(1:length(fmls), function(i){ if (nc[i] > 0){ paste0(nn[i], "=", vv[i]) } else { nn[i] } }) } hd <- paste0("function(", paste0(fmls2, collapse = ", "), ")") aux <- sapply(1:length(args), function(i){ nm <- names(args)[i] ss <- deparse(args[[i]]) ss <- paste0(ss, collapse = " ") paste0(nm, " = ", ss) }) bd1 <- paste0("\n ## section\n ", paste0(aux, collapse=";\n "), "\n ## section (end)") bd1 bd2 <- deparse(body(fun)) bd2 <- bd2[2:(length(bd2) - 1)] bd2 <- gsub("^ *", "", bd2) ## Remove leading whites bd2 <- paste0(paste0(bd2, collapse="\n ")) bd <- paste0("\n{ ", paste0(c(bd1, bd2), collapse="\n "), "\n}") ff <- paste0(c(hd, bd), collapse="") out <- eval(parse(text=ff)) environment(out) <- environment(fun) out } nms_vls_to_list <- function(nms, vls){ ## cat("nms: "); print(nms); cat(" vls: "); print(vls) all_has_names <- function(x){ (!is.null(names(x))) && all(nchar(names(x)) > 0) } if (!(is.atomic(nms) || is.list(nms))){ stop("'nms' must be list or atomic") } if (inherits(nms, "list")){ if (!is.null(vls)) { warning("vls ignored") } out <- nms } else { if (all_has_names(nms)){ vls <- as.list(nms) out <- vls } else { if (length(nms) != length(vls)){ stop("'nms' and 'vls' must have same length\n") } vls <- as.list(vls) names(vls) <- nms out <- vls } } return(out) } #' @rdname section_fun #' @export section_fun_env <- function(fun, nms, vls=NULL) { args <- nms_vls_to_list(nms, vls) section_fun_env_worker(fun, args) ##.partial(fun, args) } section_fun_env_worker <- function(fun, args){ fun <- as.section_function(fun) apply_args(fun, args) } as.section_function <- function(fun) { if (!inherits(fun, c("function", "section_function"))) stop("Can not create section_function\n") if (inherits(fun, 'section_function')) { section_function_update(fun) } else { from <- parent.frame() section_function_create(fun, from) } } section_function_create <- function(fun, from = parent.frame()) { arg_env <- new.env(parent = emptyenv()) assign('args', list(), envir = arg_env) assign('args_end', list(), envir = arg_env) fmls <- get_formals(fun) arg_getter <- get_args(arg_env) do_section_function(fun, arg_env, fmls, from) } section_function_update <- function(fun, from = parent.frame()) { #cat("section_function_update\n") arg_env <- clone_environment(attr(fun, "arg_env")) fmls <- get_formals(fun) #cat("formals:\n"); print(fmls) fmls[names(get_section(fun))] <- NULL fun_orig <- environment(fun)$fun arg_getter <- get_args(arg_env) do_section_function(fun_orig, arg_env, fmls, from) } do_section_function <- function(fun, arg_env, fmls, from){ ## fmls <- get_formals(fun) arg_getter <- get_args(arg_env) new_fun <- function() {} formals(new_fun) <- fmls body(new_fun) <- bquote( { . <- "use get_section(function_name) to see section" . <- "use get_fun(function_name) to see original function" args <- arg_getter() do.call(.(fun), args) }, list(fun = substitute(fun, from)) ) structure(new_fun, class = c('section_function', 'function'), arg_env = arg_env) } get_formals <- function(fun){ if (is.primitive(fun)) { fmls <- formals(args(fun)) } else { fmls <- formals(fun) } fmls } ## SOME UTILITIES #' @rdname section_fun #' @export get_section <- function(object){ if (!inherits(object, "section_function")) stop("'object' must be section_function object\n") attr(object, "arg_env")$args } #' @rdname section_fun #' @export get_fun <- function(object){ if (!inherits(object, "section_function")) stop("'object' must be section_function object\n") environment(object)$fun } #' @export print.section_function <- function(x, ...){ x2 <- x attributes(x2) <- NULL print.default(x2) return(invisible(x)) } #' @export summary.section_function <- function(object, ...){ cat("Original function: \n") print(get_fun(object)) cat("Section: \n") str(get_section(object)) return(invisible(object)) } get_args <- function(added_env) { function() { ## cat("in get_args:\n") env <- parent.frame() args <- names(formals(sys.function(sys.parent(1)))) ## cat("args:\n ");print(args) ## To be specified in call if (length(args) > 0) { vals <- mget(args, envir = env) ## cat("vals:\n"); str(vals) ## To be specified in call ellipsis <- names(vals) == '...' if (any(ellipsis)) { vals <- append(vals, eval(quote(list(...)), env), which(ellipsis)) vals[ellipsis] <- NULL } vals <- vals[!vapply(vals, is_missing_arg, logical(1))] } else { vals <- NULL } ## cat("when done: \n") ## str(list(args=added_env$args, vals=vals, args_end=added_env$args_end)) out <- c(added_env$args, vals, added_env$args_end) ## str(out) out } } apply_args <- function(fun, args, last = FALSE) { fmls <- formals(fun) #cat("+++ formals: \n"); print(fmls) arg_env <- attr(fun, 'arg_env') fmls[names(args)] <- NULL formals(fun) <- fmls[!names(fmls) %in% names(args)] #cat("+++ +++ formals: \n"); print(formals(fun)) if (last) { assign('args_end', append(args, arg_env$args_end), envir = arg_env) } else { ##arg_env <- clone_environment(arg_env) common <- intersect(names(arg_env$args), names(args)) if (length(common) > 0) arg_env$args[common] <- NULL assign('args', append(arg_env$args, args), envir = arg_env) } structure(fun, class = c('section_function', 'function'), arg_env = arg_env) } is_missing_arg <- function(x) { identical(x, quote(expr = )) } clone_environment <- function(e1){ as.environment(as.list(e1, all.names=TRUE)) } doBy/R/esticon.R0000644000176200001440000003433414551451413013151 0ustar liggesusers############################################################################### ## #' @title Contrasts for lm, glm, lme, and geeglm objects #' @description Computes linear functions (i.e. weighted sums) of the #' estimated regression parameters. Can also test the hypothesis, #' that such a function is equal to a specific value. #' @name esticon ## ############################################################################### #' ## #' @aliases esticon.lm esticon.gls #' #' @details Let the estimated parameters of the model be #' \deqn{\beta_1, \beta_2, \dots, \beta_p} #' #' A linear function of the estimates is of the form \deqn{l=\lambda_1 #' \beta_1+\lambda_2 \beta_2+ \dots+\lambda_p \beta_p} where #' \eqn{\lambda_1, \lambda_2, \dots,\lambda_p} is specified by the #' user. #' #' The esticon function calculates l, its standard error and by default also a #' 95 pct confidence interval. It is sometimes of interest to test the #' hypothesis \eqn{H_0: l=\beta_0} for some value \eqn{\beta_0} #' given by the user. A test is provided for the hypothesis \eqn{H_0: #' l=0} but other values of \eqn{\beta_0} can be specified. #' #' In general, one can specify r such linear functions at one time by #' specifying L to be an \eqn{r\times p} matrix where each row consists #' of p numbers \eqn{\lambda_1,\lambda_2,\dots, \lambda_p}. Default is #' then that \eqn{\beta_0} is a p vector of 0s but other values can be #' given. #' #' It is possible to test simultaneously that all specified linear functions #' are equal to the corresponding values in \eqn{\beta_0}. #' #' For computing contrasts among levels of a single factor, 'contrast.lm' may #' be more convenient. #' #' @param obj Regression object (of type lm, glm, lme, geeglm). ## #' @param x A linear contrast object (as returned by \code{esticon()}. #' @param L Matrix (or vector) specifying linear functions of the regression #' parameters (one #' linear function per row). The number of columns must match the number of #' fitted regression parameters in the model. See 'details' below. #' @param parm a specification of which parameters are to be given #' confidence intervals, either a vector of numbers or a vector #' of names. If missing, all parameters are considered. #' @param beta0 A vector of numbers #' @param conf.int TRUE ## #' @param conf.level The desired confidence level. #' @param level The confidence level #' @param joint.test Logical value. If TRUE a 'joint' Wald test for the #' hypothesis L beta = beta0 is made. Default is that the 'row-wise' tests are #' made, i.e. (L beta)i=beta0i. If joint.test is TRUE, then no confidence #' interval etc. is calculated. #' @param \dots Additional arguments; currently not used. #' #' @return Returns a matrix with one row per linear function. Columns contain #' estimated coefficients, standard errors, t values, degrees of freedom, #' two-sided p-values, and the lower and upper endpoints of the 1-alpha #' confidence intervals. #' #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @keywords utilities #' @examples #' #' data(iris) #' lm1 <- lm(Sepal.Length ~ Sepal.Width + Species + Sepal.Width : Species, data=iris) #' ## Note that the setosa parameters are set to zero #' coef(lm1) #' #' ## Estimate the intercept for versicolor #' lambda1 <- c(1, 0, 1, 0, 0, 0) #' esticon(lm1, L=lambda1) #' #' ## Estimate the difference between versicolor and virgica intercept #' ## and test if the difference is 1 #' lambda2 <- c(0, 1, -1, 0, 0, 0) #' esticon(lm1, L=lambda2, beta0=1) #' #' ## Do both estimates at one time #' esticon(lm1, L=rbind(lambda1, lambda2), beta0=c(0, 1)) #' #' ## Make a combined test for that the difference between versicolor and virgica intercept #' ## and difference between versicolor and virginica slope is zero: #' lambda3 <- c(0, 0, 0, 0, 1, -1) #' esticon(lm1, L=rbind(lambda2, lambda3), joint.test=TRUE) #' #' # Example using esticon on coxph objects (thanks to Alessandro A. Leidi). #' # Using dataset 'veteran' in the survival package #' # from the Veterans' Administration Lung Cancer study #' #' if (require(survival)){ #' data(veteran) #' sapply(veteran, class) #' levels(veteran$celltype) #' attach(veteran) #' veteran.s <- Surv(time, status) #' coxmod <- coxph(veteran.s ~ age + celltype + trt, method='breslow') #' summary(coxmod) #' #' # compare a subject 50 years old with celltype 1 #' # to a subject 70 years old with celltype 2 #' # both subjects on the same treatment #' AvB <- c(-20, -1, 0, 0, 0) #' #' # compare a subject 40 years old with celltype 2 on treat=0 #' # to a subject 35 years old with celltype 3 on treat=1 #' CvB <- c(5, 1, -1, 0, -1) #' #' est <- esticon(coxmod, L=rbind(AvB, CvB)) #' est #' ##exp(est[, c(2, 7, 8)]) #' } #' @export esticon <- function(obj, L, beta0, conf.int = TRUE, level=0.95, joint.test=FALSE,...) UseMethod("esticon") #' @export esticon.gls <- function (obj, L, beta0, conf.int = TRUE, level=0.95, joint.test=FALSE,...){ if (joint.test) .wald(obj, L, beta0) else { stat.name <- "X2.stat" ##vcv <- vcov(obj) vcv <- vcov(obj, complete=FALSE) coef.mat <- matrix(coef(obj)) df <- 1 .esticonCore(obj, L, beta0, conf.int=conf.int,level,coef.mat,vcv,df,stat.name) } } #' @export esticon.geeglm <- function (obj, L, beta0, conf.int = TRUE, level=0.95, joint.test=FALSE,...){ if (joint.test) .wald(obj, L, beta0) else { stat.name <- "X2.stat" coef.mat <- summary(obj)$coef vcv <- summary(obj)$cov.scaled df <- 1 .esticonCore(obj, L, beta0, conf.int=conf.int,level,coef.mat,vcv,df,stat.name) } } #' @export esticon.lm <- function (obj, L, beta0, conf.int = TRUE, level=0.95, joint.test=FALSE,...){ if (joint.test) .wald(obj, L, beta0) else { stat.name <- "t.stat" coef.mat <- summary.lm(obj)$coefficients coef.vec <- coef(obj) vcv <- summary.lm(obj)$cov.unscaled * summary.lm(obj)$sigma^2 df <- obj$df.residual .esticonCore(obj, L, beta0, conf.int=conf.int, level, coef.mat, vcv, df, stat.name, coef.vec=coef.vec) } } #' @export esticon.glm <- function (obj, L, beta0, conf.int = TRUE, level=0.95, joint.test=FALSE,...){ if (joint.test) .wald(obj, L, beta0) else { coef.mat <- summary.lm(obj)$coefficients vcv <- summary(obj)$cov.scaled if(family(obj)[1] %in% c("poisson", "binomial")){ stat.name <- "X2.stat" df <- 1 } else { stat.name <- "t.stat" df <- obj$df.residual } .esticonCore(obj, L, beta0, conf.int=conf.int,level=level,coef.mat,vcv,df,stat.name) } } #' @export esticon.merMod <- function (obj, L, beta0, conf.int = TRUE, level=0.95, joint.test=FALSE, ...){ if (joint.test) .wald(obj, L, beta0) else { stat.name <- "X2.stat" coef.mat <- matrix(lme4::fixef(obj)) ##vcv <- as.matrix(vcov(obj)) vcv <- as.matrix(vcov(obj, complete=FALSE)) df <- 1 .esticonCore(obj, L, beta0, conf.int=conf.int, level, coef.mat, vcv, df, stat.name) } } #' @export esticon.coxph <- function (obj, L, beta0, conf.int = TRUE, level = 0.95, joint.test = FALSE, ...){ if (joint.test == TRUE) .wald(obj, L, beta0) else { cf <- summary(obj)$coefficients vcv <- obj$var stat.name <- "X2.stat" df <- 1 .esticonCore(obj, L, beta0, conf.int = conf.int, level = level, cf, vcv, df, stat.name) } } ### ###################################################### ### ### esticon.lme needs better testing at some point ### ### ###################################################### #' @export esticon.lme <- function (obj, L, beta0, conf.int = NULL, level=0.95, joint.test=FALSE,...){ warning("The esticon function has not been thoroughly teste on 'lme' objects") if (joint.test) .wald(obj, L, beta0) else { stat.name <- "t.stat" coef.mat <- summary(obj)$tTable rho <- summary(obj)$cor vcv <- rho * outer(coef.mat[, 2], coef.mat[, 2]) tmp <- L tmp[tmp == 0] <- NA df.all <- t(abs(t(tmp) * obj$fixDF$X)) df <- apply(df.all, 1, min, na.rm = TRUE) problem <- apply(df.all != df, 1, any, na.rm = TRUE) if (any(problem)) warning(paste("Degrees of freedom vary among parameters used to ", "construct linear contrast(s): ", paste((1:nrow(tmp))[problem], collapse = ","), ". Using the smallest df among the set of parameters.", sep = "")) df <- min(df) .esticonCore(obj, L, beta0, conf.int=conf.int,level,coef.mat,vcv,df,stat.name) } } ### .functions below here ### .wald <- function (obj, L, beta0) { if (!is.matrix(L) && !is.data.frame(L)) L <- matrix(L, nrow = 1) if (missing(beta0)) beta0 <- rep(0, nrow(L)) df <- nrow(L) if (inherits(obj, "geese")){ coef.mat <- obj$beta vcv <- obj$vbeta } else if (inherits(obj, "geeglm")){ coef.mat <- obj$coef vcv <- summary(obj)$cov.scaled } else if (inherits(obj, "gls")) { ##vcv <- vcov(obj) vcv <- vcov(obj, complete=FALSE) coef.mat <- matrix(coef(obj)) } else if (inherits(obj, "gee")) { coef.mat <- obj$coef vcv <- obj$robust.variance } else if (inherits(obj, "lm")) { coef.mat <- summary.lm(obj)$coefficients[, 1] vcv <- summary.lm(obj)$cov.unscaled * summary.lm(obj)$sigma^2 if (inherits(obj, "glm")) { vcv <- summary(obj)$cov.scaled } } else if (inherits(obj, "coxph")) { coef.mat <- obj$coef vcv <- obj$var } else stop("obj must be of class 'lm', 'glm', 'aov', 'gls', 'gee', 'geese', 'coxph'") u <- (L %*% coef.mat)-beta0 vcv.u <- L %*% vcv %*% t(L) W <- t(u) %*% solve(vcv.u) %*% u prob <- 1 - pchisq(W, df = df) out <- as.data.frame(cbind(W, df, prob)) names(out) <- c("X2.stat", "DF", "Pr(>|X^2|)") as.data.frame(out) } .esticonCore <- function (obj, L, beta0, conf.int = NULL, level, coef.mat, vcv, df, stat.name, coef.vec=coef.mat[,1]) { ## Notice ## coef.mat: summary(obj)$coefficients ## coef.vec: coef(obj) ## cl <- match.call(); print(cl); print(coef.mat) if (missing(L)) stop("Contrast matrix 'L' is missing") if (!is.matrix(L) && !is.data.frame(L)) L <- matrix(L, nrow = 1) if (missing(beta0)) beta0 <- rep(0, nrow(L)) ##print(list(L=L, beta0=beta0)) idx <- !is.na(coef.vec) ## Only want columns of L for identifiable parameters L <- L[ , idx, drop=FALSE] if (!dim(L)[2] == dim(coef.mat)[1]) stop(paste("\n Dimension of ", deparse(substitute(L)), ": ", paste(dim(L), collapse = "x"), ", not compatible with no of parameters in ", deparse(substitute(obj)), ": ", dim(coef.mat)[1], sep = "")) ct <- L %*% coef.mat[, 1] ct.diff <- L %*% coef.mat[, 1] - beta0 vcv.L <- L %*% vcv %*% t(L) if (is.null(vn <- rownames(L))) vn <- 1:nrow(L) rownames(vcv.L) <- colnames(vcv.L) <- vn vc <- sqrt(diag(vcv.L)) switch(stat.name, t.stat = { prob <- 2 * (1 - pt(abs(ct.diff/vc), df)) }, X2.stat = { prob <- 1 - pchisq((ct.diff/vc)^2, df = 1) }) if (stat.name == "X2.stat") { out <- cbind(hyp=beta0, est = ct, stderr = vc, t.value = (ct.diff/vc)^2, df = df, prob = prob ) } else if (stat.name == "t.stat") { out <- cbind(hyp=beta0, est = ct, stderr = vc, t.value = ct.diff/vc, df = df, prob = prob) } dim.names <- list(NULL, c("beta0","estimate","std.error", "statistic","df","p.value")) dimnames(out) <- dim.names out <- out[, c(2,3,4,6,1,5), drop=FALSE] conf.int <- "wald" if (!is.null(conf.int)) { if (level <= 0 || level >= 1) stop("level should be betweeon 0 and 1. Usual values are 0.95, 0.90") alpha <- 1 - level switch(stat.name, t.stat = { quant <- qt(1 - alpha/2, df) }, X2.stat = { quant <- qnorm(1 - alpha/2) }) cint <- cbind(ct.diff - vc * quant, ct.diff + vc * quant) colnames(cint) <- c("lwr", "upr") out <- cbind(out, cint) } out <- as.data.frame(out) ## FIXME esticon_class added; not sure if this is good idea? class(out) <- c("esticon_class", "data.frame") attr(out, "L") <- L attr(out, "vcv") <- vcv.L out } #' @rdname esticon #' @param object An \code{esticon_class} object. #' @export coef.esticon_class <- function (object, ...) { out <- object$estimate names(out) <- rownames(object) out } #' @export #' @rdname esticon summary.esticon_class <- function (object, ...) { cat("Coefficients:\n") ##printCoefmat(object$coef) printCoefmat(object) cat("\n") cat("L:\n") ##print(object$L) print(attr(object, "L")) cat("\n") invisible(object) } #' @export print.esticon_class <- function(x, ...){ printCoefmat(x[,1:6]) } #' @export #' @rdname esticon confint.esticon_class <- function (object, parm, level = 0.95, ...) { cf <- coef(object) pnames <- names(cf) if (missing(parm)) parm <- pnames else if (is.numeric(parm)) parm <- pnames[parm] a <- (1 - level)/2 a <- c(a, 1 - a) ## stats:::confint.default code ## pct <- stats:::format.perc(a, 3) ## fac <- qnorm(a) ## replaced with pct <- a DF <- object$df fac <- if (!is.null(DF)) qt(a, DF[1]) else fac <- qnorm(a) ## to here ci <- array(NA, dim = c(length(parm), 2L), dimnames = list(parm, pct)) ses <- sqrt(diag(vcov(object)))[parm] ##str(list(cf=cf, parm=parm, fac=fac, ses=ses)) ci[] <- cf[parm] + ses %o% fac ci } #' @export #' @rdname esticon vcov.esticon_class <- function (object, ...){ attr(object, "vcv") } doBy/R/by_lapplyBy.R0000644000176200001440000000420715030273217013764 0ustar liggesusers####################################################################### #' #' @title Formula based version of lapply and sapply #' @description This function is a wrapper for calling lapply on the #' list resulting from first calling splitBy. #' @name by-lapply #' ####################################################################### #' @param formula A formula describing how data should be split. #' @param data A dataframe. #' @param FUN A function to be applied to each element in the split #' list, see 'Examples' below. #' @param \dots optional arguments to FUN. #' @param USE.NAMES Same as for `sapply` #' @param simplify Same as for `sapply` #' @return A list. #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @seealso \code{\link{splitBy}}, \code{\link{split_by}} #' @keywords utilities #' #' @examples #' fun <- function(x) range(x$uptake) #' lapplyBy(~Treatment + Type, data=CO2, FUN=fun) #' sapplyBy(~Treatment + Type, data=CO2, FUN=fun) #' #' # Same as #' lapply(splitBy(~Treatment + Type, data=CO2), FUN=fun) #' @export #' @rdname by-lapply lapply_by <- function(data, formula, FUN, ...){ cl <- match.call(expand.dots = TRUE) cl[[2]] <- formula cl[[3]] <- data names(cl)[2:3] <- c("formula", "data") cl[[1]] <- as.name("lapplyBy") eval(cl) } #' @export #' @rdname by-lapply lapplyBy <- function (formula, data = parent.frame(), FUN, ...) { out <- splitBy(formula, data = data) gr <- unique(attr(out, "grps")) out <- lapply(out, FUN, ...) out <- out[gr] out } #' @export #' @rdname by-lapply sapply_by <- function(data, formula, FUN, ..., simplify = TRUE, USE.NAMES = TRUE){ cl <- match.call(expand.dots = TRUE) cl[[2]] <- formula cl[[3]] <- data names(cl)[2:3] <- c("formula", "data") cl[[1]] <- as.name("sapplyBy") eval(cl) } #' @export #' @rdname by-lapply sapplyBy <- function (formula, data = parent.frame(), FUN, ..., simplify = TRUE, USE.NAMES = TRUE) { out <- splitBy(formula, data = data) gr <- unique(attr(out, "grps")) out <- sapply(out, FUN, ..., simplify=simplify, USE.NAMES=USE.NAMES) if (!simplify) out <- out[gr] out } doBy/R/data_scale_df.R0000644000176200001440000000270515025235177014237 0ustar liggesusers#' @title Scale numeric variables in a data frame #' #' @description Applies `base::scale()` to numeric, integer, or #' logical columns in a data frame. Non-numeric columns are left #' unchanged. #' #' @param x A data frame or matrix. #' @param center Logical; if TRUE, center the variables. #' @param scale Logical; if TRUE, scale the variables. #' #' @details #' If `x` is not a data frame, `base::scale()` is applied directly. #' #' @return An object of the same class as `x`. #' #' @examples #' scale_df(iris) #' #' @name scale_df #' @export scale_df <- function(x, center = TRUE, scale = TRUE){ if (!is(x, "data.frame")){ return(scale(x, center=center, scale=scale)) } else { ## x is dataframe b <- sapply(x, function(z){is(z, c("numeric")) || is(z, c("integer")) || is(z, c("logical")) }) if (!any(b)){ ## x only has numeric values return(scale(x, center=center, scale=scale)) } else { ## x is dataframe with non-numerics x2 <- x[,b, drop=FALSE] x2 <- scale(x2, center=center, scale=scale) x[, b] <- x2 if (!is.null(a <- attributes(x2)$"scaled:center")) attr(x, "scaled:center") <- a if (!is.null(a <- attributes(x2)$"scaled:scale")) attr(x, "scaled:scale") <- a return(x) } } } doBy/R/data_firstobs_lastobs.R0000644000176200001440000000350614551451413016055 0ustar liggesusers############################################################################### #' @title Locate the index of the first/last unique value #' @description Locate the index of the first/last unique value in i) a vector #' or of a variable in a data frame. #' @name firstlastobs ############################################################################### #' @details If writing ~a + b + c as formula, then only a is considered. #' #' @aliases firstobs lastobs firstobs.default lastobs.default firstobs.formula #' lastobs.formula #' @param x A vector #' @param formula A formula (only the first term is used, see 'details'). #' @param data A data frame #' @param \dots Currently not used #' @return A vector. #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @keywords utilities #' @examples #' x <- c(rep(1, 5), rep(2, 3), rep(3, 7), rep(1, 4)) #' firstobs(x) #' lastobs(x) #' data(dietox) #' firstobs(~Pig, data=dietox) #' lastobs(~Pig, data=dietox) #' #' @export #' @rdname firstlastobs lastobs <- function(x, ...) UseMethod("lastobs") #' @export #' @rdname firstlastobs firstobs <- function(x, ...) UseMethod("firstobs") #' @export #' @rdname firstlastobs lastobs.default <- function(x, ...) { ux <- unique(x) m <-match(ux, x) sort(sapply(ux, function(i) {max(which(i==x))})) } #' @export #' @rdname firstlastobs firstobs.default <- function(x, ...) { ux <- unique(x) m <- match(ux,x) sort(sapply(ux, function(i) {min(which(i==x))})) } #' @export #' @rdname firstlastobs lastobs.formula <- function(formula, data=parent.frame(), ...) { rhs <- gsub(" +","",strsplit(paste(formula[2]),"\\+")[[1]][1]) lastobs(data[,rhs]) } #' @export #' @rdname firstlastobs firstobs.formula <- function(formula, data=parent.frame(), ...) { rhs <- gsub(" +","",strsplit(paste(formula[2]),"\\+")[[1]][1]) firstobs(data[,rhs]) } doBy/R/functional_bquote_list.R0000644000176200001440000000166314551451413016260 0ustar liggesusers#' Backquote a list of functions #' #' @param fun_list List of functions #' #' @seealso [base::bquote()], [set_default()], [section_fun()] #' @examples #' #' ## Evaluate a list of functions #' f1 <- function(x){x + 1} #' f2 <- function(x){x + 8} #' #' f1_ <- set_default(f1, list(x=10)) #' f2_ <- set_default(f2, list(x=10)) #' #' f1_(); f2_() #' #' fn_list <- list(f1_, f2_) #' fn_list_ <- bquote_fun_list(fn_list) #' #' eval(fn_list[[1]]) ## No #' sapply(fn_list, eval) ## No #' #' eval(fn_list_[[1]]) ## Yes #' sapply(fn_list_, eval) ## Yes #' #' @export bquote_fun_list <- function(fun_list) { if (!inherits(fun_list, "list")) stop("'fun_list' is not a list.") cls <- c("function", "section_function") if (!all(sapply(fun_list, inherits, cls))) stop("Not all elements in 'fun_list' are functions or section_function objects.") lapply(fun_list, function(g) { bquote(.(g)()) } ) } doBy/R/linest_get.R0000644000176200001440000001240214551451413013632 0ustar liggesusers## ###################################################################### ## ## Auxillary functions for calculating contrasts, lsmeans etc ## ## Banff, August 2013, Søren Højsgaard ## ## ###################################################################### #' @title Auxillary functions for computing lsmeans, contrasts etc #' #' @description Auxillary functions for computing lsmeans, contrasts etc. #' #' @name linest-get #' #' @param obj An R object #' @param xlev FIXME: to be described #' @param at FIXME: to be described #' @param covariateVal FIXME: to be described #' @param newdata FIXME: to be described #' @export #' @rdname linest-get get_xlevels <- function(obj){ UseMethod("get_xlevels") } #' @export #' @rdname linest-get get_xlevels.default <- function(obj){ obj$xlevels } #' @export #' @rdname linest-get get_xlevels.mer <- function(obj){ ans <- lapply(obj@frame,levels) ans <- ans[unlist(lapply(ans, length))>0] nn <- names(attr(terms(obj), "dataClasses")) ii <- match(names(ans), nn) ii <- ii[!is.na(ii)] ans <- ans[nn[ii]] ans } #' @export #' @rdname linest-get get_xlevels.merMod <- function(obj){ ##cat("get_xlevels.lmerMod\n") ans <- lapply(obj@frame,levels) ans <- ans[unlist(lapply(ans, length))>0] nn <-attr(terms(obj), "term.labels") ii <- match(names(ans), nn) ii <- ii[!is.na(ii)] ans <- ans[nn[ii]] ##print(ans) ans } #' @export #' @rdname linest-get get_contrasts <- function(obj){ UseMethod("get_contrasts") } #' @export #' @rdname linest-get get_contrasts.default <- function(obj){ obj$contrasts ## FIXME: check RL code } #' @export #' @rdname linest-get get_contrasts.merMod <- function(obj){ attr(model.matrix(obj), "contrasts") } #' @export #' @rdname linest-get set_xlevels <- function(xlev, at){ nam <- names(xlev) nn <- match(nam, names(at)) nn <- nn[!is.na(nn)] at.fact <- at[nn] xlev[names(at[nn])] <- at.fact attr(xlev, "at.fact") <- at.fact xlev } #' @export #' @rdname linest-get get_vartypes <- function(obj){ #cat("get_vartypes:\n") ## Common!! trms <- terms( model.frame( obj )) trms <- delete.response( trms ) att <- attributes( trms ) #cat("factors:\n"); print(att$factors) if (length(att$factors) > 0){ rhs.terms <- rownames(att$factors)[rowSums(att$factors) > 0] rhs.class <- att$dataClass[match(rhs.terms, names(att$dataClass))] nums <- rhs.terms[rhs.class == "numeric"] ##print(list(rhs.terms=rhs.terms, rhs.class=rhs.class, nums=nums)) fact <- rhs.terms[rhs.class == "factor"] } else { nums <- character(0) rhs.class <- character(0) fact <- character(0) } ## END ## print("here") ## print(list(numeric=nums, factor=fact)) list(numeric=nums, factor=fact) } #' @export #' @rdname linest-get set_covariate_val <- function(xlev, covariateVal){ nam <- names(xlev) nn <- match(nam, names(covariateVal)) nn <- nn[!is.na(nn)] con.con <- covariateVal[nn] xlev[names(covariateVal[nn])] <- con.con xlev } .get_covariate_ave <- function(obj, at=NULL, tt=terms(obj)){ ##cat(".get_covariate_ave:\n") ## Common!! trms <- terms( model.frame( obj )) trms <- delete.response( trms ) att <- attributes( trms ) ##ff <<- att$factors if (length(att$factors)>0){ rhs.terms <- rownames(att$factors)[rowSums(att$factors)>0] rhs.class <- att$dataClass[match(rhs.terms, names(att$dataClass))] nums <- rhs.terms[rhs.class=="numeric"] } else { nums <- character(0) rhs.class <- character(0) } ##print(nums) ## END ## calculate average: ans <- lapply(model.frame(obj)[,nums, drop=FALSE], mean) ##cat("ans:\n"); print(ans) nn <- match(names(ans), names(at)) nn <- nn[!is.na(nn)] at.num <- at[nn] ans[names(at[nn])] <- at.num ##cat("ans (after insertion of 'at' values):\n"); print(ans) attr(ans, "at.num") <- at.num ans } #' @export #' @rdname linest-get get_X <- function(obj, newdata, at=NULL){ UseMethod("get_X") } #' @export #' @rdname linest-get #' get_X.default <- function(obj, newdata, at=NULL){ ##cat("get_X.default\n"); print(newdata) tt <- terms(obj) Terms <- delete.response(tt) offval <- NULL M <- attr(terms(formula(obj)), "factors") idx <- grep("offset\\(", rownames(M)) if (length(idx) > 0){ off <- rownames(M)[idx] offvar <- gsub("offset\\((.*)\\)", "\\1\\", off) offval <- if (!is.null(at) && !is.null(at[offvar])) at[offvar] else 1 d <- data.frame(rep(offval, nrow(newdata))) names(d) <- offvar newdata <- cbind(d, newdata) } mf <- model.frame(Terms, newdata, xlev = get_xlevels(obj)) X <- model.matrix(Terms, mf, contrasts.arg = get_contrasts(obj)) attr(X, "assign" ) <- NULL attr(X, "contrasts" ) <- NULL attr(X, "offset" ) <- offval X } #' @export #' @rdname linest-get get_X.merMod <- function(obj, newdata, at=NULL){ tt <- terms(obj) Terms <- delete.response(tt) mf <- model.frame(Terms, newdata, xlev = get_xlevels(obj)) X <- model.matrix(Terms, mf, contrasts.arg = get_contrasts(obj)) attr(X,"assign") <- NULL attr(X, "contrasts") <- NULL X } doBy/R/pick.R0000644000176200001440000000442215023576403012430 0ustar liggesusers#' @title Extract (pick) elements without using brackets #' #' @description Extract (pick) elements without using brackets so that elements can be picked out as part of a pipe workflow. #' #' @details #' #' These two helper functions extract elements from lists, data frames, or vectors. #' They are simple wrappers for the standard bracket operators in R: #' #' - `pick1()` uses single brackets (`[`) and returns a **subset**. #' - `pick2()` uses double brackets (`[[`) and returns the **element itself**. #' #' These are safer and more flexible than `$`, especially when used with the base R pipe (`|>`) #' or in functional programming. #' #' @param x A list, data frame, or vector. #' @param which The index or name of the element(s) to extract. #' #' @return #' - `pick1()` returns a subset of `x`. #' - `pick2()` returns a single element from `x`. #' #' @examples #' lst <- list(a = 1:3, b = 4:6) #' #' # Without pipe #' pick1(lst, "a") # List with one element #' pick2(lst, "a") # Just the vector 1:3 #' #' # With base R pipe #' lst |> pick1("a") #' lst |> pick2("a") #' #' df <- data.frame(x = 1:5, y = letters[1:5]) #' #' df |> pick1("y") # Returns a data frame with column 'y' #' df |> pick2("y") # Returns column 'y' as a character vector #' #' @name pick_elements #' @aliases pick1 pick2 #' @export pick1 <- function(x, which) { x[which] } #' @rdname pick_elements #' @export pick2 <- function(x, which) { x[[which]] } #' head and tail for matrices #' @param x matrix #' @param n,m number of rows and columns #' @name head_matrix #' @examples #' M <- matrix(1:20, nrow=4) #' head2(M) #' head2(M, 2) #' @export head2 <- function(x, n=6, m=n) { if (!inherits(x, c("matrix","sparseMatrix"))) stop("'x' must be a matrix or a sparse matrix\n") if (length(dim(x)) != 2) stop("'x' must be have dimension with length two. \n") di <- dim(x) nr <- di[1] nc <- di[2] rr <- 1:min(n, nr) cc <- 1:min(m, nc) x[rr, cc] } #' @rdname head_matrix #' @export tail2 <- function(x, n=6, m=n) { if (length(dim(x)) != 2) stop("'x' must be have dimension with length two. \n") di <- dim(x) nr <- di[1] nc <- di[2] n <- min(n, nr) m <- min(m, nc) rr <- (nr-n+1):nr cc <- (nc-m+1):nc x[rr, cc] } doBy/R/sequence_utilities.R0000644000176200001440000001206315030070745015401 0ustar liggesusers#' @title Find sub-sequences of identical elements in a vector. #' @description Find sub-sequences of identical elements in a vector. #' @name sub_seq #' #' @param x An atomic vector or a factor. #' @param item Optionally a specific value to look for in `x`. #' @return A dataframe. #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @seealso \code{\link{rle}} #' #' #' @keywords utilities #' @examples #' #' x <- c(1, 1, 1, 0, 0, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 3) #' sub_seq(x) #' sub_seq(x, item=1) #' @rdname sub_seq #' @export sub_seq <- function (x, item = NULL) { rrr <- rle(as.character(x)) len <- rrr$lengths val <- rrr$values first <- last <- rep.int(NA, length(val)) first[1] <- 1 last [1] <- len[1] if (length(val) > 1) { for (kk in 2:length(val)) { first[kk] <- last[kk - 1] + 1 last [kk] <- last[kk - 1] + len[kk] } } midp <- floor(first + len / 2) ans <- cbind(first=first, last=last, slength=len, midpoint=midp) if (!is.null(item)) { iii <- val == item ans <- as.data.frame.matrix(ans[iii,, drop=FALSE], stringsAsFactors=FALSE) ans$value <- val[iii] } else { ans <- as.data.frame.matrix(ans, stringsAsFactors=FALSE) ans$value <- val } ans } #' @rdname sub_seq #' @export subSeq <- sub_seq ## #' @rdname sub_seq ## #' @export ## is_grouped <- function(x) { ## !any(table(rle2(x)$values) > 1) ## } ## #' @rdname sub_seq ## #' @export ## rle2 <- function(x) { ## if (is.factor(x)) { ## rle(as.character(x)) ## } else { ## rle(x) ## } ## } #' #' Calculate "time since event" in a vector #' #' Calculates the time since the nearest event in a sequence, #' optionally using a custom time scale. #' #' Events are coded as 1 (or TRUE). Non-events are anything else. The #' result includes absolute and signed distances to events. #' #' @param yvar A numeric or logical vector indicating events. #' @param tvar An optional numeric vector specifying time values. Defaults to the index. #' @return A data frame with columns 'yvar', 'tvar', 'abs.tse' (absolute time since event), #' 'sign.tse' (signed time since event), and other helper columns. #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @seealso \code{\link{subSeq}}, \code{\link{rle}} #' @examples #' #' ## Example 1: Basic usage with default time index #' y <- c(0, 0, 1, 0, 0, 1, 0) #' tse <- time_since_event(y) #' print(tse) #' #' ## Example 2: Custom (non-integer) time variable #' y <- c(0, 0, 1, 0, 0, 0, 1, 0) #' t <- seq(0.5, 3.5, length.out = length(y)) #' tse <- time_since_event(y, t) #' print(tse) #' #' ## Example 3: Plotting the signed time since event #' plot(sign.tse ~ tvar, data = tse, type = "b", #' main = "Signed time since event", #' xlab = "Time", ylab = "Signed time since event") #' grid() #' abline(h = 0, col = "red", lty = 2) #' #' @export time_since_event <- function(yvar, tvar=seq_along(yvar)) { if (!(is.numeric(yvar) | is.logical(yvar))) { stop("yvar must be either numeric or logical") } yvar[is.na(yvar)] <- 0 event.idx <- which(yvar == 1) if (length(event.idx) == 0) { return(NULL) } n.event <- length(event.idx) ## find event times event.time <- tvar[event.idx] ## get time difference to each event rrr <- do.call(rbind, lapply(event.idx, function(ii) tvar-tvar[ii])) abs.tse <- apply(abs(rrr), 2, min) ## get the event windows (~ symmetrical around event time) ewin<-rep.int(NA, length(yvar)) if (n.event > 1) { ff <- event.time[1:(n.event-1)] + diff(event.time) / 2 ewin[tvar <= ff[1]] <- 1 for (ii in 2:(length(ff) - 0)){ ewin[tvar > ff[ii - 1] & tvar <= ff[ii] ] <- ii } ewin[tvar > ff[length(ff)]] <- n.event } else { ewin[] <- n.event } ## get the signs ggg <- list() for (ii in 1:(length(event.idx))){ ggg[[ii]] <- rrr[ii, ewin == ii] } ggg <- unlist(ggg) sign.tse <- sign(ggg) * abs.tse run <- cumsum(yvar) un <- unique(run) tlist <- list() for (ii in 1:length(un)){ vv <- un[ii] yy <- yvar[run == vv] tt <- tvar[run == vv] tt <- tt - tt[1] tlist[[ii]] <- tt } tae <- unlist(tlist) tae[run == 0] <- NA yvar2 <- rev(yvar) tvar2 <- rev(tvar) run2 <- cumsum(yvar2) un2 <- unique(run2) tlist2 <- list() for (ii in 1:length(un2)){ vv <- un2[ii] yy <- yvar2[run2 == vv] tt <- tvar2[run2 == vv] tt <- tt - tt[1] tlist2[[ii]] <- tt } tbe <- unlist(tlist2) tbe[run2==0] <- NA tbe <- rev(tbe) run[run==0]<-NA ans <- cbind(data.frame(yvar=yvar, tvar=tvar), abs.tse, sign.tse, ewin=ewin, run, tae=tae, tbe=tbe) ans } ## yvar, tvar=seq_along(yvar) #' @rdname time_since_event #' @param ... Arguments pased on to time_since_event #' @export timeSinceEvent <- function(...) { .Deprecated("time_since_event") time_since_event(...) } doBy/R/model_stability.r0000644000176200001440000003415015030306372014721 0ustar liggesusers#' @title Model stability for glm objects #' #' @param data. A data frame #' @param model A glm object #' @param n.searches Number of searches #' @param method Method for generating data #' @param ... Additional arguments to be passed to \code{\link{step}} #' #' @export model_stability_glm <- function(data., model, n.searches=10, method=c("subgroups", "resample"), ...){ method <- match.arg(method) data_list <- generate_data_list(data., K=n.searches, method=method) lhs <- as.character(model$formula[[2]]) fit_list <- data_list |> parallel::mclapply(function(dat..){ model <- update(model, data=dat..) step(model, ...) }) fit_list2 <- parallel::mclapply( fit_list, function(x){ update(x, data=data.) } ) rhs_nms <- get_rhs_nms(model) rhs_list <- get_predictor_list(fit_list2) rhs_matrix <- set_list2matrix(rhs_list, aggregate=TRUE) rhs_raw <- set_list2matrix(rhs_list, aggregate=FALSE) rhs_matrix <- as.data.frame(rhs_matrix) freq <- rhs_matrix$Freq__ rhs_matrix$Freq__ <- NULL rhs_matrix = as(t(rhs_matrix), "dgCMatrix") rhs_raw = as(t(rhs_raw), "dgCMatrix") loc <- get_location(rhs_raw, rhs_matrix) out <- list( call = match.call(), model = model, data. = data., data_list = data_list, formula = formula(model), n.searches = n.searches, fit = fit_list2, rhs_matrix = rhs_matrix, rhs_raw = rhs_raw, n.searches = n.searches, loc = loc, lhs = lhs, freq = freq) class(out) <- "model_stability_glm_class" out } #' @export print.model_stability_glm_class <- function(x, ...){ x$data. <- NULL print.default(x[c("rhs_matrix", "freq")]) } #' @export formula.model_stability_glm_class <- function(x, fit=FALSE, unique=TRUE, text=FALSE, ...){ if (fit){ text <- FALSE } f <- get_formulas(x, unique=unique, text=text) if (!fit){ f } else { cl <- x$model$call lapply(f, function(z) { cl$formula <- z out <- update(eval(cl), data=x$data.) out } ) } } #' @export summary.model_stability_glm_class <- function(object, ...){ freq_pred <- object$rhs_raw |> rowSums() num_pred <- object$rhs_matrix |> colSums() out <- list(number_of_models=ncol(object$rhs_raw), number_of_unique_models=ncol(object$rhs_matrix), number_of_predictors=num_pred, frequency_of_predictors=freq_pred) class(out) <- "model_stability_glm_summary_class" out } #' @export print.model_stability_glm_summary_class <- function(x, ...){ print.default(unclass(x)) } #' @title Get formulas from model_stability_glm_class object #' @param object A model_stability_glm_class object #' @param unique If TRUE, return unique models #' @param text If TRUE, return text (rather than formula). #' @export get_formulas <- function(object, unique=TRUE, text=FALSE){ if (unique){ mmm <- object$rhs_matrix } else { mmm <- object$rhs_raw } rhs2formula <- function(object, i, text=FALSE) { out <- paste0(object$lhs, "~", paste0(names(which(mmm[,i]==1)), collapse="+")) if (!text) as.formula(out) else out } out <- lapply(1:ncol(mmm), function(i){ rhs2formula(object, i, text=text) }) return(out) } #' @title Cross-validation for list of glm objects #' #' @param data. A data frame #' @param fit_list A list of glm objects #' @param K Number of folds #' #' @importFrom boot cv.glm #' @export cv_glm_fitlist <- function(data., fit_list, K=10){ fit_list <- lapply(fit_list, function(fit){ if(!inherits(fit, "glm")) stop("fit must be an glm object") update(fit, data=data.) }) cv.error <- sapply(fit_list, function(fit){ boot::cv.glm(data., fit, K=K)$delta[1] }) return(cv.error) } get_rhs_nms <- function(model){ nms <- names(eval(model$call$data)) lhs <- as.character(formula(model)[[2]]) rhs_nms <- setdiff(nms, lhs) rhs_nms } get_location <- function(rhs_raw, rhs_matrix){ u <- apply(rhs_raw, 1, paste0, collapse='') z <- apply(rhs_matrix, 1, paste0, collapse='') loc <- sapply(u, function(i){ (i == z) |> which() }) names(loc) <- NULL loc } get_predictor_list <- function(x){ rhs_list <- lapply(x, function(s){ s |> terms() |> attr("term.labels") }) rhs_list } get_rhs_matrix <- function(x, aggregate=FALSE){ if (!inherits(x, "model_stability_glm_class")) stop("'x' is not model_stability_glm_class\n") rhs_list <- get_predictor_list(x$fit) rhs_raw <- set_list2matrix(rhs_list, aggregate=aggregate) t(rhs_raw) } #' @title Matrix representatation of list of vectors and vice versa #' @param set_list list of vectors #' @param aggregate should the vectors be aggregated #' @param set_matrix matrix representatation #' @name set_list_set_matrix #' #' @examples #' l <- list(c(1,2,3), c(3,2,4), c(3,2,4)) #' m1 <- set_list2matrix(l) #' m1 #' matrix2set_list(m1) #' #' m2 <- set_list2matrix(l, aggregate=TRUE) #' m2 #' matrix2set_list(m2) #' #' #' @export set_list2matrix <- function(set_list, aggregate=FALSE){ aggregate_matrix <- function(mat){ if (ncol(mat) > 0){ mat <- mat |> as.data.frame() |> table() |> as.data.frame.table() mat <- dplyr::filter(mat, .data$Freq>0) mat <- mat[order(mat$Freq, decreasing=TRUE),] mat <- sapply(mat, function(o){ as.numeric(as.character(o)) }) if (is.null(dim(mat))) mat <- as.data.frame(t(mat)) else mat } else { as.data.frame(cbind(Freq=nrow(mat))) } } ## Get unique terms across all lists unique_terms <- unique(unlist(set_list)) ## Create an empty matrix M with 3 rows and columns equal to the number of unique terms M <- matrix(0, nrow = length(set_list), ncol = length(unique_terms), dimnames = list(NULL, unique_terms)) ## Iterate over each list and mark the presence of terms in the matrix for (i in seq_along(set_list)) { ##print(set_list[[i]]) M[i, set_list[[i]]] <- 1 } if (aggregate) { M2 <- aggregate_matrix(M) colnames(M2) <- c(colnames(M), "Freq__") return(M2) } else return(M) } #' @rdname set_list_set_matrix #' @param set_matrix matrix representatation #' @export matrix2set_list<- function(set_matrix){ has_freq <- "Freq__" %in% colnames(set_matrix) if (has_freq){ set_matrix <- exclude_column_by_name(repeat_rows(set_matrix, set_matrix[,"Freq__"]), "Freq__") } nr <- nrow(set_matrix) out <- vector("list", nr) nms <- colnames(set_matrix) nms <- if(is.null(nms)) 1:ncol(set_matrix) else nms ##print(out); cat("nms:\n"); print(nms) for (i in 1:nrow(set_matrix)){ ri <- set_matrix[i,] ## print(ri) idx <- which(abs(ri)> 1e-5) ##cat("idx:\n");print(idx);print(nms[idx]) out[[i]] <- nms[idx] } return(out) } repeat_rows <- function(M, v) { # Check that the length of v matches the number of rows in M if (length(v) != nrow(M)) { stop("The length of vector v must match the number of rows in matrix M.") } # Create an empty list to store the repeated rows repeated_rows <- vector("list", length = sum(v)) # Fill the list with repeated rows index <- 1 for (i in seq_along(v)) { for (j in seq_len(v[i])) { repeated_rows[[index]] <- M[i, , drop = FALSE] index <- index + 1 } } # Combine the list into a new matrix new_matrix <- do.call(rbind, repeated_rows) return(new_matrix) } exclude_column_by_name <- function(M, colname) { # Check if the column name exists in the matrix col_index <- which(colnames(M) == colname) if (length(col_index) == 0) { stop(paste("Column", colname, "does not exist in the matrix.")) } # Exclude the column by name M <- M[, -col_index, drop = FALSE] return(M) } #' @title Generate data list #' @param data. A data frame #' @param K Number of folds #' @param method Method for generating data #' #' @export generate_data_list <- function(data., K, method=c("subgroups", "resample")){ method <- match.arg(method) sample0 <- function (x, ...) { x[sample.int(length(x), ...)] } out <- as.list(rep(NA, K)) switch (method, "subgroups" = { n <- nrow(data.) f <- ceiling(n/K) s <- sample0(rep(1L:K, f), n) ms <- max(s) for (i in seq_len(ms)) { j.in <- seq_len(n)[(s != i)] di <- data.[j.in, , drop = FALSE] out[[i]] <- di } }, "resample" = { for (i in 1:K){ j.in <- sample(nrow(data.), replace=TRUE) |> sort() di <- data.[j.in, , drop = FALSE] out[[i]] <- di } } ) out } # predictor.list <- # lapply(object$fit, # function(s){ # s |> terms() |> attr("term.labels") # }) # rhs.matrix <- set_list2matrix(predictor.list, aggregate=FALSE) # stability_glm_old <- function(data., model, n.searches=5, k=2, ...){ # # formula. <- formula(model) # data_list <- modelr::crossv_kfold(data., k = n.searches, id = ".id") # # fit_list <- data_list[["train"]] |> # lapply(function(dat..){ # model <- update(model, data=dat..) # step(model, ..., trace=0) # }) # # fit_list2 <- lapply( # fit_list, function(x){ # update(x, data=data.) # } # ) # # out <- list(call=match.call(), # model= model, # data.=data., # data_list=data_list, # formula=formula., # k=k, # n.searches=n.searches, # fit=fit_list2 # ) # # class(out) <- "stability_glm_class" # out # } # ## stability_glm <- function(data., formula., k=2, ...){ ## cat("NOTE: This is an experimental functionality\n") ## fit_list <- data.[["train"]] |> ## map(function(dat..){ ## step(lm(formula., data=dat..), k=k, ..., trace=0) ## }) ## out <- list(call=match.call(), ## data=data., ## formula=formula., ## k=k, ## n.fold=dim(data.)[1], ## fit=fit_list ## ) ## class(out) <- "stability_glm_class" ## out ## } ## #' @export ## model_stability_glm <- function(data., model, n.searches=5, method=c("resample", "subgroups"), ...){ ## data_list <- generate_data_list(data., K=n.searches, method=method) ## formula. <- formula(model) ## sapply(data_list, nrow) ## fit_list <- data_list |> ## lapply(function(dat..){ ## model <- update(model, data=dat..) ## step(model, ...) ## }) ## fit_list2 <- lapply( ## fit_list, function(x){ ## update(x, data=data.) ## } ## ) ## rhs_nms <- get_rhs_nms(model) ## predictor.list <- get_predictor_list(fit_list2) ## rhs <- set_list2matrix(predictor.list, rhs_nms, aggregate=TRUE) ## rhs <- as.data.frame(rhs) ## freq <- rhs$Freq__ ## rhs$Freq__ <- NULL ## out <- list( ## call = match.call(), ## model = model, ## data. = data., ## data_list = data_list, ## formula = formula., ## n.searches = n.searches, ## fit = fit_list2, ## rhs_matrix = as(t(rhs), "dgCMatrix"), ## freq = freq) ## class(out) <- "model_stability_glm_class" ## out ## } ## #' @export ## summary.model_stability_glm_class <- function(object, ...){ ## lhs <- as.character(object$formula[[2]]) ## rhs_nms <- get_rhs_nms(object$model) ## predictor.list <- get_predictor_list(object$fit) ## rhs_raw <- set_list2matrix(predictor.list, rhs_nms, aggregate=FALSE) ## rhs_matrix <- set_list2matrix(predictor.list, rhs_nms, aggregate=TRUE) ## rownames(rhs_matrix) <- 1:nrow(rhs_matrix) ## rhs_matrix <- ## apply(rhs_matrix, 2, as.numeric, simplify=FALSE) |> ## as.data.frame() ## freq <- rhs_matrix$Freq__ ## rhs_matrix$Freq__ <- NULL ## u <- apply(rhs_raw, 1, paste0, collapse='') ## z <- apply(rhs_matrix, 1, paste0, collapse='') ## loc <- sapply(u, function(i){ ## (i == z) |> which() ## }) ## names(loc) <- NULL ## loc ## n.searches <- length(object$fit) ## rhs_raw <- t(rhs_raw) ## rhs_matrix=t(rhs_matrix) ## out <- list( ## model = object$model, ## n.searches = n.searches , ## data. = object$data., ## lhs = lhs, ## rhs_matrix = object$rhs_matrix, #as(t(rhs_matrix), "dgCMatrix"), ## freq = object$freq, ## rhs_raw = rhs_raw, ## rhs_raw = as(t(rhs_raw), "dgCMatrix"), ## loc=loc ## ) ## class(out) <- "model_stability_glm_summary_class" ## out ## } ## #' @export ## formula.model_stability_glm_class <- function(x, fit=FALSE, ...){ ## if (!fit){ ## return(lapply(x$fit, formula)) ## } else { ## x$fit ## } ## } ## #' @export ## formula.model_stability_glm_summary_class <- function(x, fit=FALSE, ...){ ## if (!fit){ ## get_formulas(x) ## } else { ## f <- get_formulas(x) ## cl <- x$model$call ## lapply(f, function(z) { ## cl$formula <- z ## out <- update(eval(cl), data=x$data.) ## out ## } ## ) ## } ## } doBy/R/by_scaleBy.R0000644000176200001440000000562215025234361013555 0ustar liggesusers#' @title Group-wise scaling of data #' @description Splits a data frame or matrix by grouping variables and scales numeric variables within each group. #' #' @param formula Grouping structure: a formula, character vector, or variables as `as.quoted`. #' @param data A data frame or matrix. #' @param center Logical; if TRUE, center the variables. #' @param scale Logical; if TRUE, scale the variables. #' #' @return A list of data frames or matrices (same class as input), one per group. #' #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @seealso \code{\link{summaryBy}}, \code{\link{transformBy}}, \code{\link{orderBy}} #' @keywords utilities #' #' @examples #' scale_by(iris, ~Species) #' scale_by(iris, ~1) #' #' ## Combine result into one data frame: #' a <- scale_by(iris, ~Species) #' d <- do.call(rbind, a) #' #' ## Old interface #' scaleBy(~Species, data = iris, center = TRUE, scale = FALSE) #' scaleBy(~1, data = iris) #' #' @rdname by_scale #' @export scaleBy <- function(formula, data = parent.frame(), center = TRUE, scale = TRUE) { lapplyBy(formula, data = data, scale_df, center = center, scale = scale) } #' @rdname by_scale #' @export scale_by <- function(data, formula, center = TRUE, scale = TRUE) { do.call(scaleBy, list(formula = formula, data = data, center = center, scale = scale)) } ## #' @title Scale a dataframe or matrix ## #' @description Split a dataframe into a list according to the levels ## #' of variables in the dataframe and scale the numeric variables ## #' in each dataframe in the list. ## #' @name by_scale ## #' ## #' @param formula Variables to split data frame by, as `as.quoted` ## #' variables, a formula or character vector. ## #' @param data A dataframe or matrix ## #' @param center Logical, should data be centered. ## #' @param scale Logical, should data be scaled. ## #' ## #' @return A list of objects of same class as `x` ## #' ## #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} ## #' @seealso \code{\link{orderBy}}, \code{\link{order_by}}, ## #' \code{\link{summaryBy}}, \code{\link{summary_by}}, ## #' \code{\link{transformBy}}, \code{\link{transform_by}} ## #' @keywords utilities ## #' ## #' @examples ## #' ## #' scaleBy(~Species, data=iris, center=TRUE, scale=FALSE) ## #' scaleBy(~1, data=iris, center=TRUE, scale=FALSE) ## #' ## #' scale_by(iris, ~Species) ## #' scale_by(iris, ~1) ## #' ## #' ## Not combine list of dataframes to one dataframe e.g. as: ## #' a <- scale_by(iris, ~Species) ## #' d <- do.call(rbind, a) ## #' @rdname by_scale ## #' @export ## scaleBy <- function(formula, data=parent.frame(), center=TRUE, scale=TRUE){ ## lapplyBy(formula, data=data, scale_df, center=center, scale=scale) ## } ## #' @rdname by_scale ## #' @export ## scale_by <- function(data, formula, center=TRUE, scale=TRUE){ ## arg <- list(formula=formula, data=data, center=center, scale=scale) ## do.call(scaleBy, arg) ## } doBy/R/dobyxtabs.R0000644000176200001440000000321314677443326013511 0ustar liggesusers#' @export doby.xtabs <- function (formula = ~., data = parent.frame(), subset, na.action, exclude = c(NA,NaN), drop.unused.levels = FALSE, FUN=sum) { ep <- FALSE; sh.eprint<- function(x){ if (ep==TRUE){str <- deparse(substitute(x)); cat(paste("E>>", str," =", x),fill=TRUE)}} if (!missing(formula) && !inherits(formula, "formula")) stop("formula is incorrect") if (any(attr(terms(formula), "order") > 1)) stop("interactions are not allowed") if (missing(na.action)) na.action <- getOption("na.action") m <- match.call(expand.dots = FALSE) if (is.matrix(eval(m$data, parent.frame()))) m$data <- as.data.frame(data) m$... <- m$exclude <- m$drop.unused.levels <- NULL ## FIXME ... m$... <- m$exclude <- m$FUN <- NULL m[[1]] <- as.name("model.frame") mf <- eval(m, parent.frame()) if (length(formula) == 2) { by <- mf y <- NULL } else { i <- attr(attr(mf, "terms"), "response") by <- mf[-i] y <- mf[[i]] } by <- lapply(by, function(u) { if (!is.factor(u)) u <- factor(u, exclude = exclude) u[, drop = drop.unused.levels] }) x <- if (is.null(y)) do.call("table", by) else if (NCOL(y) == 1){ tapply(y, by, FUN) } else { z <- lapply(as.data.frame(y), tapply, by, FUN) array(unlist(z), dim = c(dim(z[[1]]), length(z)), dimnames = c(dimnames(z[[1]]), list(names(z)))) } x[is.na(x)] <- 0 class(x) <- c("xtabs", "table") attr(x, "call") <- match.call() x value <- x return(value) } ###################################################################### doBy/R/data_binomial_to_bernoulli_data.r0000644000176200001440000000510614605763311020074 0ustar liggesusers #' @title Convert binomial data to bernoulli data #' #' @description Convert binomial data to bernoulli data by expanding dataset. #' #' @param data. A dataframe #' @param y Column with 'successes' in binomial distribution `y~bin(size, p)` #' @param size Column with 'failures', i.e. size-y or 'total', i.e. size. #' @param type Whether `size` is rest (i.e. 'failures') or 'total' #' @param response_name Name of response variable in output dataset. #' @param rest_name Name of 'failures' in column `response_name`. #' #' @examples #' #' dat <- budworm #' dat <- dat[dat$dose %in% c(1,2), ] #' dat$ntotal <- 5 #' dat #' dat.a <- dat |> #' binomial_to_bernoulli_data(ndead, ntotal, type="total") #' dat.b <- dat |> #' dplyr::mutate(nalive=ntotal-ndead) |> dplyr::select(-ntotal) |> #' binomial_to_bernoulli_data(ndead, nalive, type="rest") #' #' m0 <- glm(cbind(ndead, ntotal-ndead) ~ dose + sex, data=dat, family=binomial()) #' m1 <- glm(ndead / ntotal ~ dose + sex, data=dat, weight=ntotal, family=binomial()) #' ma <- glm(response ~ dose + sex, data=dat.a, family=binomial()) #' mb <- glm(response ~ dose + sex, data=dat.b, family=binomial()) #' #' dat.a$response #' dat.b$response ## Not same and therefore the following do not match #' #' all.equal(coef(m0), coef(ma)) #' all.equal(coef(m0), coef(mb)) #' all.equal(coef(m1), coef(ma)) #' all.equal(coef(m1), coef(mb)) #' @export binomial_to_bernoulli_data <- function(data., y, size, type=c("rest", "total"), response_name="response", rest_name=NULL){ type <- match.arg(type) yy <- rlang::enquo(y) |> rlang::as_name() ## dead xx <- rlang::enquo(size) |> rlang::as_name() ## total (or rest) if (is.null(rest_name)){ rest_name <- paste0("not_", yy) } `:=` = NULL if (identical(type, "total")){ data. <- data. |> mutate(!!sym(rest_name) := !!sym(xx) - !!sym(yy)) |> select(-!!sym(xx)) tidy_data <- data. |> tidyr::pivot_longer(c(!!sym(yy), !!sym(rest_name)), names_to = response_name, values_to = "n") } else { tidy_data <- data. |> tidyr::pivot_longer(c(!!sym(yy), !!sym(xx)), names_to = response_name, values_to = "n") } out <- tidy_data |> tidyr::uncount(n) resp <- out[,response_name] |> pull() lev <- rev(c(yy, setdiff(unique(resp), yy))) out[,response_name] <- out[,response_name] |> pull() |> factor(levels=lev) return(out) } doBy/R/by_transformBy.R0000644000176200001440000000460014767112321014477 0ustar liggesusers############################################################################### #' #' @title Function to make groupwise transformations #' @description Function to make groupwise transformations of data by applying #' the transform function to subsets of data. #' @name by-transform #' ############################################################################### #' #' @details The ... arguments are tagged vector expressions, which are evaluated #' in the data frame data. The tags are matched against names(data), and for #' those that match, the value replace the corresponding variable in data, #' and the others are appended to data. #' #' @param formula A formula with only a right hand side, see examples below #' @param data A data frame #' @param \dots Further arguments of the form tag=value #' @return The modified value of the dataframe data. #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' #' @seealso \code{\link{orderBy}}, \code{\link{order_by}}, \code{\link{summaryBy}}, \code{\link{summary_by}}, #' \code{\link{splitBy}}, \code{\link{split_by}} #' @keywords univar #' @examples #' #' data(dietox) #' transformBy(~Pig, data=dietox, minW=min(Weight), maxW=max(Weight), #' gain=diff(range(Weight))) #' #' @export #' @rdname by-transform transform_by <- function (data, formula, ...) { cl <- match.call(expand.dots = TRUE) cl[[2]] <- formula cl[[3]] <- data names(cl)[2:3] <- c("formula", "data") cl[[1]] <- as.name("transformBy") eval(cl) } #' @export #' @rdname by-transform transformBy <- function (formula, data, ...) { if (!inherits(data, "tbl_df")) is.tib = FALSE else {is.tib = TRUE; data = as.data.frame(data)} transform2<- function (data, ...) { e <- eval(substitute(list(...)), data, parent.frame()) tags <- names(e) inx <- match(tags, names(data)) matched <- !is.na(inx) if (any(matched)) { data[inx[matched]] <- e[matched] data <- data.frame(data) } if (!all(matched)) { for (i in 1:length(e)) data[, names(e)[i]] <- e[i] } return(data) } ddd <- splitBy(formula, data=data, omit=TRUE) ee <- lapply(ddd, function(d){ transform2(d, ...) }) out <- do.call("rbind", ee) rownames(out) <- NULL if (is.tib) as_tibble(out) else out ##out } doBy/R/linest_utilities.R0000644000176200001440000000302714746511661015101 0ustar liggesusers #' @title Determines if contrasts are estimable. #' #' @description Determines if contrasts are estimable, that is, if the contrasts #' can be written as a linear function of the data. #' #' @name is_estimable #' #' @details Consider the setting \eqn{E(Y)=Xb}. A linear function of \eqn{b}, #' say \eqn{l'b} is estimable if and only if there exists an \eqn{r} such #' that \eqn{r'X=l'} or equivalently \eqn{l=X'r}. Hence \eqn{l} must be in #' the column space of \eqn{X'}, i.e. in the orthogonal complement of the #' null space of \eqn{X}. Hence, with a basis \eqn{B} for the null space, #' \code{is_estimable()} checks if each row \eqn{l} of the matrix \eqn{K} is #' perpendicular to each column basis vector in \eqn{B}. #' #' @param K A matrix. #' @param null.basis A basis for a null space (can be found with #' \code{null_basis()}). #' @return A logical vector. #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @references \url{http://web.mit.edu/18.06/www/Essays/newpaper_ver3.pdf} #' @keywords utilities #' #' @export is_estimable is_estimable <- function(K, null.basis){ if (is.null(null.basis) || (is.matrix(null.basis) && ncol(null.basis)==1) ){ rep(TRUE, nrow(K)) } else { out <- lapply(1:nrow(K), function(i){ k <- K[i,] all(abs(apply(null.basis, 2, function(x) sum(k * x))) < 1e-04) }) unlist( out ) } } null_basis <- function(M){ MASS::Null(t(M)) } doBy/R/vtools.R0000644000176200001440000000607515024601303013023 0ustar liggesusers#' Variable utilities: parse, select, check, map, rename #' #' These functions provide flexible tools for parsing and managing variable names #' from formulas, unquoted names, or character vectors. Demonstrated using `CO2` dataset. #' @param ... Variable input (unquoted, character vector, or formula) #' @importFrom rlang enquos eval_tidy as_name abort is_formula f_rhs quo_get_expr #' @export vparse <- function(...) { dots <- enquos(...) if (length(dots) > 1) { # Case: multiple unquoted inputs return(unname(sapply(dots, as_name))) } # Single input q <- dots[[1]] expr <- quo_get_expr(q) # Case 1: formula (~Treatment + Type) if (is_formula(expr)) { return(all.vars(f_rhs(expr))) } # Case 2: single unquoted variable (symbol) if (is.symbol(expr)) { return(as_name(expr)) } # Case 3: character vector val <- eval_tidy(q) if (is.character(val)) { return(val) } abort("Invalid input to vparse: expected unquoted names, a character vector, or a formula.") } #' Shorthand for vparse() #' #' A short and convenient alias for `vparse()`. Accepts unquoted names, character vectors, or a formula. #' #' @param ... Variable input in any accepted `vparse()` form #' #' @return A character vector of variable names #' @export v <- function(...) { vparse(...) } #' Select columns from a data frame using flexible input #' #' @param df A data frame #' @param ... Variable input (unquoted, character vector, or formula) #' @return A data frame with selected columns #' @export vselect <- function(df, ...) { vars <- vparse(...) df[, vars, drop = FALSE] } #' Check if variables exist in a data frame #' #' @param df A data frame #' @param ... Variables to check #' @return TRUE if all variables exist, otherwise error #' @export vcheck <- function(df, ...) { vars <- vparse(...) missing <- vars[!vars %in% names(df)] if (length(missing) > 0) { abort(paste("Missing variables in data frame:", paste(missing, collapse = ", "))) } TRUE } #' Apply a function to each parsed variable name #' #' @param .vars Variables to parse #' @param .f Function to apply to each name #' @return A list of results #' @export vmap <- function(.vars, .f) { q <- enquo(.vars) vars <- vparse(!!q) lapply(vars, .f) } #' Rename columns in a data frame #' #' @param df A data frame #' @param rename_map A named character vector (old_name = new_name) #' @return A data frame with renamed columns #' @export vrename <- function(df, rename_map) { stopifnot(is.character(rename_map), !is.null(names(rename_map))) old_names <- names(rename_map) new_names <- unname(rename_map) matched <- match(old_names, names(df)) if (any(is.na(matched))) { abort("Some names in rename_map do not exist in the data frame.") } names(df)[matched] <- new_names df } # Example usage with CO2 dataset #' @examples #' data(CO2) #' vparse(Treatment, Type) #' vparse(c("Treatment", "Type")) #' vparse(~Treatment + Type) #' vselect(CO2, Treatment, Type) #' vcheck(CO2, ~Treatment + Type) #' vmap(~Treatment + Type, toupper) #' vrename(CO2, c(Treatment = "Trt", Type = "Group")) doBy/R/by_subsetBy.R0000644000176200001440000000534314551451413013775 0ustar liggesusers############################################################################### #' #' @title Finds subsets of a dataframe which is split by variables in #' a formula. #' @description A data frame is split by a formula into groups. Then #' subsets are found within each group, and the result is #' collected into a data frame. #' @name by-subset #' ############################################################################### #' @param formula A right hand sided formula or a character vector of #' variables to split by. #' @param subset logical expression indicating elements or rows to #' keep: missing values are taken as false. #' @param data A data frame. #' @param select expression, indicating columns to select from a data #' frame. #' @param drop passed on to \code{[} indexing operator. #' @param join If FALSE the result is a list of data frames (as #' defined by 'formula'); if TRUE one data frame is returned. #' @param \dots further arguments to be passed to or from other methods. #' @return A data frame. #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @seealso \code{\link{splitBy}}, \code{\link{split_by}} #' @keywords utilities #' @examples #' #' data(dietox) #' subsetBy(~Evit, Weight < mean(Weight), data=dietox) #' @export #' @rdname by-subset subset_by <- function(data, formula, subset, select, drop=FALSE, join=TRUE, ...){ cl <- match.call(expand.dots = TRUE) cl[[2]] <- formula cl[[3]] <- cl[[4]] # writing 'subset' fails because evaluation is tried cl[[4]] <- data names(cl)[2:4] <- c("formula", "subset", "data") cl[[1]] <- as.name("subsetBy") eval(cl) } #' @export #' @rdname by-subset subsetBy <- function(formula, subset, data=parent.frame(), select, drop=FALSE, join=TRUE, ...){ out <- splitBy(formula, data=data) subsetMissing <- missing(subset) selectMissing <- missing(select) e <- substitute(subset) out <- lapply(out, function(x){ if (subsetMissing) r <- TRUE else { r <- eval(e, x, parent.frame()) if (!is.logical(r)) stop("'subset' must evaluate to logical") r <- r & !is.na(r) } if (selectMissing) vars <- TRUE else { nl <- as.list(1:ncol(x)) names(nl) <- names(x) vars <- eval(substitute(select), nl, parent.frame()) } x[r, vars, drop = drop] } ) if (join) do.call("rbind", out) else out } doBy/R/by_summaryBy.R0000644000176200001440000004154215030316233014157 0ustar liggesusers#' @title Groupwise summary statistics #' #' @description Computes summary statistics by groups, similar to the `summary` procedure in SAS. #' A more flexible alternative to base R's \code{\link[stats]{aggregate}}. #' #' @name by-summary #' @param formula A formula specifying response and grouping variables. #' @param data A data frame. #' @param FUN A function or list of functions to apply to the response variables. #' @param id A formula indicating variables to retain (not grouped by). #' @param keep.names Logical; keep original variable names if only one function is applied. #' @param p2d Replace parentheses in output names with dots? #' @param order Logical; should result be ordered by grouping variables? #' @param full.dimension Logical; if TRUE, repeat rows so output matches input size. #' @param var.names Optional custom names for response variables. #' @param fun.names Optional custom names for functions applied. #' @param ... Additional arguments passed to functions in `FUN`. #' #' @return A data frame of grouped summary statistics. #' #' @details #' Extra arguments in `...` are passed to all functions in `FUN`. If needed, wrap functions to handle these consistently (e.g., for `na.rm = TRUE`). #' #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @seealso \code{\link[stats]{aggregate}}, \code{\link{orderBy}}, \code{\link{transformBy}}, \code{\link{splitBy}} #' @keywords summary grouping #' #' @examples #' data(CO2) #' #' # Simple groupwise mean #' summaryBy(uptake ~ Type + Treatment, data = CO2, FUN = mean) #' summaryBy(cbind(uptake, conc) ~ Type + Treatment, data = CO2, FUN = mean) #' #' # Compare with #' aggregate(cbind(uptake, conc) ~ Type + Treatment, data = CO2, FUN = mean) #' #' ## Using '.' on the right hand side of a formula means to stratify by #' ## all variables not used elsewhere: #' summaryBy(uptake ~ ., data = CO2, FUN = mean) #' #' # Multiple functions using a custom summary function #' myfun <- function(x, ...) #' c(m = mean(x, na.rm = TRUE), v = var(x, na.rm = TRUE), n = length(x)) #' summaryBy(uptake ~ Type + Treatment, data = CO2, FUN = myfun) #' #' # Summary on transformed variables #' # works: #' summaryBy(cbind(lu=log(uptake), conc) ~ Type, data = CO2, FUN = mean) #' # fails: #' #summaryBy(cbind(log(uptake), conc) ~ Type, data = CO2, FUN = mean) #' @export #' @rdname by-summary summary_by <- function(data, formula, id=NULL, FUN=mean, keep.names=FALSE, p2d=FALSE, order=TRUE, full.dimension=FALSE, var.names=NULL, fun.names=NULL, ...){ cl <- match.call(expand.dots = TRUE) cl[[2]] <- formula cl[[3]] <- data names(cl)[2:3] <- c("formula", "data") cl[[1]] <- as.name("summaryBy") eval(cl) } #' @export #' @rdname by-summary summaryBy <- function (formula, data=parent.frame(), id=NULL, FUN=mean, keep.names=FALSE, p2d=FALSE, order=TRUE, full.dimension=FALSE, var.names=NULL, fun.names=NULL, ...){ if (!inherits(data, "tbl_df")) is.tib = FALSE else {is.tib = TRUE; data = as.data.frame(data)} debug.info <- 0 zzz <- .get_variables(formula, data, id, debug.info) ## ; str(zzz) lhs.num <- zzz$lhs.num rhs.grp <- zzz$rhs.grp ids.var <- zzz$form.ids.var ## str(list(lhs.num=lhs.num)) rh.trivial <- length( rhs.grp ) == 0 #; cat(sprintf("rh.trivial=%d\n", rh.trivial)) rh.string <- .get_rhs_string( data, rhs.grp ) rh.unique <- unique(rh.string) rh.idx <- match(rh.unique, rh.string) rh.string.factor <- factor(rh.string, levels=rh.unique) ## This is important ### Get data for id.vars; use ids.var, data, rh.idx if (length(ids.var)>0){ id.data <- data[ rh.idx, ids.var, drop=FALSE ] ##; print(id.data) } ### Get lhs data; use lhs.num, data ## Below, we need lhs in the form cbind(y1, y2, y3) for further computations. ## ## It is OK to have computations in this form a la cbind(y1, y2, ## y3, y4=y1+y2, y5=log(y2)). ## ## If lhs is y1 this is translated into cbind(y1). ## ## If lhs is ., this is translated into c(y1, y2, y3). ## ## It is also so (alas) that one may write y1 + y2 + y3, ## which is also translated into c(y1, y2, y3) if (length(lhs.num) > 1) { ## If lhs.num is vector of length > 1, wrap with cbind: lhs.num <- paste0("cbind( ", toString(lhs.num), " )") } else { ## strip cbind( ... ) if it is there; then put cbind around ## (if lhs is simply y1, then there is no cbind around). ff2 <- gsub("^cbind\\((.*)\\)$", "\\1", lhs.num) lhs.num <- paste0("cbind( ", ff2, " )") } ## aa <- lapply(paste(lhs.num), function(x)eval(parse(text=x), data)) ## lh.data <- do.call(cbind, aa) ## replace two lines above with lh.data <- eval(parse(text=lhs.num), data) ## Hack: redefine lhs.num : name of variables in data frame lhs.num <- colnames(lh.data) ##colnames(lh.data) <- lhs.num ### Function names; use FUN funNames <- .get_fun_names( FUN ) ##print(rh.string.factor) ### Calculate groupwise statistics if (!is.list(FUN)) FUN <- list(FUN) out <- NULL for (ff in 1:length(FUN)) { ## loop over functions ##currFUN <- FUN[[ff]] currFUN <- match.fun( FUN[[ff]] ) for (vv in 1:length(lhs.num)) { ## loop over variables currVAR <- lh.data[,lhs.num[vv]] zzz <- tapply(currVAR, rh.string.factor, function(x){ currFUN(x, ...) }, simplify=FALSE) zzz <- do.call(rbind, zzz) out <- cbind(out, zzz) } } if (!is.null(var.names) && length(var.names)==length(lhs.num)) lhs.names <- var.names else lhs.names <- lhs.num ### Set names for columns ##print(funNames) if (!is.null(fun.names) ) ##&& length(fun.names)==length(funNames)) funNames <- fun.names newnames <- .get_col_names(ncol(out), colnames(out), funNames, lhs.names, keep.names) ##cat(sprintf("newnames = %s\n", toString( newnames ))) colnames(out) <- newnames out <- as.data.frame(out) ### Pad the rhs data to the result if (!rh.trivial){ out <- cbind(data[rh.idx, rhs.grp, drop=FALSE], out) } ### Pad id.data to result ##print(id.data) if (length(ids.var)>0){ out <- cbind(out, id.data) } ### Must the result have full dimension? if (full.dimension){ rrr <- as.numeric(rh.string.factor) out <- out[rrr,, drop=FALSE] } ### Order the result by the rhs if (order && !rh.trivial){ rhs.string <- paste (rhs.grp, collapse='+') out <- orderBy(as.formula(paste("~", rhs.string)), data=out) } ### Replace '('s and ')'s with '.'s if (p2d) names(out) <- gsub("\\)","\\.", gsub("\\(","\\.",names(out))) ### Finalize rownames(out) <- 1:nrow(out) if (length(unique(names(out))) != length(names(out))) warning("dataframe contains replicate names \n", call.=FALSE) if (is.tib) as_tibble(out) else out ## out } .get_rhs_string <- function(data, rhs.var, sep.string="@"){ if (length(rhs.var)==0){ rep.int("1", nrow(data)) } else { rh.string <- paste(data[,rhs.var[1]]) if (length( rhs.var ) > 1){ for (ii in 2:length( rhs.var )){ rh.string <- paste(rh.string, sep.string, data[, rhs.var[ii]], sep='') } } rh.string } } .get_fun_names <- function( FUN ){ if (!is.list(FUN)) funNames <- paste(deparse(substitute(FUN, env=parent.frame())), collapse = " ") else funNames <- unlist(lapply(substitute(FUN, env=parent.frame())[-1], function(a) paste(a))) ##cat(sprintf("funNames = %s\n", toString(funNames))) funNames } .get_col_names <- function(ncol.ans, colNames, funNames, lhs.num, keep.names){ ### Names for new variables ## Does the columns of ans have names?? oldnames <- colNames #colnames(ans) if (is.null(oldnames)) hasNames <- 0 else { hasNames <- 1*(prod(nchar(oldnames))>0) } ##cat(sprintf("hasNames=%i\n", hasNames)) ## Dim of response (per variable on LHS) dimr <- (ncol.ans)/length(lhs.num) only.one.response <- (ncol.ans==length(lhs.num)) if ( keep.names && only.one.response ){ newnames <- lhs.num } else { if (hasNames>0){ ## newnames = var.fun funNames <- colNames[1:dimr] newnames <- unlist(lapply(lhs.num, function(v){paste(v, funNames, sep='.')})) } else { if (length(funNames) != dimr){ funNames <- paste("FUN", 1:dimr, sep='') newnames <- unlist(lapply(lhs.num, function(v){paste(v, funNames, sep='.')})) } else { newnames <- unlist(lapply(funNames, function(x) paste(lhs.num, x, sep='.'))) } if (length(newnames)!=ncol.ans){ funNames <- paste(funNames, 1:dimr, sep=".") newnames <- unlist(lapply(funNames, function(x) paste(lhs.num, x, sep='.'))) } } } newnames } .get_variables <- function(formula, data, id, debug.info){ data.var <- names(data) if (!inherits(formula, c("formula", "list"))) stop("'formula' must be a formula or a list") if (inherits(formula, "formula")){ if (length(formula) != 3) stop("Formula must have a left hand side") rhs <- formula[[3]] form.rhs.var <- all.vars(rhs) ## May contain "." and "1" lhs <- formula[[2]] form.lhs.var <- all.vars(lhs) ## May contain "." #print(form.lhs.var) zz <- .lhsParse(lhs) form.lhs.var <- if (length(zz)==1) paste(zz) else paste(unlist(.lhsParse(lhs))) ##print(form.lhs.var) } else { if (length(formula)>=2){ lhs <- formula[[1]] rhs <- formula[[2]] form.lhs.var <- lhs form.rhs.var <- rhs } else { stop("Invalid specification of formula") } } if (is.null(id)){ form.ids.var <- character(0) } else { if (!inherits(id, c("formula", "character"))){ stop("'id' must be a formula or a character vector") } if (inherits(id, "formula")){ form.ids.var <- all.vars(id) } else { form.ids.var <- id } } data.cls <- lapply(data, class) data.num.idx <- data.cls %in% c("numeric","integer") data.num.var <- data.var[ data.num.idx ] data.fac.var <- data.var[ !data.num.idx ] ## print(form.lhs.var) ## print(data.num.var) lhs.num <- intersect( form.lhs.var, data.num.var ) rhs.num <- intersect( form.rhs.var, data.num.var ) ids.num <- intersect( form.ids.var, data.num.var ) lhs.fac <- intersect( form.lhs.var, data.fac.var ) rhs.fac <- intersect( form.rhs.var, data.fac.var ) ids.fac <- intersect( form.ids.var, data.fac.var ) lll <- list(data.var=data.var, form.lhs.var=form.lhs.var, form.rhs.var=form.rhs.var, form.ids.var=form.ids.var, lhs.num=lhs.num, rhs.num=rhs.num, ids.num=ids.num, lhs.fac=lhs.fac, rhs.fac=rhs.fac, ids.fac=ids.fac ) #if (debug.info>=1) ##{ cat("status:\n"); str(lll, vec.len=20) } if ( "." %in% form.lhs.var ){ ## need all numeric variables not metioned elswhere on lhs form.lhs.var <- setdiff(form.lhs.var, ".") lhs.num <- union( form.lhs.var, setdiff(data.num.var, c(rhs.num, ids.num))) if ( length( lhs.fac ) > 0 ){ isSpecial <- rep(NA, length( lhs.fac )) for (j in 1:length(lhs.fac)){ isSpecial[j]<- (class(data[,lhs.fac[j]])[1] %in% c("POSIXt", "Date")) } lhs.num <- union( lhs.num, lhs.fac[ isSpecial ] ) } } else { lhs.num <- form.lhs.var } ## The grouping variable if ("." %in% form.rhs.var){ ## need all factors not mentioned elsewhere as grouping factors free.fac <- setdiff(data.fac.var, c(lhs.fac, ids.fac)) rhs.grp <- c(setdiff(form.rhs.var, "."), free.fac) } else { rhs.grp <- form.rhs.var } rhs.grp <- intersect( rhs.grp, data.var ) rrr <- list(lhs.num=lhs.num, rhs.fac=rhs.fac, form.ids.var=form.ids.var, form.rhs.var=form.rhs.var, rhs.grp=rhs.grp) ##str(rrr) rrr } .lhsParse <- function(x){ ##cat(".lhsParse:"); print(x); print(class(x)) if (inherits(x, 'name')){ value <- x } else { s <- paste(x[[1]]) value <- switch(s, '+'={ c(.lhsParse(x[[2]]),.lhsParse(x[[3]]))}, 'I'={ x[[2]]}, { deparse(x)}) } value } ## ' ## ' data(dietox) ## ' dietox12 <- subset(dietox,Time==12) ## ' ## ' fun <- function(x){ ## ' c(m=mean(x), v=var(x), n=length(x)) ## ' } ## ' ## ' summaryBy(cbind(Weight, Feed) ~ Evit + Cu, data=dietox12, ## ' FUN=fun) ## ' ## ' summaryBy(list(c("Weight", "Feed"), c("Evit", "Cu")), data=dietox12, ## ' FUN=fun) ## ' ## ' ## Computations on several variables is done using cbind( ) ## ' summaryBy(cbind(Weight, Feed) ~ Evit + Cu, data=subset(dietox, Time > 1), ## ' FUN=fun) ## ' ## ' ## Calculations on transformed data is possible using cbind( ), but ## ' # the transformed variables must be named ## ' ## ' summaryBy(cbind(lw=log(Weight), Feed) ~ Evit + Cu, data=dietox12, FUN=mean) ## ' ## ' ## There are missing values in the 'airquality' data, so we remove these ## ' ## before calculating mean and variance with 'na.rm=TRUE'. However the ## ' ## length function does not accept any such argument. Hence we get ## ' ## around this by defining our own summary function in which length is ## ' ## not supplied with this argument while mean and var are: ## ' ## ' sumfun <- function(x, ...){ ## ' c(m=mean(x, na.rm=TRUE, ...), v=var(x, na.rm=TRUE, ...), l=length(x)) ## ' } ## ' summaryBy(cbind(Ozone, Solar.R) ~ Month, data=airquality, FUN=sumfun) ## ' ## Compare with ## ' aggregate(cbind(Ozone, Solar.R) ~ Month, data=airquality, FUN=sumfun) ## ' ## ' ## Using '.' on the right hand side of a formula means to stratify by ## ' ## all variables not used elsewhere: ## ' ## ' data(warpbreaks) ## ' summaryBy(breaks ~ wool + tension, warpbreaks, FUN=mean) ## ' summaryBy(breaks ~ ., warpbreaks, FUN=mean) ## ' summaryBy(. ~ wool + tension, warpbreaks, FUN=mean) ## ' ## ' summaryBy(. ~ wool + tension, warpbreaks, FUN=mean) ## ' ## ' ## ' ## ' @title Function to calculate groupwise summary statistics ## ' @description Function to calculate groupwise summary statistics, ## ' much like the summary procedure of SAS ## ' @name by-summary ## ' ## ' @details Extra arguments (...) are passed onto the functions in ## ' FUN. Hence care must be taken that all functions in FUN accept ## ' these arguments - OR one can explicitly write a functions which ## ' get around this. This can particularly be an issue in ## ' connection with handling NAs. See examples below. Some code ## ' for this function has been suggested by Jim ## ' Robison-Cox. Thanks. ## ' ## ' @param formula A formula object, see examples below. ## ' @param data A data frame. ## ' @param FUN A list of functions to be applied, see examples below. ## ' @param id A formula specifying variables which data are not grouped by but ## ' which should appear in the output. See examples below. ## ' @param keep.names If TRUE and if there is only ONE function in FUN, then the ## ' variables in the output will have the same name as the variables in the ## ' input, see 'examples'. ## ' @param p2d Should parentheses in output variable names be replaced by dots? ## ' @param order Should the resulting dataframe be ordered according to the ## ' variables on the right hand side of the formula? (using \link{orderBy} ## ' @param full.dimension If TRUE then rows of summary statistics are repeated ## ' such that the result will have the same number of rows as the input ## ' dataset. ## ' @param var.names Option for user to specify the names of the variables on the ## ' left hand side. ## ' @param fun.names Option for user to specify function names to apply to the ## ' variables on the left hand side. ## ' @param \dots Additional arguments to FUN. This could for example be NA actions. ## ' @return A dataframe. ## ' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} ## ' @seealso \code{\link{ave}}, \code{\link{descStat}}, \code{\link{orderBy}}, \code{\link{order_by}}, ## ' \code{\link{splitBy}}, \code{\link{split_by}}, \code{\link{transformBy}}, \code{\link{transform_by}} ## ' @keywords univar doBy/R/data_rbind_list.R0000644000176200001440000000342515002443106014614 0ustar liggesusers#' Bind list of data frames and add list names as a column #' #' @description #' Binds a named list of data frames (or tibbles) into a single data frame. #' Adds the list name as a new column (first column). #' #' @param lst A named list of data frames or tibbles. #' @param name A character scalar: name of the column to hold the list names (default "name"). #' #' @return A data frame or tibble, depending on the class of the input. #' #' @examples #' lst <- list(a = data.frame(x = 1:2), b = data.frame(x = 3:4)) #' rbind_list(lst) #' #' lst <- split(iris, iris$Species) #' rbind_list(lst) #' #' #' @export rbind_list <- function(lst, name = "name") { stopifnot( is.list(lst), !is.null(names(lst)), all(nzchar(names(lst))), is.character(name), length(name) == 1 ) use_tibble <- requireNamespace("tibble", quietly = TRUE) && any(vapply(lst, inherits, logical(1), "tbl_df")) out <- Map(function(df, nm) { df <- as.data.frame(df) # ensures even tibbles behave nicely df[[name]] <- nm df[, c(name, setdiff(names(df), name)), drop = FALSE] }, lst, names(lst)) result <- do.call(rbind, out) row.names(result) <- NULL if (use_tibble) { result <- tibble::as_tibble(result) } result } ## Navn | Fordel | Ulempe ## rbind_list | Beskrivende, enkel, matcher base R-logik | En smule generisk ## stack_list | Mere "verbalt" og beskriver handlingen | Kan forveksles med stack() ## combine_list | Letforståeligt, signalerer sammensmeltning | Ikke specifikt om rbind ## bind_rows_list | Tydeligt og i stil med dplyr::bind_rows() | Længere navn ## join_list | Knap så præcist – lyder som en slags merge | Forvirrende i statistisk kontekst ## collapse_list | Tydeligt at det handler om at lave én ud af mange | Kan tolkes som vektor-collapse doBy/R/by_sampleBy.R0000644000176200001440000000365514551451413013755 0ustar liggesusers######################################################################### #' @title Sampling from a data frame #' @description A data frame is split according to some variables in a #' formula, and a sample of a certain fraction of each is drawn. #' @name by-sample ######################################################################### #' @details If systematic=FALSE (default) then frac gives the fraction #' of data sampled. If systematic=TRUE and frac=.2 then every 1/.2 #' i.e. every 5th observation is taken out. #' #' @param formula A formula defining the grouping of the data frame. #' @param frac The part of data to be sampled. #' @param replace Is the sampling with replacement. #' @param data A data frame. #' @param systematic Should sampling be systematic. #' #' @return A dataframe. #' @seealso \code{\link{orderBy}}, \code{\link{order_by}}, #' \code{\link{splitBy}}, \code{\link{split_by}}, #' \code{\link{summaryBy}}, \code{\link{summary_by}}, #' \code{\link{transformBy}}, \code{\link{transform_by}} #' #' @keywords utilities #' #' @examples #' data(dietox) #' sampleBy(formula = ~ Evit + Cu, frac=.1, data = dietox) #' @export #' @rdname by-sample sample_by <- function(data, formula, frac=0.1, replace=FALSE, systematic=FALSE){ arg <- list(formula=formula, frac=frac, replace=replace, data=data, systematic=systematic) do.call(sampleBy, arg) } #' @export #' @rdname by-sample sampleBy <- function(formula, frac=0.1, replace=FALSE, data=parent.frame(), systematic=FALSE){ out <- splitBy(formula, data=data) out <- lapply(out, function(dat){ if (systematic){ idx <- seq(1, nrow(dat), 1 / frac) } else { idx <- sort(sample(1:nrow(dat), size=round(frac * nrow(dat)), replace=replace)) } dat[idx, ] }) out <- do.call("rbind", out) out } doBy/R/by_splitBy.R0000644000176200001440000001350415024542051013614 0ustar liggesusers########################################################################### #' #' @title Split a data frame into groups defined by variable(s) #' #' @description Split a dataframe according to the levels of variables #' in the dataframe. Uses vparse() to interpret flexible input. #' @name by-split #' #' @param data. A data frame (or tibble) to split #' @param ... Variables defining the groups #' @param omit If TRUE (default), group-defining variables are omitted in each split group #' @param formula A right hand sided formula (for the old interface) #' @param data A data frame (for the old interface) #' #' @return An object of class \"splitByData\" (a named list with group attributes) #' @export #' #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @seealso \code{\link{orderBy}}, \code{\link{order_by}}, #' \code{\link{summaryBy}}, \code{\link{summary_by}}, #' \code{\link{transformBy}}, \code{\link{transform_by}} #' @keywords utilities #' @examples #' #' split_by(CO2, ~Treatment+Type) #' split_by(CO2, Treatment, Type) #' split_by(CO2, c("Treatment", "Type")) #' split_by(CO2, "Treatment", "Type") #' #' x <- split_by(CO2, "Treatment", "Type") #' head(x, 3) #' tail(x, 3) #' #' ## Via wrapper: #' foo2 <- function(x) { #' x <- rlang::enquo(x) #' split_by(CO2, !!x) #' } #' foo2(~Treatment) #' #' ## The "Old" interface #' splitBy(~Treatment + Type, CO2) #' splitBy(~Treatment + Type, data=CO2) #' splitBy(c("Treatment", "Type"), data=CO2) #' #' @rdname by-split #' @export split_by <- function(data., ..., omit = TRUE) { rhs <- vparse(...) split_by_worker(data., rhs, omit = omit) } #' @export #' @rdname by-split splitBy <- function(formula, data, omit=TRUE){ if (inherits(formula, "formula")){ rhs <- all.vars(formula[[2]]) } else { rhs <- formula } split_by_worker(data, rhs, omit=omit) } split_by_worker <- function(data., rhs, omit=TRUE){ is.tib <- inherits(data., "tbl_df") if (is.tib) data. = as.data.frame(data.) grps <- apply(data.[, rhs, drop=FALSE], 1, paste0, collapse="|") ## print(grps) out <- split(data., grps) if (omit){ rhs.idx <- match(rhs, names(data.)) out <- lapply(out, function(d){ d[, -rhs.idx] }) } if (is.tib) { out <- lapply(out, function(d) { as_tibble(d) }) } groupid <- unique(data.[, rhs, drop=FALSE]) idxvec <- split(1:nrow(data.), grps) attr(out, "groupid") <- groupid attr(out, "idxvec") <- idxvec attr(out, "grps") <- grps class(out) <- c("splitByData", "list") return(out) } #' @export print.splitByData <- function(x, ...){ # print(attr(x,"groupid")) print(cbind(listentry=names(x), attr(x,"groupid"))) return(invisible(x)) } #' @importFrom utils head #' @param x A splitByData object. #' @param n An integer vector. #' @export #' @rdname by-split head.splitByData <- function(x, n=6L, ...){ lapply(x, head, n=n, ...) } #' @importFrom utils tail #' @export #' @rdname by-split tail.splitByData <- function(x, n=6L, ...){ lapply(x, tail, n=n, ...) } #' @export #' @rdname by-split #' @param drop Obsolete split_by.legacy <- function(data, formula, drop=TRUE){ arg <- list(formula=formula, data=data, drop=drop) do.call(splitBy.legacy, arg) } #' @export #' @rdname by-split splitBy.legacy <-function (formula, data = parent.frame(), drop=TRUE) { #, return.matrix=FALSE){ if (!inherits(data, "tbl_df")) is.tib = FALSE else {is.tib = TRUE; data = as.data.frame(data)} data.var <- names(data) if (!(inherits(formula, c("formula", "character")))) stop("'formula' must be a right hand sided formula or a character vector") if (inherits(formula, "formula")){ rhs <- formula[[2]] rhs.var <- all.vars(rhs) } else { rhs.var <- formula } cls <- lapply(data, class) factor.columns <- which(unlist(lapply(cls, function(x) any( x %in% "factor")))) #print(factor.columns) cls <- lapply(data, class) num.idx <- cls %in% c("numeric","integer") fac.var <- data.var[ !num.idx ] rhs.fac <- intersect( rhs.var, data.var ) if ("." %in% rhs.var){ ## need all factors not mentioned elsewhere as grouping factors rhs.fac <- union( fac.var, rhs.fac) } ## str(list(rhs.var=rhs.var, rhs.fac=rhs.fac, fac.var=fac.var)) rh.trivial <- length( rhs.var ) == 0 ## FIXME rhs.fac, rhs.var -- clean up!!! ## Use: data, rhs.fac, rh.trivial if ( rh.trivial ){ grps <- rep.int(1, nrow(data)) unique.grps <- 1 rh.idx <- 1 } else { grps <- .get_rhs_string( data, rhs.fac, sep.string="|") unique.grps <- unique(grps) rh.idx <- match(unique.grps, grps) } out_list <- vector("list", length(unique.grps)) names(out_list) <- unique.grps for (ii in 1:length(unique.grps)){ dd <- data[unique.grps[ ii ] == grps, ,drop=FALSE] if (drop && length(factor.columns)>0){ for (jj in 1:length(factor.columns)){ dd[, factor.columns[jj]] <- factor(dd[, factor.columns[jj]]) } } if (is.tib) dd <- as_tibble(dd) out_list[[ ii ]] <- dd } idxvec <- vector("list", length(unique.grps)) names(idxvec) <- unique.grps for (ii in 1:length(unique.grps)){ idxvec[[ii]] <- which(grps == unique.grps[ii]) } groupid <- data[rh.idx, rhs.fac, drop=FALSE] rownames(groupid) <- 1:nrow(groupid) attr(out_list,"groupid") <- groupid attr(out_list,"idxvec") <- idxvec attr(out_list,"grps") <- grps class(out_list) <- c("splitByData", "list") out_list } doBy/R/linest_LSmeans.R0000644000176200001440000001132714746514064014432 0ustar liggesusers## ############################################################################# #' #' @title Compute LS-means (aka population means or #' marginal means) #' @description LS-means (least squares means, also known as #' population means and as marginal means) for a range of model types. #' @name ls-means #' ## ############################################################################# #' #' @details There are restrictions on the formulas allowed in the model object. #' For example having \code{y ~ log(x)} will cause an error. Instead one #' must define the variable \code{logx = log(x)} and do \code{y ~ logx}. #' #' @aliases LSmeans LSmeans.default LSmeans.lmerMod popMeans #' popMeans.default popMeans.lmerMod #' #' @param object Model object #' @param effect A vector of variables. For each configuration of #' these the estimate will be calculated. #' @param at A list of values of covariates (including levels of some #' factors) to be used in the calculations #' @param level The level of the (asymptotic) confidence interval. #' @param adjust.df Should denominator degrees of freedom be adjusted? #' @param \dots Additional arguments; currently not used. #' #' @return A dataframe with results from computing the contrasts. #' #' @note \code{LSmeans} and \code{popMeans} are synonymous. #' #' @section Warning: Notice that \code{LSmeans} and \code{LE_matrix} #' fails if the model formula contains an offset (as one would #' have in connection with e.g. Poisson regression. #' #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @seealso \code{\link{LE_matrix}}, \code{\link{linest}} #' @keywords utilities #' @examples #' #' ## Two way anova: #' #' data(warpbreaks) #' #' m0 <- lm(breaks ~ wool + tension, data=warpbreaks) #' m1 <- lm(breaks ~ wool * tension, data=warpbreaks) #' LSmeans(m0) #' LSmeans(m1) #' #' ## same as: #' K <- LE_matrix(m0);K #' linest(m0, K) #' K <- LE_matrix(m1);K #' linest(m1, K) #' #' LE_matrix(m0, effect="wool") #' LSmeans(m0, effect="wool") #' #' LE_matrix(m1, effect="wool") #' LSmeans(m1, effect="wool") #' #' LE_matrix(m0, effect=c("wool", "tension")) #' LSmeans(m0, effect=c("wool", "tension")) #' #' LE_matrix(m1, effect=c("wool", "tension")) #' LSmeans(m1, effect=c("wool", "tension")) #' #' #' ## Regression; two parallel regression lines: #' #' data(Puromycin) #' #' m0 <- lm(rate ~ state + log(conc), data=Puromycin) #' ## Can not use LSmeans / LE_matrix here because of #' ## the log-transformation. Instead we must do: #' Puromycin$lconc <- log( Puromycin$conc ) #' m1 <- lm(rate ~ state + lconc, data=Puromycin) #' #' LE_matrix(m1) #' LSmeans(m1) #' #' LE_matrix(m1, effect="state") #' LSmeans(m1, effect="state") #' #' LE_matrix(m1, effect="state", at=list(lconc=3)) #' LSmeans(m1, effect="state", at=list(lconc=3)) #' #' ## Non estimable contrasts #' #' ## ## Make balanced dataset #' dat.bal <- expand.grid(list(AA=factor(1:2), BB=factor(1:3), #' CC=factor(1:3))) #' dat.bal$y <- rnorm(nrow(dat.bal)) #' #' ## ## Make unbalanced dataset #' # 'BB' is nested within 'CC' so BB=1 is only found when CC=1 #' # and BB=2,3 are found in each CC=2,3,4 #' dat.nst <- dat.bal #' dat.nst$CC <-factor(c(1, 1, 2, 2, 2, 2, 1, 1, 3, 3, #' 3, 3, 1, 1, 4, 4, 4, 4)) #' #' mod.bal <- lm(y ~ AA + BB * CC, data=dat.bal) #' mod.nst <- lm(y ~ AA + BB : CC, data=dat.nst) #' #' LSmeans(mod.bal, effect=c("BB", "CC")) #' LSmeans(mod.nst, effect=c("BB", "CC")) #' LSmeans(mod.nst, at=list(BB=1, CC=1)) #' #' LSmeans(mod.nst, at=list(BB=1, CC=2)) #' ## Above: NA's are correct; not an estimable function #' #' if( require( lme4 )){ #' warp.mm <- lmer(breaks ~ -1 + tension + (1|wool), data=warpbreaks) #' LSmeans(warp.mm, effect="tension") #' class(warp.mm) #' fixef(warp.mm) #' coef(summary(warp.mm)) #' vcov(warp.mm) #' if (require(pbkrtest)) #' vcovAdj(warp.mm) #' } #' #' LSmeans(warp.mm, effect="tension") #' #' @export LSmeans #' #' @export #' @rdname ls-means LSmeans <- function(object, effect=NULL, at=NULL, level=0.95, ...){ UseMethod("LSmeans") } #' @export #' @rdname ls-means LSmeans.default <- function(object, effect=NULL, at=NULL, level=0.95, ...){ K <- LE_matrix(object, effect=effect, at=at) out <- linest(object, K, level=level, ...) out } #' @export #' @rdname ls-means LSmeans.lmerMod <- function(object, effect=NULL, at=NULL, level=0.95, adjust.df=TRUE, ...){ K <- LE_matrix(object, effect=effect, at=at) out <- linest(object, K, level=level, adjust.df=adjust.df, ...) out } #' @export #' @rdname ls-means popMeans <- LSmeans #' @export #' @rdname ls-means popMeans.default <- LSmeans.default #' @export #' @rdname ls-means popMeans.lmerMod <- LSmeans.lmerMod doBy/R/by_lmBy.R0000644000176200001440000001056515030273362013100 0ustar liggesusers########################################################################## #' #' @title List of lm objects with a common model #' @description The data is split into strata according to the levels #' of the grouping factors and individual lm fits are obtained for #' each stratum. #' @name by-lmby #' ########################################################################## #' #' @aliases lmBy coef.lmBy coef.summary_lmBy summary.lmBy fitted.lmBy #' residuals.lmBy getBy #' @param formula. A linear model formula object of the form `y ~ x1 + #' ... + xn | g1 + ... + gm`. In the formula object, `y` #' represents the response, `x1, ..., xn` the covariates, and the #' grouping factors specifying the partitioning of the data #' according to which different lm fits should be performed. #' @param data. A dataframe #' @param id A formula describing variables from data which are to be #' available also in the output. #' @param \dots Additional arguments passed on to \code{lm()}. #' @return A list of lm fits. #' #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' #' @keywords models #' @examples #' #' lm_lst <- lmBy(1 / uptake ~ log(conc) | Treatment, data=CO2) #' coef(lm_lst) #' #' fitted(lm_lst) #' residuals(lm_lst) #' #' summary(lm_lst) #' coef(summary(lm_lst)) #' coef(summary(lm_lst), simplify=TRUE) #' #' @export #' @rdname by-lmby lm_by <- function (data., formula., id=NULL, ...) { cl <- match.call(expand.dots = TRUE) cl[[2]] <- formula. cl[[3]] <- data. names(cl)[2:3] <- c("formula.", "data.") cl[[1]] <- as.name("lmBy") eval(cl) } #' @export #' @rdname by-lmby lmBy <- function(formula., data., id=NULL, ...){ cl <- match.call() mff <- parseGroupFormula(formula.) group_data <- splitBy(mff$groupFormula, data=data., omit=FALSE) form <- mff$model model_list <- lapply(group_data, function(wd) { zzz <- lm(form, data=wd, ...) zzz$call[[2]] <- form zzz }) if (!is.null(id)){ id.vars <- unique(c(all.vars(id), all.vars(mff$groupFormula))) } else { id.vars <- all.vars(mff$groupFormula) } id.data <- do.call(rbind, lapply(group_data, function(wd) { wd[1, id.vars, drop=FALSE] })) attr(model_list, "call") <- cl attr(model_list, "dataList") <- group_data attr(model_list, "idData") <- id.data class(model_list) <- "lmBy" model_list } #' @export print.lmBy <- function(x, ...){ ##lapply(c(x), print) print(c(x)) return(invisible(x)) } #' @export summary.lmBy <- function(object, ...){ res <- lapply(object, summary) class(res) <- "summary_lmBy" res } #' @export print.summary_lmBy <- function(x, ...){ lapply(x, print) invisible(x) } #' @export coef.summary_lmBy <- function(object, simplify=FALSE, ...){ ans <- lapply(object, coef) if (simplify){ cc <- do.call(rbind, ans) cn <- colnames(cc) rn <- rownames(cc) nn <- names(ans) rn <- rownames(ans[[1]]) ff <- factor(rep(nn, each=length(rn))) rownames(cc) <- NULL ans <- data.frame(ff, rn, as.data.frame(cc)) colnames(ans) <- c("stratum", "parameter", cn) } ans } #' @export getBy <- function(object, name=c()){ if (missing(name)) stop("'name' must not be missing") if (inherits(object, "lmBy")) { ii <- match(name, c("dataList", "idData")) if (is.na(ii)) stop(sprintf("%s not available", name)) attr(object,name) } } #' @export coef.lmBy <- function(object, augment=FALSE, ...){ ans <- do.call(rbind, lapply(object, coef)) if (augment){ ans <- cbind(ans, getBy(object, "idData")) } ans } #' @export sigma.lmBy <- function(object, ...){ out <- sapply(object, sigma, ...) return(out) } #' @export fitted.lmBy <- function(object, augment=FALSE, ...){ ans <- lapply(object, fitted) if (augment) { ans <- mapply(function(a,b){ data.frame(.fit=a,b) }, ans, getBy(object, "dataList"), SIMPLIFY=FALSE) } ans } #' @export residuals.lmBy <- function(object, augment=FALSE, ...){ ans <- lapply(object, residuals) if (augment) { ans <- mapply(function(a,b){ data.frame(.fit=a,b) }, ans, getBy(object, "dataList"), SIMPLIFY=FALSE) } ans } doBy/R/linest_tidy.R0000644000176200001440000000330614634655044014037 0ustar liggesusers## Note to self: The 'modern' packages tibble and broom are only used ## in this file #' @title Tidy a linest object #' @description Tidy summarizes information about the components of the object. #' @name tidy-linest #' #' @param x A 'linest_class' object (produced by \code{linest} methods). #' @param conf.int Should confidence intervals be added. #' @param conf.level Desired confidence level. #' @param \dots Additional arguments; currently not used. #' @export tidy.linest_class <- function(x, conf.int = FALSE, conf.level = 0.95, ...) { co <- stats::coef(x) rownames(co) <- NULL if (ncol(co) == 5) { ## There are degrees of freedom in the output. co <- co[,c(1,2,3,5,4)] nn <- c("estimate", "std.error", "statistic", "p.value", "df") } else nn <- c("estimate", "std.error", "statistic", "p.value") names(co) <- nn if (conf.int) { ci <- .ci_fun(co, level=conf.level) colnames(ci) <- c("conf.low", "conf.high") co <- cbind(co, ci) } as_tibble(co) } #' @title Tidy an esticon object #' @description Tidy summarizes information about the components of the object. #' @name tidy-esticon #' #' @param x A 'esticon_class' object (produced by \code{esticon} methods). #' @param conf.int Should confidence intervals be added. #' @param conf.level Desired confidence level. #' @param \dots Additional arguments; currently not used. #' @export tidy.esticon_class <- function(x, conf.int = FALSE, conf.level = 0.95, ...) { co <- x[,1:6] rownames(co) <- NULL if (conf.int){ ci <- confint(x, level=conf.level) colnames(ci) <- c("conf.low", "conf.high") co <- cbind(co, ci) } as_tibble(co) } doBy/R/doby_utilities.R0000644000176200001440000002550315015013443014524 0ustar liggesusers#' @title Extract components from a formula with "conditioning bar" #' #' @description Extract components from a formula with the form #' \code{y ~ x1 + ... + xn | g1 + ... + gm} #' #' @param form A formula of the form \code{y ~ x1 + ... + xn | g1 + ... + gm} #' @return If the formula is \code{y ~ x1 + x2 | g1 + g2} the result is #' \item{model}{\code{y ~ x1 + x2}} \item{groups}{\code{ g1 + g2}} #' \item{groupFormula}{\code{~ g1 + g2}} #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @keywords utilities #' @examples #' #' gf1 <- parseGroupFormula(y ~ x1 + x2 | g1 + g2) #' gf1 #' #' gf2 <- parseGroupFormula( ~ x1 + x2 | g1 + g2) #' gf2 #' #' @export parseGroupFormula parseGroupFormula <- function(form) { if (!inherits(form, "formula")) stop("formula must be a two-sided formula object") rhs <- form[[length(form)]] if (!inherits(rhs, "call") || rhs[[1]] != as.symbol('|')) stop("rhs of formula must be a conditioning expression") new_form <- form new_form[[length(form)]] <- rhs[[2]] groups <- rhs[[3]] grpFormula <- as.formula(paste("~", deparse(groups))) list(model = new_form, groups = groups, groupFormula=grpFormula) } #' @title Convert right hand sided formula to a list #' @description Convert right hand sided formula to a list #' @param f A right hand sided formula .rhsf2list <- function (f) { if (is.character(f)) list(f) else if (is.numeric(f)) lapply(list(f), "as.character") else if (is.list(f)) lapply(f, "as.character") else { .xxx. <- f[[length(f)]] f1 <- unlist(strsplit(paste(deparse(.xxx.), collapse = ""), " *\\+ *")) f2 <- unlist(lapply(f1, strsplit, " *\\* *| *: *| *\\| *"), recursive = FALSE) f2 } } #' Where are the n largest or n smallest elements in a numeric vector ? #' #' Determines the locations, i.e., indices of the n largest or n smallest #' elements of a numeric vector. #' #' @aliases which.maxn which.minn #' @param x numeric vector #' @param n integer >= 1 #' @return A vector of length at most n with the indices of the n largest / #' smaller elements. NAs are discarded and that can cause the vector to be #' smaller than n. #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @seealso \code{\link{which.max}}, \code{\link{which.min}} #' @keywords utilities #' @examples #' #' x <- c(1:4, 0:5, 11, NA, NA) #' ii <- which.minn(x, 5) #' #' x <- c(1, rep(NA,10), 2) #' ii <- which.minn(x, 5) #' #' @export which.maxn which.maxn <- function(x, n = 1) { if (n==1) which.max(x) else { if (n > 1){ ii <- order(x,decreasing = TRUE)[1:min(n, length(x))] ii[!is.na(x[ii])] } else { stop("n must be >= 1") } } } #' @export which.minn <- function(x, n = 1) { if (n == 1) which.min(x) else { if (n > 1) { ii <- order(x,decreasing=FALSE)[1:min(n, length(x))] ii[!is.na(x[ii])] } else { stop("n must be >= 1") } } } #' Truncate values in a matrix / vector to zero if they are below a certain threshold. #' #' @param x matrix / vector #' @param tol threshold #' @param sparse logical; if TRUE and `x` is a matrix, return a sparse matrix #' #' @export truncate0 <- function(x, tol=0.6, sparse=TRUE){ ## doBy candidate x[abs(x) <= tol] <- 0 if (is.matrix(x)){ if (sparse) as(x, "dgCMatrix") else x } } #' @name taylor #' #' @title Taylor expansion (one dimension) #' #' @description Returns Taylor polynomial approximating a function fn(x) #' #' @param fn A function of one variable and that variable must be named 'x'. #' @param x0 The point in which to to the Taylor expansion. #' @param ord The order of the Taylor expansion. #' #' @return function. #' #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' #' @keywords utilities #' #' @examples #' fn <- function(x) log(x) #' ord <- 2 #' x0 <- 2 #' #' xv <- seq(.2, 5, .1) #' plot(xv, fn(xv), type="l") #' lines(xv, taylor(fn, x0=x0, ord=ord)(xv), lty=2) #' abline(v=x0) #' #' fn <- function(x)sin(x) #' ord <- 4 #' x0 <- 0 #' xv <- seq(-2*pi, 2*pi, 0.1) #' plot(xv, fn(xv), type="l") #' lines(xv, taylor(fn, x0=x0, ord=ord)(xv), lty=2) #' abline(v=x0) #' #' #' #' @export taylor <- function(fn, x0, ord=1){ if (!is.function(fn)) stop("'fn' is not a function") aname <- names(formals(fn)) if (length(aname) != 1) stop("'fn' must take a single argument") if (!identical(aname, "x")) stop("argument to 'fn' must be called 'x'") df <- Deriv(fn, nderiv=1:ord) quad <- paste0("(x-x0)^") aa <- paste0(quad, 1:ord) bb <- gsub("x0", x0, aa) bb fac <- factorial(1:ord) ff <- unlist(df(x0)) / fac bb <- paste0(ff, "*", bb) bb expr <- parse(text=(paste0(bb, collapse="+"))) fun <- expr_to_fun(expr) fun } #' Recode values of a vector #' #' Recodes a vector with values, say 1,2 to a variable with values, say 'a', #' 'b' #' #' @param x A vector; the variable to be recoded. #' @param src The source values: a subset of the present values of x #' @param tgt The target values: the corresponding new values of x #' @param default Default target value for those values of x not listed in #' `src`. When default=NULL, values of x which are not given in `src` will #' be kept in the output. #' @param keep.na If TRUE then NA's in x will be retained in the output #' @return A vector #' @section Warning : Care should be taken if x is a factor. A safe approach may #' be to convert x to a character vector using as.character. #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @seealso \code{\link[base]{cut}}, \code{\link[base]{factor}}, #' \code{\link[doBy]{recodeVar}} #' @keywords utilities #' @examples #' #' x <- c("dec", "jan", "feb", "mar", "apr", "may") #' src1 <- list(c("dec", "jan", "feb"), c("mar", "apr", "may")) #' tgt1 <- list("winter", "spring") #' recodeVar(x, src=src1, tgt=tgt1) #' #[1] "winter" "winter" "winter" "spring" "spring" "spring" #' #' x <- c(rep(1:3, 3)) #' #[1] 1 2 3 1 2 3 1 2 3 #' #' ## Simple usage: #' recodeVar(x, src=c(1, 2), tgt=c("A", "B")) #' #[1] "A" "B" NA "A" "B" NA "A" "B" NA #' #' ## Here we need to use lists #' recodeVar(x, src=list(c(1, 2)), tgt=list("A")) #' #[1] "A" "A" NA "A" "A" NA "A" "A" NA #' recodeVar(x, src=list(c(1, 2)), tgt=list("A"), default="L") #' #[1] "A" "A" "L" "A" "A" "L" "A" "A" "L" #' recodeVar(x, src=list(c(1, 2), 3), tgt=list("A", "B"), default="L") #' #[1] "A" "A" "B" "A" "A" "B" "A" "A" "B" #' #' ## Dealing with NA's in x #' x<-c(NA,rep(1:3, 3),NA) #' #[1] NA 1 2 3 1 2 3 1 2 3 NA #' recodeVar(x, src=list(c(1, 2)), tgt=list("A")) #' #[1] NA "A" "A" NA "A" "A" NA "A" "A" NA NA #' recodeVar(x, src=list(c(1, 2)), tgt=list("A"), default="L") #' #[1] NA "A" "A" "L" "A" "A" "L" "A" "A" "L" NA #' recodeVar(x, src=list(c(1, 2)), tgt=list("A"), default="L", keep.na=FALSE) #' #[1] "L" "A" "A" "L" "A" "A" "L" "A" "A" "L" "L" #' #' x <- c("no", "yes", "not registered", "no", "yes", "no answer") #' recodeVar(x, src = c("no", "yes"), tgt = c("0", "1"), default = NA) #' #' #' @export recodeVar recodeVar <- function(x, src, tgt, default=NULL, keep.na=TRUE) { if (length(src)!=length(tgt)) { stop("length of src not equal to length of tgt") } mtc <- lapply(src, function(zzz) { which(x %in% zzz) }) idx <- seq_along(x) unmatch <- setdiff(idx, unlist(mtc)) if (is.factor(x)) { val <- as.character(x) } else { val <- x } for (ii in 1:length(tgt)) val[mtc[[ii]]] <- tgt[[ii]] if (!is.null(default)) { if (keep.na) { iii <- intersect(which(!is.na(x)), unmatch) val[iii] <- default } else { val[unmatch] <- default } } if (is.factor(x)) val <- as.factor(val) val return(val) } #' @rdname recodeVar #' @export recode_var <- recodeVar #' Rename columns in a matrix or a dataframe. #' #' Rename columns in a matrix or a dataframe. #' #' #' @param indata A dataframe or a matrix #' @param src Source: Vector of names of columns in `indata` to be #' renamed. Can also be a vector of column numbers. #' @param tgt Target: Vector with corresponding new names in the #' output. #' @return A dataframe if `indata` is a dataframe; a matrix in #' `indata` is a matrix. #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @keywords utitlities #' @examples #' renameCol(CO2, 1:2, c("kk", "ll")) #' renameCol(CO2, c("Plant", "Type"), c("kk", "ll")) #' #' # These fail - as they should: #' # renameCol(CO2, c("Plant", "Type", "conc"), c("kk", "ll")) #' # renameCol(CO2, c("Plant", "Type", "Plant"), c("kk", "ll")) #' #' @export renameCol renameCol <- function(indata, src, tgt){ if (inherits(indata, "data.frame")) { isDF <- TRUE dfnames <- names(indata) } else { if (inherits(indata, "matrix")) { isDF <- FALSE dfnames <- colnames(indata) } else { stop("'indata' must be either a dataframe or a matrix") } } if (length(src)!=length(unique(src))){ stop("A src name has been repeated") } if (length(tgt)!=length(unique(tgt))){ stop("A tgt name has been repeated") } if (length(src)!=length(tgt)){ stop("length of src not equal to length of tgt") } if (is.numeric(src)){ idx <- src iii <- intersect(seq_along(dfnames), src) iii <- setdiff(src, iii) if (length(iii)>0){ sss <- sprintf("Column(s) %s are not in 'indata'", toString(iii)) stop(sss) } } else { idx <- match(src, dfnames) if (any(is.na(idx))){ sss <- sprintf("Column names %s are not in 'indata'", toString(src[is.na(idx)])) stop(sss) } } ans <- indata if (isDF){ names(ans)[idx] <- tgt } else { colnames(ans)[idx] <- tgt } return(ans) } ############################################################################### #' @title Computing simple descriptive statistics of a numeric vector. #' @description Computing simple descriptive statistics of a numeric #' vector - not unlike what proc means of SAS does ############################################################################### #' @param x A numeric vector #' @param na.rm Should missing values be removed #' @return A vector with named elements. #' @author Gregor Gorjanc; \email{gregor.gorjanc@@bf.uni-lj.si} #' @seealso \code{\link{summaryBy}}, \code{\link{summary_by}} #' @keywords utilities #' @examples #' #' x <- c(1, 2, 3, 4, NA, NaN) #' descStat(x) #' #' @export descStat descStat <- function (x, na.rm = TRUE) { if(!is.numeric(x)) stop("'x' must be numeric") m <- mean(x, na.rm = na.rm) s <- sd(x, na.rm = na.rm) c(n = length(x), obs = sum(!is.na(x)), mean = m, median = median(x, na.rm = na.rm), sd = s, cv = s/m, min = min(x, na.rm = na.rm), max = max(x, na.rm = na.rm)) } doBy/R/data_split_byrow_bycol.R0000644000176200001440000000470615030065124016234 0ustar liggesusers#' @title Split matrix or dataframe into list #' #' @description Split matrix or dataframe into list by columns or by rows #' #' @name split_byrow_bycol #' @param x Matrix or dataframe. #' @param idx Index to split by. If NULL, split by columns or rows. #' @param as.list If TRUE, return list of dataframes. If FALSE, return list of matrices. #' #' @examples #' x <- mtcars[1:3, 1:6] #' x |> split_bycol() #' x |> split_bycol(as.list=TRUE) #' x |> split_bycol(as.list=FALSE) #' x |> split_bycol(idx=c(1,1,1,2,2,3,3,3)) #' ## x |> split_bycol(idx=c(1,1,7,2,2,3,3,3)) ## Gives error #' #' x <- mtcars[1:6, 1:6] #' x |> split_byrow() #' x |> split_byrow(idx=c(1,1,2,2)) #' #' @export #' @rdname split_byrow_bycol split_bycol <- function(x, idx=NULL, as.list=FALSE) { if (!inherits(x, c("matrix", "data.frame"))) stop("'x' must be matrix or dataframe\n") if (ncol(x) == 0) { return(list()) } isdf <- inherits(x, "data.frame") if (as.list){ out <- as.list(as.data.frame(x)) } else { if (is.null(idx)) idx <- 1:ncol(x) if (!(is.numeric(idx))) { stop("'idx' must be numeric\n") } if (!(length(idx) == ncol(x))){ idx <- rep(idx, length.out = ncol(x)) } if (max(idx)>ncol(x)) stop("max idx exceeds number of columns\n") out <- lapply(seq_along(unique(idx)), function(i) { zz <- x[, idx == i, drop=FALSE] names(zz) <- names(x)[idx == i] zz }) } if (!isdf){ out <- lapply(out, as.matrix) } return(out) } #' @examples #' m <- as.matrix(x) #' u <- x |> split_byrow(idx=c(1,1,2,2)) #' y <- m |> split_byrow(idx=c(1,1,2,2)) #' #' @export #' @rdname split_byrow_bycol split_byrow <- function (x, idx=NULL) { if (!inherits(x, c("matrix", "data.frame"))) stop("'x' must be matr ix or dataframe\n") isdf <- inherits(x, "data.frame") if (nrow(x) == 0) { return(list()) } if (is.null(idx)) { idx <- 1:nrow(x) } if (!(length(idx) == nrow(x))){ idx <- rep(idx, length.out = nrow(x)) } out <- split(x, idx) if (!isdf){ out <- lapply(out, function(z){ matrix(z, ncol=ncol(x)) }) } if (!is.null(colnames(x))) { out <- lapply(out, function(z){ colnames(z) <- colnames(x) z }) } return(out) } doBy/R/DATA_doby.R0000644000176200001440000010127515030274157013233 0ustar liggesusers#' @title Height of math teachers #' @description Height of a sample of math teachers in Danish high #' schools collected at a continued education day at Mariager Fjord Gymnasium in 2019. #' #' Format: #' height: Height in centimeters #' sex: male or female #' #' @examples #' #' aggregate(height ~ sex, data=math_teachers, FUN=mean) #' aggregate(height ~ sex, data=math_teachers, FUN=function(x) {c(mean=mean(x), sd=sd(x))}) #' "math_teachers" ##' @title Berkeley Growth Study data ##' @description dataframe with heights of 39 boys and 54 girls from ##' age 1 to 18 and the ages at which they were collected. ##' ##' Format: ##' ##' gender: Gender of child ##' age: Age at time of data recordning ##' subject: Idenfication for each child ##' height: Height of child ##' ##' ##' @details Notice that the ages are not equally spaced. Data are ##' taken from the fda package (growth) but put in long format ##' here. ##' ##' @references ##' Ramsay, James O., Hooker, Giles, and Graves, Spencer (2009), ##' _Functional data analysis with R and Matlab_, Springer, New York. ##' ##' Ramsay, James O., and Silverman, Bernard W. (2005), _Functional ##' Data Analysis, 2nd ed._, Springer, New York. ##' ##' Ramsay, James O., and Silverman, Bernard W. (2002), _Applied ##' Functional Data Analysis_, Springer, New York. ##' ##' Tuddenham, R. D., and Snyder, M. M. (1954) "Physical growth of ##' California boys and girls from birth to age 18", _University of ##' California Publications in Child Development_, 1, 183-364. ##' ##' "child_growth" #' Chemical composition of wine #' #' Using chemical analysis determine the origin of wines #' #' Data comes from the UCI Machine Learning Repository. The grape variety #' \code{Cult} is the class identifier. #' @concept dataset #' @name data-wine #' @docType data #' @format A data frame with 178 observations on the following 14 variables. #' \describe{ #' \item{\code{Cult}}{a factor with levels \code{v1} \code{v2} #' \code{v3}: 3 different graph varieties} #' \item{\code{Alch}}{Alcohol} #' \item{\code{Mlca}}{Malic acid} #' \item{\code{Ash}}{Ash} #' \item{\code{Aloa}}{Alcalinity of ash} #' \item{\code{Mgns}}{Magnesium} #' \item{\code{Ttlp}}{Total phenols} #' \item{\code{Flvn}}{Flavanoids} #' \item{\code{Nnfp}}{Nonflavanoid phenols} #' \item{\code{Prnt}}{Proanthocyanins} #' \item{\code{Clri}}{Color intensity} #' \item{\code{Hue}}{Hue} #' \item{\code{Oodw}}{OD280/OD315 of diluted wines} #' \item{\code{Prln}}{Proline} #' } #' #' @references See references at #' \url{https://archive.ics.uci.edu/ml/datasets/Wine/} #' #' @source Frank, A. & Asuncion, A. (2010). UCI Machine Learning #' Repository \url{https://archive.ics.uci.edu/ml/}. Irvine, CA: #' University of California, School of Information and Computer #' Science. #' #' @keywords datasets #' @usage data(wine) #' @examples #' #' data(wine) #' ## maybe str(wine) ; plot(wine) ... #' "wine" #' @title income data #' @description Data on income, years of educations and ethnicity for a samle of adult Americans aged over 25. The year of sampling is not avalable in the source. #' @name income #' @docType data #' @format This data frame contains: #' \describe{ #' \item{inc:}{Income: Yearly income (thousands of dollars).} #' \item{educ:}{Education: Number of years of education (12=high school graduate, 16=college graduate).} #' \item{race:}{Racial-Ethnic group: "b" (black), "h" (hispanic) and "w" (white).} #' } #' @keywords datasets #' #' @details Variable names are as in the reference. #' #' @references Agresti, A. (2024) Statistical Methods for the Social #' Sciences, Global Edition (6th edition). ISBN-13: 9781292449197. Table 13.1 #' "income" #' @title shoes #' @description Wear of soles of shoes of materials A and B for one foot each for of ten boys. #' #' @details The shoes data are measurements of the amount wear of the #' soles of shoes worn by 10 boys. The soles were made to two #' different synthetic materials, a standard material A and a #' cheaper material B. #' #' @name shoes #' @docType data #' @format This data frame contains: #' \describe{ #' \item{A:}{Wear, material A} #' \item{B:}{Wear, material B} #' \item{boy:}{Id of boy} #' \item{footA:}{The foot with material A} #' } #' @keywords datasets #' #' @references Box, Hunter, Hunter (2005) Statistics for Experimenters, 2nd edition Wiley, p. 81. #' "shoes" #' @title crickets data #' @description Mating songs of male tree crickets. #' @name crickets #' #' @docType data #' @format This data frame contains: #' \describe{ #' \item{species:}{Species, (exis, nius), see details} #' \item{temp:}{temperature} #' \item{pps:}{pulse per second} #' } #' @keywords datasets #' #' @details #' #' Walker (1962) studied the mating songs of male tree crickets. Each #' wingstroke by a cricket produces a pulse of song, and females may #' use the number of pulses per second to identify males of the #' correct species. Walker (1962) wanted to know whether the chirps #' of the crickets Oecanthus exclamationis (abbreviated exis) and #' Oecanthus niveus (abbreviated nius) had different pulse rates. See #' the biostathandbook for details. (The #' abbreviations are made from the the first two and last two letters #' of the species.) Walker measured the pulse rate of the crickets #' (variable `pps`) at a variety of temperatures (`temp`): #' #' @examples #' #' data(crickets) #' coplot(pps ~ temp | species, data=crickets) "crickets" ## ## beets ## #' @title beets data #' #' @description Yield and sugar percentage in sugar beets from a split plot #' experiment. Data is obtained from a split plot experiment. There are 3 #' blocks and in each of these the harvest time defines the "whole plot" and #' the sowing time defines the "split plot". Each plot was 25 square meters and #' the yield is recorded in kg. See 'details' for the experimental layout. #' #' @name beets #' #' @docType data #' @format The format is: chr "beets" #' #' @details #' \preformatted{ #' Experimental plan #' Sowing times 1 4. april #' 2 12. april #' 3 21. april #' 4 29. april #' 5 18. may #' Harvest times 1 2. october #' 2 21. october #' Plot allocation: #' Block 1 Block 2 Block 3 #' +-----------|-----------|-----------+ #' Plot | 1 1 1 1 1 | 2 2 2 2 2 | 1 1 1 1 1 | Harvest time #' 1-15 | 3 4 5 2 1 | 3 2 4 5 1 | 5 2 3 4 1 | Sowing time #' |-----------|-----------|-----------| #' Plot | 2 2 2 2 2 | 1 1 1 1 1 | 2 2 2 2 2 | Harvest time #' 16-30 | 2 1 5 4 3 | 4 1 3 2 5 | 1 4 3 2 5 | Sowing time #' +-----------|-----------|-----------+ #' } #' #' @references Ulrich Halekoh, Søren Højsgaard (2014)., A Kenward-Roger #' Approximation and Parametric Bootstrap Methods for Tests in Linear Mixed #' Models - The R Package pbkrtest., Journal of Statistical Software, #' 58(10), 1-30., \url{https://www.jstatsoft.org/v59/i09/} #' #' @keywords datasets #' #' @examples #' data(beets) #' #' beets$bh <- with(beets, interaction(block, harvest)) #' summary(aov(yield ~ block + sow + harvest + Error(bh), beets)) #' summary(aov(sugpct ~ block + sow + harvest + Error(bh), beets)) #' "beets" #' Gene expression signatures for p53 mutation status in 250 breast cancer #' samples #' #' Perturbations of the p53 pathway are associated with more aggressive and #' therapeutically refractory tumours. We preprocessed the data using Robust #' Multichip Analysis (RMA). Dataset has been truncated to the 1000 most #' informative genes (as selected by Wilcoxon test statistics) to simplify #' computation. The genes have been standardized to have zero mean and unit #' variance (i.e. z-scored). #' #' The factor \code{code} defines whether there was a mutation in the p53 #' sequence (code=case) or not (code=control). #' #' @name data_breastcancer #' #' @docType data #' #' @format A data frame with 250 observations on 1001 variables. The #' first 1000 columns are numerical variables; the last column #' (named \code{code}) is a factor with levels \code{case} and #' \code{control}. #' #' @references Miller et al (2005, PubMed #' ID:16141321) #' #' @source Chris Holmes, \email{c.holmes@@stats.ox.ac.uk} #' #' @keywords datasets #' #' @examples #' #' data(breastcancer) #' bc <- breastcancer #' pairs(bc[,1:5], col=bc$code) #' #' train <- sample(1:nrow(bc), 50) #' table(bc$code[train]) #' \dontrun{ #' library(MASS) #' z <- lda(code ~ ., data=bc, prior = c(1, 1) / 2, subset = train) #' pc <- predict(z, bc[-train, ])$class #' pc #' bc[-train, "code"] #' table(pc, bc[-train, "code"]) #' } #' "breastcancer" ## ## carcass ## #' Lean meat contents of 344 pig carcasses #' #' Measurement of lean meat percentage of 344 pig carcasses together with #' auxiliary information collected at three Danish slaughter houses #' #' @name carcass #' #' @aliases carcass carcassall #' @format carcassall: A data frame with 344 observations on the following 17 #' variables. #' \describe{ #' \item{\code{weight}}{Weight of carcass} #' \item{\code{lengthc}}{Length of carcass from back toe to head (when #' the carcass hangs in the back legs)} #' \item{\code{lengthf}}{Length of carcass from back toe to front leg #' (that is, to the shoulder)} #' \item{\code{lengthp}}{Length of carcass from back toe to the pelvic bone} #' \item{\code{Fat02, Fat03, Fat11, Fat12, Fat13, Fat14, Fat16}}{Thickness of fat #' layer at different locations on the back of the carcass (FatXX #' refers to thickness at (or rather next to) rib no. XX. Notice that #' 02 is closest to the head} #' \item{\code{Meat11, Meat12, Meat13}}{Thickness of meat layer at different #' locations on the back of the carcass, see description above} #' \item{\code{LeanMeat}}{Lean meat percentage determined by dissection} #' \item{\code{slhouse}}{Slaughter house; a factor with levels \code{slh1} and \code{slh2}.} #' \item{\code{sex}}{Sex of the pig; a factor with levels \code{castrate} and \code{female}.} #' \item{\code{size}}{Size of the carcass; a factor with levels \code{normal} and \code{large}. #' Here, `normal` refers to carcass weight under 80 kg; `large` refers to carcass weights between 80 and 110 kg.} #' #' } #' #' @details: Notice that there were slaughtered large pigs only at one slaughter house. #' @note carcass: Contains only the variables Fat11, Fat12, Fat13, #' Meat11, Meat12, Meat13, LeanMeat #' @source Busk, H., Olsen, E. V., Brøndum, J. (1999) Determination of #' lean meat in pig carcasses with the Autofom classification #' system, Meat Science, 52, 307-314 #' @keywords datasets #' @examples #' data(carcass) #' head(carcass) #' "carcass" "carcassall" #' Diet of Atlantic cod in the Gulf of St. Lawrence (Canada) #' #' Stomach content data for Atlantic cod (\emph{Gadus morhua}) in the Gulf of #' St.Lawrence, Eastern Canada. Note: many prey items were of no interest for #' this analysis and were regrouped into the "Other" category. #' #' Cod are collected either by contracted commerical fishing vessels #' (\code{ship.type} 90 or 99) or by research vessels. Commercial vessels are #' identified by a unique \code{ship.id}. #' #' Either one research vessel or several commercial vessels conduct a survey #' (\code{trip}), during which a trawl, gillnets or hooked lines are set #' several times. Most trips are random stratified surveys (depth-based #' stratification). #' #' Each trip takes place within one of the \code{region}s. The \code{trip} #' label is only guaranteed to be unique within a region and the \code{set} #' label is only guaranteed to be unique within a \code{trip}. #' #' For each fish caught, the \code{fish.length} is recorded and the fish is #' allocated a \code{fish.id}, but the \code{fish.id} is only guaranteed to be #' unique within a \code{set}. A subset of the fish caught are selected for #' stomach analysis (stratified random selection according to fish length; unit #' of stratification is the set for research surveys, the combination ship.id #' and stratum for surveys conducted by commercial vessels, although strata are #' not shown in codstom). #' #' The basic experimental unit in this data set is a cod stomach (one stomach #' per fish). Each stomach is uniquely identified by a combination of #' \code{region}, \code{ship.type}, \code{ship.id}, \code{trip}, \code{set}, #' and \code{fish.id}. #' #' For each prey item found in a stomach, the species and mass of the prey item #' are recorded, so there can be multiple observations per stomach. There may #' also be several prey items with the same \code{prey.type} in the one stomach #' (for example many \code{prey.types} have been recoded \code{Other}, which #' produced many instances of \code{Other} in the same stomach). #' #' If a stomach is empty, a single observation is recorded with #' \code{prey.type} \code{Empty} and a \code{prey.mass} of zero. #' #' @name codstom #' @docType data #' #' @format A data frame with 10000 observations on the following 10 variables. #' \describe{ #' \item{\code{region}}{a factor with levels \code{SGSL} \code{NGSL} #' representing the southern and northern Gulf of St. Lawrence, respectively} #' \item{\code{ship.type}}{a factor with levels \code{2} \code{3} \code{31} #' \code{34} \code{90} \code{99}} #' \item{\code{ship.id}}{a factor with levels \code{11558} \code{11712} #' \code{136148} \code{136885} #' \code{136902} \code{137325} \code{151225} \code{151935} \code{99433}} #' \item{\code{trip}}{a factor with levels \code{10} \code{11} #' \code{12} \code{179} \code{1999} #' \code{2} \code{2001} \code{20020808} \code{3} \code{4} \code{5} #' \code{6} \code{7} \code{8} #' \code{88} \code{9} \code{95}} #' \item{\code{set}}{a numeric vector} #' \item{\code{fish.id}}{a numeric vector} #' \item{\code{fish.length}}{a numeric vector, length in mm} #' \item{\code{prey.mass}}{a numeric vector, mass of item in stomach, in g} #' \item{\code{prey.type}}{a factor with levels \code{Ammodytes_sp} #' \code{Argis_dent} #' \code{Chion_opil} \code{Detritus} \code{Empty} \code{Eualus_fab} #' \code{Eualus_mac} \code{Gadus_mor} \code{Hyas_aran} #' \code{Hyas_coar} #' \code{Lebbeus_gro} \code{Lebbeus_pol} \code{Leptocl_mac} #' \code{Mallot_vil} #' \code{Megan_norv} \code{Ophiuroidea} \code{Other} \code{Paguridae} #' \code{Pandal_bor} \code{Pandal_mon} \code{Pasiph_mult} #' \code{Sabin_sept} #' \code{Sebastes_sp} \code{Them_abys} \code{Them_comp} \code{Them_lib}} #' } #' @source Small subset from a larger dataset (more stomachs, more variables, #' more \code{prey.types}) collected by D. Chabot and M. Hanson, Fisheries & #' Oceans Canada \email{chabotd@@dfo-mpo.gc.ca}. #' #' @keywords datasets #' @examples #' #' data(codstom) #' str(codstom) #' # removes multiple occurences of same prey.type in stomachs #' codstom1 <- summaryBy(prey.mass ~ #' region + ship.type + ship.id + trip + set + fish.id + prey.type, #' data = codstom, #' FUN = sum) #' #' # keeps a single line per stomach with the total mass of stomach content #' codstom2 <- summaryBy(prey.mass ~ region + ship.type + ship.id + trip + set + fish.id, #' data = codstom, #' FUN = sum) #' #' # mean prey mass per stomach for each trip #' codstom3 <- summaryBy(prey.mass.sum ~ region + ship.type + ship.id + trip, #' data = codstom2, FUN = mean) #' #' \dontrun{ #' # wide version, one line per stomach, one column per prey type #' library(reshape) #' codstom4 <- melt(codstom, id = c(1:7, 9)) #' codstom5 <- cast(codstom4, #' region + ship.type + ship.id + trip + set + fish.id + fish.length ~ #' prey.type, sum) #' k <- length(names(codstom5)) #' prey_col <- 8:k #' out <- codstom5[,prey_col] #' out[is.na(out)] <- 0 #' codstom5[,prey_col] <- out #' codstom5$total.content <- rowSums(codstom5[, prey_col]) #' } #' "codstom" #' crimeRate #' #' Crime rates per 100,000 inhabitants in states of the USA for different crime #' types in 1977. #' #' @name crimeRate #' #' @docType data #' @format This data frame contains: #' \describe{ #' \item{state:}{State of the USA} #' \item{murder:}{crime of murder} #' \item{rape:}{} #' \item{robbery:}{} #' \item{assault:}{} #' \item{burglary:}{residential theft} #' \item{larceny:}{unlawful taking of personal property (pocket picking)} #' \item{autotheft:}{} #' } #' @keywords datasets #' @examples #' #' data(crimeRate) #' "crimeRate" #' crimeRate #' #' Crime rates per 100,000 inhabitants in states of the USA for different crime #' types in 1977. #' #' @name crime_rate #' #' @docType data #' @format This data frame contains: #' \describe{ ## #' \item{State:}{State of the USA} #' \item{murder:}{crime of murder} #' \item{rape:}{} #' \item{robbery:}{} #' \item{assault:}{} #' \item{burglary:}{residential theft} #' \item{larceny:}{unlawful taking of personal property (pocket picking)} #' \item{autotheft:}{} #' } #' @keywords datasets #' @examples #' #' data(crime_rate) #' "crime_rate" ## ## dietox ## #' Growth curves of pigs in a 3x3 factorial experiment #' #' The \code{dietox} data frame has 861 rows and 7 columns. #' #' @details Data contains weight of slaughter pigs measured weekly for 12 #' weeks. Data also contains the start weight (i.e. the weight at week #' 1). The treatments are 3 different levels of Evit = vitamin E (dose: 0, #' 100, 200 mg dl-alpha-tocopheryl acetat /kg feed) in combination with 3 #' different levels of Cu=copper (dose: 0, 35, 175 mg/kg feed) in the feed. #' The cumulated feed intake is also recorded. The pigs are litter mates. #' #' #' @format This data frame contains the following columns: #' #' \describe{ #' \item{Weight}{Weight in Kg} #' \item{Feed}{Cumulated feed intake in Kg} #' \item{Time}{Time (in weeks) in the experiment} #' \item{Pig}{Factor; id of each pig} #' \item{Evit}{Factor; vitamin E dose; see 'details'.} #' \item{Cu}{Factor, copper dose; see 'details'} #' \item{Start}{Start weight in experiment, i.e. weight at week 1.} #' \item{Litter}{Factor, id of litter of each pig} #' } #' #' @source Lauridsen, C., Højsgaard, S.,Sørensen, M.T. C. (1999) Influence of #' Dietary Rapeseed Oli, Vitamin E, and Copper on Performance and #' Antioxidant and Oxidative Status of Pigs. J. Anim. Sci.77:906-916 #' @keywords datasets #' @examples #' #' data(dietox) #' head(dietox) #' coplot(Weight ~ Time | Evit * Cu, data=dietox) #' "dietox" ## ## fatacid ## #' @title Fish oil in pig food #' #' @description Fish oil in pig food #' #' @name fatacid #' @docType data #' #' @format A dataframe. #' #' @details A fish oil fatty acid \code{X14} has been added in #' different concentrations to the food for pigs in a #' study. Interest is in studying how much of the fatty acid can #' be found in the tissue. The concentrations of \code{x14} in the #' food are \code{verb+dose+=\{0.0, 4.4, 6.2, 9.3\}}. #' #' The pigs are fed with this food until their weight is 60 kg. From #' thereof and until they are slaughtered at 100kg, their food does #' not contain the fish oil. #' At 60kg (sample=1) and 100kg (sample=2) muscle #' biopsies are made and the concentration of x14 is #' determined. #' Measurements on the same pig are correlated, and pigs are additionally #' related through litters. #' #' @references Data courtesy of Charlotte Lauridsen, Department of #' Animal Science, Aarhus University, Denmark. "fatacid" #' @title Forced expiratory volume in children #' #' @description Dataset to examine if respiratory function in children #' was influenced by smoking. #' #' @name fev #' @docType data #' #' @format A data frame with 654 observations on the following 5 variables. #' \describe{ #' \item{\code{Age}}{Age in years.} #' \item{\code{FEV}}{Forced expiratory volume in liters per second.} #' \item{\code{Ht}}{Height in centimeters.} #' \item{\code{Gender}}{Gender.} #' \item{\code{Smoke}}{Smoking status.} #' } #' #' #' @references I. Tager and S. Weiss and B. Rosner and F. Speizer (1979). Effect #' of Parental Cigarette Smoking on the Pulmonary Function of #' Children. American Journal of Epidemiology. 110:15-26 #' #' @examples #' #' data(fev) #' summary(fev) #' "fev" #' Heat development in cement under hardening. #' #' Heat development in cement under hardening related to the chemical #' composition. #' #' @name haldCement #' @docType data #' #' @format A data frame with 13 observations on the following 5 variables. #' \describe{ #' \item{\code{x1}}{Percentage (weight) of `[3Ca0][Al2O3]`} #' \item{\code{x2}}{Percentage (weight) of `[3Cao][SiO2]`} #' \item{\code{x3}}{Percentage (weight) of `[4Ca0][Al2O3][Fe03]`} #' \item{\code{x4}}{Percentage (weight) of `[2Cao][SiO2]`} #' \item{\code{y}}{Heat development measured in calories per #' gram cement after 180 days} #' } #' #' @references Anders Hald (1949); Statistiske Metoder; Akademisk Forlag (in #' Danish), page 509. #' @keywords datasets #' @examples #' #' data(haldCement) #' #' if( interactive() ){ #' pairs( haldCement ) #' } #' m <- lm(y ~ x1 + x2 + x3 + x4, data=haldCement) #' summary(m) #' #' # Notice: The model explains practically all variation in data; #' # yet none of the explanatory variables appear to be statistically #' # significant. #' "haldCement" #' Yield from Danish agricultural production of grain and root crop. #' #' @name cropyield #' @docType data #' @format A dataframe with 97 rows and 7 columns. #' \describe{ #' \item{\code{year}}{From 1901 to 1997.} #' \item{\code{precip}}{Milimeter precipitation.} #' \item{\code{yield}}{Million feed units (see details).} #' \item{\code{area}}{Area in 1000 ha for grains and root crop.} #' \item{\code{fertil}}{1000 tons fertilizer.} #' \item{\code{avgtmp1}}{Average temperature April-June (3 months).} #' \item{\code{avgtmp2}}{Average temperature July-Octobre (4 months). } #' } #' #' @details A feed unit is the amount of energy in a kg of barley. #' @references Danmarks statistik (Statistics Denmark). #' @keywords datasets "cropyield" #' Milk yield data for manually milked cows. #' #' Milk yield data for cows milked manually twice a day (morning and evening). #' #' There are data for 222 cows. Some cows appear more than once in the dataset #' (in different lactations) and there are 288 different lactations. #' #' @aliases milkman_rdm1 #' #' @name milkman #' @docType data #' #' @format #' A data frame with 161836 observations on the following 12 variables. #' \describe{ #' \item{\code{cowno}}{a numeric vector; cow identification} #' \item{\code{lactno}}{a numeric vector; lactation number} #' \item{\code{ampm}}{a numeric vector; milking time: 1: morning; 2: evening} #' \item{\code{dfc}}{a numeric vector; days from calving} #' \item{\code{my}}{a numeric vector; milk yield (kg)} #' \item{\code{fatpct}}{a numeric vector; fat percentage} #' \item{\code{protpct}}{a numeric vector; protein percentage} #' \item{\code{lactpct}}{a numeric vector; lactose percentage} #' \item{\code{scc}}{a numeric vector; somatic cell counts} #' \item{\code{race}}{a factor with levels \code{RDM} \code{Holstein} \code{Jersey}} #' \item{\code{ecmy}}{a numeric vector; energy corrected milk} #' \item{\code{cowlact}}{Combination of cowno and lactno; necessary #' because the same cow may appear more than once in the dataset (in #' different lactations)} #' } #' #' @keywords datasets #' #' @references Friggens, N. C.; Ridder, C. and Løvendahl, P. (2007). #' On the Use of Milk Composition Measures to Predict the Energy Balance of Dairy Cows. #' J. Dairy Sci. 90:5453–5467 doi:10.3168/jds.2006-821. #' #' This study was part of the Biosens project used data from the #' “Malkekoens energibalance og mobilisering” project; both were #' funded by the Danish Ministry of Food, Agriculture and Fisheries #' and the Danish Cattle Association. #' #' @examples #' #' data(milkman) #' "milkman" "milkman_rdm1" #' @title NIRmilk #' #' @description Near infra red light (NIR) measurements are made at #' 152 wavelengths on 17 milk samples. While milk runs through a #' glass tube, infra red light is sent through the tube and the #' amount of light passing though the tube is measured at #' different wavelengths. Each milk sample was additionally #' analysed for fat, lactose, protein and dry matter. #' #' @name NIRmilk #' @docType data #' @format This data frame contains 17 rows and 158 columns. The #' first column is the sample number. The columns `Xklm` contains #' the transmittance (fraction of electromagnetic power) #' transmittance through the sample at wavelength `klm`. The #' response variables are fat, protein, lactose and dm (dry #' matter). #' @keywords datasets #' @examples #' #' data(NIRmilk) #' "NIRmilk" #' @title nir_milk #' #' @description Near infra red light (NIR) measurements are made at #' 152 wavelengths on 17 milk samples. While milk runs through a #' glass tube, infra red light is sent through the tube and the #' amount of light passing though the tube is measured at #' different wavelengths. Each milk sample was additionally #' analysed for fat, lactose, protein and dry matter. #' #' @name nir_milk #' @docType data #' @format A list with two components #' x Datafrane with infra red light amount at different wavelengths (column names are the wavelengths; just remove the leading X). #' y Datafrane with response variables fat, protein, #' lactose and dm (drymatter) #' #' @keywords datasets #' @seealso [NIRmilk] #' @examples #' #' data(nir_milk) #' "nir_milk" #' Weight and size of 20 potatoes #' #' Weight and size of 20 potatoes. Weight in grams; size in millimeter. There #' are two sizes: \code{length} is the longest length and \code{width} is the #' shortest length across a potato. #' #' @name potatoes #' @docType data #' #' @format A data frame with 20 observations on the following 3 variables. #' \describe{ #' \item{\code{weight}}{a numeric vector} #' \item{\code{length}}{a numeric vector} #' \item{\code{width}}{a numeric vector} #' } #' #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @source My own garden; autumn 2015. #' @keywords datasets #' @examples #' #' data(potatoes) #' plot(potatoes) #' "potatoes" #' Mathematics marks for students #' #' The \code{mathmark} data frame has 88 rows and 5 columns. #' #' @name data_mathmark #' #' @aliases mathmark math #' @format This data frame contains the following columns: mechanics, vectors, #' algebra, analysis, statistics. #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @references David Edwards, An Introduction to Graphical Modelling, Second #' Edition, Springer Verlag, 2000 #' @keywords datasets #' @usage data(mathmark) #' #' @examples #' #' data(mathmark) #' "mathmark" "math" #' @title Budworm data #' #' @description Experiment on the toxicity to the tobacco budworm #' Heliothis virescens of doses of the pyrethroid #' trans-cypermethrin to which the moths were beginning to show #' resistance. Batches of 20 moths of each sex were exposed for #' three days to the pyrethroid and the number in each batch that #' were dead or knocked down was recorded. Data is reported in #' Collett (1991, p. 75). #' #' @concept data #' @name data_budworm #' @docType data #' #' @format This data frame contains 12 rows and 4 columns: #' #' \describe{ #' \item{sex:}{sex of the budworm.} #' \item{dose:}{dose of the insecticide trans-cypermethrin (in micro grams)}. #' \item{ndead:}{budworms killed in a trial.} #' \item{ntotal:}{total number of budworms exposed per trial.} #' } #' #' @references Venables, W.N; Ripley, B.D.(1999) Modern Applied Statistics with #' S-Plus, Heidelberg, Springer, 3rd edition, chapter 7.2 #' #' @source Collett, D. (1991) Modelling Binary Data, Chapman & Hall, London, #' Example 3.7 #' #' #' @keywords datasets #' @examples #' #' data(budworm) #' #' ## function to caclulate the empirical logits #' empirical.logit<- function(nevent,ntotal) { #' y <- log((nevent + 0.5) / (ntotal - nevent + 0.5)) #' y #' } #' #' #' # plot the empirical logits against log-dose #' #' log.dose <- log(budworm$dose) #' emp.logit <- empirical.logit(budworm$ndead, budworm$ntotal) #' plot(log.dose, emp.logit, type='n', xlab='log-dose',ylab='emprirical logit') #' title('budworm: emprirical logits of probability to die ') #' male <- budworm$sex=='male' #' female <- budworm$sex=='female' #' lines(log.dose[male], emp.logit[male], type='b', lty=1, col=1) #' lines(log.dose[female], emp.logit[female], type='b', lty=2, col=2) #' legend(0.5, 2, legend=c('male', 'female'), lty=c(1,2), col=c(1,2)) #' #' \dontrun{ #' * SAS example; #' data budworm; #' infile 'budworm.txt' firstobs=2; #' input sex dose ndead ntotal; #' run; #' } #' #' "budworm" #' Coronary artery disease data #' #' A cross classified table with observational data from a Danish #' heart clinic. The response variable is CAD (coronary artery #' disease, some times called heart attack). #' #' @details #' #' Notice that data are collected at a heart clinic, so data do not #' represent the population, but are conditional on patients having #' ended up at the clinic. #' #' * cad1: Complete dataset, 236 cases. #' #' * cad2: Incomplete dataset, 67 cases. Information on (some of) the #' variables 'Hyperchol', 'Smoker' and 'Inherit' is missing. #' #' @name data_cad #' @aliases cad1 cad2 #' @docType data #' @format A data frame with 236 observations on the following 14 variables. #' #' \describe{ #' \item{\code{Sex}}{Sex; a factor with levels \code{Female} \code{Male}} #' #' \item{\code{AngPec}}{Angina pectoris (chest pain attacks); a #' factor with levels \code{Atypical} \code{None} \code{Typical}} #' #' \item{\code{AMI}}{Acute myocardic infarct; a factor with #' levels \code{Definite} \code{NotCertain}} #' #' \item{\code{QWave}}{A reading from an electrocardiogram; a #' factor with levels \code{No} \code{Yes}; Yes means pathological and is a sign of previous myocardial infarction. } #' #' \item{\code{QWavecode}}{a factor with levels \code{Nonusable} #' \code{Usable}. An assesment of whether QWave is reliable.} #' #' \item{\code{STcode}}{a factor with levels #' \code{Nonusable} \code{Usable}. An assesment of whether STchange is reliable.} #' #' \item{\code{STchange}}{A reading from an electrocardiogram; a factor #' with levels \code{No} \code{Yes}. An STchange indicates a blockage of the coronary artery.} #' #' \item{\code{SuffHeartF}}{Sufficient heart frequency; a factor with levels \code{No}, \code{Yes}} #' #' \item{\code{Hypertrophi}}{a factor with levels \code{No}, \code{Yes}. Hypertrophy refers to an #' increased size of the heart muscle due to exercise. } #' #' \item{\code{Hyperchol}}{a factor with levels \code{No} \code{Yes}. Hypercholesterolemia, also called high cholesterol, #' is the presence of high levels of cholesterol in the blood.} #' #' \item{\code{Smoker}}{Is the patient a smoker; a factor with levels \code{No}, \code{Yes}.} #' #' \item{\code{Inherit}}{Hereditary predispositions for CAD; a factor with levels \code{No}, \code{Yes}.} #' #' \item{\code{Heartfail}}{Previous heart failures; a factor with levels \code{No} \code{Yes}} #' #' \item{\code{CAD}}{Coronary Artery Disease; a factor with levels #' \code{No} \code{Yes}}. CAD refers to a reduction of blood flow #' to the heart muscle (commonly known as a heart attack). The #' diagnosis made from biopsies. #' #' } #' #' #' @references Hansen, J. F. (1980). The clinical diagnosis of #' ischaemic heart disease due to coronary artery disease. Danish #' Medical Bulletin #' #' Højsgaard, Søren and Thiesson, Bo (1995). BIFROST - Block recursive #' models Induced From Relevant knowledge, Observations and #' Statistical Techniques. Computational Statistics and Data Analysis, #' vol. 19, p. 155-175 #' #' #' #' @keywords datasets #' @usage data(cad1) #' #' @examples #' #' data(cad1) #' ## maybe str(cad1) ; plot(cad1) ... #' "cad1" "cad2" #' Personality traits #' #' The `peronality` dataframe has 240 rows and 32 columns #' #' @name data_personality #' #' @format This dataframe has recordings on the following 32 #' variables: distant, talkatv, carelss, hardwrk, anxious, #' agreebl, tense, kind, opposng, relaxed, disorgn, outgoin, #' approvn, shy, discipl, harsh, persevr, friendl, worryin, #' respnsi, contrar, sociabl, lazy, coopera, quiet, organiz, #' criticl, lax, laidbck, withdrw, givinup, easygon #' #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} #' @references Origin unclear #' @keywords datasets #' @usage data(personality) #' @examples #' data(personality) #' str(personality) "personality" doBy/R/plot_functions.r0000644000176200001440000002116315030071144014577 0ustar liggesusers#' @title Plot linear model object #' #' @param lm_fit An object of class 'lm' #' @param format The format of the plot (or a list of plots if format is "list") #' @param global_aes Currently no effect. #' #' @examples #' data(income) #' m1 <- lm(inc ~ race + educ, data=income) #' plot_lm(m1) #' plot_lm(m1, "2x2") #' plot_lm(m1, "1x4") #' plot_lm(m1, "4x1") #' plot_lm(m1, "list") #' #' @export plot_lm <- function(lm_fit, format="2x2", global_aes=NULL) { if(!inherits(lm_fit, "lm")) stop("'lm_fit' must inherit form 'lm'\n") format <- match.arg(format, c("2x2", "1x4", "4x1", "list")) dd <- data.frame( fitted_values = predict(lm_fit), resp = predict(lm_fit)+resid(lm_fit), std_res = rstandard(lm_fit)) pl1 <- ggplot(dd, aes(x = .data$fitted_values, y = .data$std_res)) + geom_point() + geom_hline(yintercept=c(0, -1.96, 1.95)) pl2 <- ggplot(dd, aes(x=.data$fitted_values, y=.data$resp)) + geom_point() + geom_smooth(method="lm", formula=y ~ x, se=FALSE) pl3 <- ggplot(dd, aes(x=.data$fitted_values, y=sqrt(abs(.data$std_res)))) + geom_point() pl4 <- ggplot(dd, aes(sample = .data$std_res)) + stat_qq() + stat_qq_line() + labs(y="std_res", x = "theoretical quantiles") switch(format, "2x2"={ cowplot::plot_grid(pl1, pl2, pl3, pl4, nrow=2) }, "1x4"={ cowplot::plot_grid(pl1, pl2, pl3, pl4, nrow=1) }, "4x1"={ cowplot::plot_grid(pl1, pl2, pl3, pl4, nrow=4) }, "list"={ list(pl1=pl1, pl2=pl2, pl3=pl3, pl4=pl4) } ) } #' @title Two-way interaction plot #' #' @description Plots the mean of the response for #' two-way combinations of factors, thereby illustrating possible #' interactions. #' #' @name interaction-plot #' #' @note This is a recent addition to the package and is subject to change. #' #' @param .data A data frame #' @param .formula A formula of the form `y ~ x1 + x2` #' @param interval Either `conf.int`, `boxplot` or `none` #' #' @examples #' #' income$educf <- cut(income$educ, breaks=3) #' income |> interaction_plot(inc ~ race + educf) #' income |> interaction_plot(inc ~ race + educf, interval="conf.int") #' income |> interaction_plot(inc ~ race + educf, interval="boxplot") #' income |> interaction_plot(inc ~ race + educf, interval="none") #' #' @import dplyr #' @import ggplot2 #' @export interaction_plot <- function(.data, .formula, interval="conf.int"){ interval = match.arg(tolower(interval), c("conf.int", "boxplot", "none")) if (!inherits(.formula, "formula")) stop("'.formula' is not formula") if (length(.formula) != 3) stop("'.formula' is not two sided") lhs <- all.vars(.formula[[2]]) rhs <- all.vars(.formula[[3]]) if (length(rhs) < 2) stop("rhs must have at least two elements") rr <- sym(lhs) s1 <- sym(rhs[1]) s2 <- sym(rhs[2]) ## s2 <- sym(rhs[-1]) # lapply(rhs, sym) ## str(list(rr, s1, s2)) ## If lhs is transformed resp <- eval(.formula[[2]], .data) .data$RESP <- resp ## KLUDGY rr2 <- sym("RESP") ## KLUDGY dd1 <- .data |> group_by(!!sym(s1), !!sym(s2)) tmp <- dd1 |> summarise(val = mean({{ rr2 }}), sd = sd({{ rr2 }}) / sqrt(n()), lwr = .data$val - 1.96 * sd, upr=.data$val + 1.96 * sd, .groups="keep") ##print(.data); print(dd1); print(tmp) switch(interval, "boxplot" = { ## BOXPLOT pp <- ggplot(.data, aes(x = factor(!!sym(s1)), y = !!sym(rr2), colour = !!sym(s2))) pp + labs(y = deparse(.formula[[2]]), x=rhs[1]) + geom_boxplot() + ## FIXME want conf. interval bars geom_point(data = tmp, aes(y = .data$val)) + geom_line(data = tmp, aes(y = .data$val, group = !!sym(s2))) + theme_bw() }, "conf.int" = { ## mean +/- 2 sd pp <- ggplot(tmp, aes(x = factor(!!sym(s1)), y = .data$val, colour = !!sym(s2))) pp + labs(y = deparse(.formula[[2]]), x=rhs[1]) + geom_point(data = tmp, aes(y = .data$val)) + geom_line(data = tmp, aes(y = .data$val, group = !!sym(s2))) + geom_errorbar(aes(ymin=.data$lwr, ymax=.data$upr), data=tmp, width=.4, position=position_dodge(0.1)) + geom_point(aes(x = factor(!!sym(s1)), y = !!sym(rr2), colour = !!sym(s2)), data=.data) }, "none" = { pp <- ggplot(tmp, aes(x = factor(!!sym(s1)), y = .data$val, colour = !!sym(s2))) pp + labs(y = deparse(.formula[[2]]), x=rhs[1]) + geom_point(data = tmp, aes(y = .data$val)) + geom_line(data = tmp, aes(y = .data$val, group = !!sym(s2))) }) } #' Plot the response variable against the predictor variables. #' #' @param formula. A formula of the form y ~ x1 + x2 + ... + xn, where #' y is the response variable and x1, x2, ..., xn are the #' predictor variables. A dot as right hand side is allowed. #' #' @param data. A data frame containing the variables in the formula. #' @param geoms A list of ggplot2 geoms to be added to the plot. #' @param global_aes A list of global aesthetics to be added to the plot. #' @param plot A logical value indicating whether the plot should be displayed. #' @param nrow,ncol Number of rows / columns in plot. #' #' @return A list of ggplot2 plots. #' @export #' #' @examples #' library(ggplot2) #' response_plot(iris, Sepal.Width ~ ., geoms=geom_point()) #' response_plot(iris, Sepal.Width ~ ., geoms=geom_point(), global_aes=list(color="Species")) #' personality |> response_plot(easygon~., geoms=geom_point(), global_aes=NULL) #' response_plot <- function(data., formula., geoms=NULL, global_aes=NULL, plot=TRUE, nrow=NULL, ncol=NULL) { trms <- terms(formula., data=data.) ## trms yy <- as.character(formula.[[2]]) xx <- setdiff(attr(trms, "term.labels"), yy) ## list(x=xx, y=yy) |> str() aes_template <- lapply(xx, function(x_){ c(list(x=x_, y=yy), global_aes) }) ## aes_template aes_list <- aes_template |> lapply(ggpubr_create_aes) ## aes_list plot_basic <- lapply(aes_list, function(z_){ data. |> ggplot(mapping = z_) }) ## plot_basic plot_list <-lapply(plot_basic, function(pl_) { pl_ + geoms } ) if (plot){ s <- cowplot::plot_grid(plotlist = plot_list, nrow=nrow, ncol=ncol) print(s) } return(invisible(plot_list)) } ### Taken from ggpubr::create_aes to avoid circular dependencies ggpubr_create_aes <- function (.list, parse = TRUE) { # if (missing(parse)) { # parse <- base::getOption("ggpubr.parse_aes", default = TRUE) # } # if (parse) { return(create_aes.parse(.list)) # } # else { # return(create_aes.name(.list)) # } } create_aes.parse <- function (.list) { # .list <- .list %>% purrr::map(function(x) parse_expression(x)) .list <- .list |> lapply(function(x) parse_expression(x)) do.call(ggplot2::aes, .list) } parse_expression <- function (x) { if (is_parsable_aes(x)) { if (contains_space(x)) { if (!is_math_string(x)) return(as.name(x)) } x <- parse(text = x)[[1]] } x } is_numeric_char <- function (x) { if (is.character(x)) res <- grepl("^[[:digit:]]+$", x) else res <- FALSE res } is_parsable_aes <- function (x) { is.character(x) & (!is_numeric_char(x)) & (length(x) == 1) } contains_space <- function (x) { grepl("\\s", x) } is_math_string <- function (x) { operators <- c("+", "-", "*", "^", "%%", "%/%", "/", "==", ">", "<", "!=", "<=", ">=") contains_math_operators <- unlist(lapply(operators, grepl, x, fixed = TRUE)) contains_parentheses <- grepl(pattern = "\\(.*\\)", x) any(c(contains_math_operators, contains_parentheses)) } # parse_as_expression <- function (text) # { # stopifnot(is.character(text)) # out <- vector("expression", length(text)) # for (i in seq_along(text)) { # expr <- parse(text = text[[i]]) # out[[i]] <- if (length(expr) == 0) # NA # else expr[[1]] # } # out # } doBy/R/linest_matrix.R0000644000176200001440000002172714752232672014400 0ustar liggesusers## ############################################################################# #' #' @title Linear estimates matrix #' @description Generate matrix specifying linear estimate. #' @name linest-matrix #' ## ############################################################################# #' #' @param object Model object #' #' @param effect A vector of variables. For each configuration of #' these the estimate will be calculated. #' #' @param at Either NULL, a list or a dataframe. 1) If a list, then the list #' must consist of covariates (including levels of some factors) #' to be used in the calculations. 2) If a dataframe, the #' dataframe is split rowwise and the function is invoked on each #' row. #' #' @param linest_list Linear estimate list (as generated by \code{get_linest_list}). #' #' @details Check this #' #' @seealso \code{\link{LSmeans}}, \code{\link{linest}} #' @keywords utilities #' #' @examples #' #' ## Two way anova: #' #' data(warpbreaks) #' #' ## An additive model #' m0 <- lm(breaks ~ wool + tension, data=warpbreaks) #' #' ## Estimate mean for each wool type, for tension="M": #' K <- LE_matrix(m0, at=list(wool=c("A", "B"), tension="M")) #' K #' #' ## Vanilla computation: #' K %*% coef(m0) #' #' ## Alternative; also providing standard errors etc: #' linest(m0, K) #' esticon(m0, K) #' #' ## Estimate mean for each wool type when averaging over tension; #' # two ways of doing this #' K <- LE_matrix(m0, at=list(wool=c("A", "B"))) #' K #' K <- LE_matrix(m0, effect="wool") #' K #' linest(m0, K) #' #' ## The linear estimate is sometimes called to "least squares mean" #' # (LSmeans) or popupulation means. #' # Same as #' LSmeans(m0, effect="wool") #' #' ## Without mentioning 'effect' or 'at' an average across all #' #predictors are calculated: #' K <- LE_matrix(m0) #' K #' linest(m0, K) #' #' ## Because the design is balanced (9 observations per combination #' #of wool and tension) this is the same as computing the average. If #' #the design is not balanced, the two quantities are in general not #' #the same. #' mean(warpbreaks$breaks) #' #' ## Same as #' LSmeans(m0) #' #' ## An interaction model #' m1 <- lm(breaks ~ wool * tension, data=warpbreaks) #' #' K <- LE_matrix(m1, at=list(wool=c("A", "B"), tension="M")) #' K #' linest(m1, K) #' K <- LE_matrix(m1, at=list(wool=c("A", "B"))) #' K #' linest(m1, K) #' K <- LE_matrix(m1, effect="wool") #' K #' linest(m1, K) #' LSmeans(m1, effect="wool") #' #' K <- LE_matrix(m1) #' K #' linest(m1, K) #' LSmeans(m1) #' @export #' @rdname linest-matrix LE_matrix <- function(object, effect=NULL, at=NULL){ UseMethod("LE_matrix") } ## FIXME: LE_matrix.default: Should be a check of what 'object' is #' @export #' @rdname linest-matrix LE_matrix.default <- function(object, effect=NULL, at=NULL){ effect <- .parse.effect(effect) ## print("HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH") ## str(list(effect=effect, at=at)) if (!is.null(at)) if (!inherits(at, c("list", "data.frame"))) stop("'at' must be list or data.frame\n") if (!inherits(at, "data.frame")){ out <- get_linest_list(object, effect, at) out <- aggregate_linest_list (out) } else { ## call function for each row in at at.list <- split(at, 1:nrow(at)) at.list <- lapply(at.list, as.list) out <- lapply(at.list, function(at.arg) LE_matrix(object, effect, at.arg)) out <- do.call(rbind, out) } class(out) <- c("linest_matrix_class", "matrix") out } #' @export #' @rdname linest-matrix aggregate_linest_list <- function (linest_list){ out <- lapply( linest_list, function( mm ) apply( mm, 2, mean ) ) out <- do.call(rbind, out) attr(out, "at") <- attr(linest_list, "at") attr(out, "grid") <- attr(linest_list, "grid") attr(out, "offset") <- attr(linest_list, "offset") out } #' @export print.linest_matrix_class <- function(x, ...){ prmatrix(x) invisible(x) } #' @export summary.linest_matrix_class <- function(object, ...){ print(object) cat("at: \n"); print(attr(object, "at")) cat("grid: \n"); print(attr(object, "grid")) invisible(object) } .parse.effect <- function(effect=NULL){ ## All returns c("A", "B") ## parse.effect(~A+B) ## parse.effect(y~A+B) ## parse.effect(c("A", "B")) if (is.null(effect)) return(NULL) if (!inherits(effect, c("character", "formula"))) stop("'effect' must be a character vector or a formula") if (inherits(effect, "character")){ if (identical(effect, character(0)) | identical(effect, "")) NULL else effect } else { all.vars(effect[[length(effect)]]) } } ## This is the workhorse for generating the "contrast matrix" #' @export #' @rdname linest-matrix get_linest_list <- function(object, effect=NULL, at=NULL) { pr <- FALSE ##cat(".get_linest_list\n") effect <- .parse.effect(effect) trms <- delete.response( terms(object) ) fact.lev <- get_xlevels( object ) ## factor levels ## cat("+++ fact.lev:\n"); print(fact.lev) cov.ave <- .get_covariate_ave(object, at) ## average of covariates (except those mentioned in 'at') ## cat("+++ cov.ave:\n"); print(cov.ave) vartype <- get_vartypes( object ) ## which are factors and which are numerics ## cat("+++ vartype:\n"); print(vartype) at.factor.name <- intersect(vartype$factor, names(at)) cov.ave.name <- names( cov.ave ) effect <- setdiff( effect, at.factor.name ) if (is.null(effect)) { new.fact.lev <- if (length(at.factor.name) > 0) at[ at.factor.name ] else NULL } else { zz <- set_xlevels(fact.lev, at=at) ## print(fact.lev) ## print(zz) new.fact.lev <- zz[c(effect, at.factor.name)] } ## str(list(effect=effect, at=at, fact.lev=fact.lev, new.fact.lev=new.fact.lev, ## vartype=vartype, cov.ave=cov.ave, at.factor.name=at.factor.name, ## cov.ave.name=cov.ave.name)) if (is.null(new.fact.lev)){ ##cat("case: No 'effect' and no 'at'-factors; hence just a global average. \n") if (length(fact.lev) > 0) { ##cat("there are factors in the model\n") newdata <- expand.grid(fact.lev) if (length( cov.ave.name ) > 0){ ##cat("there are covariates in the model\n") newdata[, cov.ave.name] <- cov.ave ## Augment with covariates } } else { ## cat("No factors in the model\n") if (length( cov.ave.name ) > 0){ ##cat("yes there are covariates\n") newdata <- data.frame(matrix(unlist(cov.ave), nrow=1L)) names(newdata) <- cov.ave.name } else { ##cat("there are no factors or covariates\n") newdata <- data.frame(1) } } out_list <- list(get_X(object, newdata=newdata, at=NULL)) ## cat("out_list:\n"); print(out_list) attr(out_list, "at") <- at[intersect(vartype$numeric, names(at))] attr(out_list, "grid") <- NULL } else { if(pr)cat("The general case; there are 'effect' factors or 'at' factors.\n") grid.data <- expand.grid(new.fact.lev) ## print(grid.data) grid.data <- as.data.frame(lapply(grid.data, as.character), stringsAsFactors=FALSE) ## if (length( cov.ave.name ) > 0){ ## ##cat("there are covariates in the model\n") ## grid.data[, cov.ave.name] <- cov.ave ## Augment with covariates ## } ## nfl <<- new.fact.lev ## gd <<- grid.data str(list(new.fact.lev=new.fact.lev, grid.data=grid.data)) out_list <- list() for (ii in 1:nrow(grid.data)){ config <- grid.data[ii, ,drop=FALSE ] ## print(config) fact.lev2 <- set_xlevels(fact.lev, at=config) newdata <- expand.grid(fact.lev2) newdata[, cov.ave.name] <- cov.ave XX <- get_X(object, newdata=newdata, at=at) ## str(list(XX=XX)) ## print(XX) out_list[[ ii ]] <- XX } grid.data[, names(cov.ave) ] <- cov.ave attr(out_list, "at") <- at attr(out_list, "grid") <- grid.data attr(out_list, "offset") <- attr(XX, "offset") ## FIXME: reference to XX; what is offset? } class(out_list) <- "linest_list_class" out_list } ## -------------------------------------------------------------------- setOldClass("linest_matrix_class") setAs("linest_matrix_class", "matrix", function(from){ attr(from, "at") <- attr(from, "grid") <- NULL class(from) <- "matrix" from }) setAs("linest_matrix_class", "Matrix", function(from){ attr(from, "at") <- attr(from, "grid") <- NULL class(from) <- "matrix" as(from, "Matrix") }) doBy/R/recover_pca_data.R0000644000176200001440000000273514551451413014766 0ustar liggesusers##' Recover data from principal component analysis ##' ##' Recover data from principal component analysis based on the first (typically few) components. ##' ##' @param object An object of class `prcomp`. ##' @param comp The number of components to be used. Must be smaller ##' than the number of variables. ##' ##' @return A dataframe ##' ##' @examples ##' ##' crime <- doBy::crimeRate ##' rownames(crime) <- crime$state ##' crime$state <- NULL ##' ##' o <- order(apply(scale(crime), 1, sum)) ##' dat <- crime[o,] ##' head(dat) ##' tail(dat) ##' matplot(scale(dat), type="l") ##' ##' pc1 <- prcomp(dat, scale. = TRUE) ##' summary(pc1) ##' rec2 <- recover_pca_data(pc1, 2) ##' ##' pairs(rec2) ##' ##' par(mfrow=c(1,2)) ##' matplot(scale(dat), type="l") ##' matplot(scale(rec2), type="l") ##' ##' j <- merge(dat, rec2, by=0) ##' pairs(j[,-1]) ##' ##' @export recover_pca_data <- function (object, comp = 1) { if (!inherits(object, "prcomp")) stop("'object' must be of class 'prcomp'.") if (length(comp) == 1) comp <- 1:comp if (is.null(object$x)){ stop("Can not 'recover' data.\n") } ## x <- object$x rot <- object$rot vn <- row.names(rot) if (object$scale[1]) { rot <- diag(object$scale) %*% rot } pred <- object$x[, comp, drop = FALSE] %*% t.default(rot[, comp, drop = FALSE]) if (object$center[1]) { pred <- t(t(pred) + object$center) } colnames(pred) <- vn return(pred) } doBy/R/by_orderBy.R0000644000176200001440000000572615030273432013604 0ustar liggesusers######################################################################### #' #' @title Ordering (sorting) rows of a data frame #' @description Ordering (sorting) rows of a data frame by the certain #' variables in the data frame. This function is essentially a #' wrapper for the \code{order()} function - the important #' difference being that variables to order by can be given by a #' model formula. #' @name by-order #' ######################################################################### #' #' @details The sign of the terms in the formula determines whether #' sorting should be ascending or decreasing; see examples below #' #' @param formula The right hand side of a formula #' @param data A dataframe #' @return The ordered data frame #' @author Søren Højsgaard, \email{sorenh@@math.aau.dk} and Kevin #' Wright #' @seealso \code{\link{transformBy}}, \code{\link{transform_by}}, \code{\link{splitBy}}, \code{\link{split_by}} #' @keywords utilities #' @examples #' #' orderBy(~ conc + Treatment, CO2) #' ## Sort decreasingly by conc #' orderBy(~ - conc + Treatment, CO2) #' ## Same as: #' order_by(CO2, c("conc", "Treatment")) #' order_by(CO2, c("-conc", "Treatment")) #' @export #' @rdname by-order order_by <- function(data, formula){ cl <- match.call(expand.dots = TRUE) cl[[2]] <- formula cl[[3]] <- data names(cl)[2:3] <- c("formula", "data") cl[[1]] <- as.name("orderBy") eval(cl) } #' @export #' @rdname by-order orderBy <- function (formula, data){ if (!inherits(data, "tbl_df")) is.tib = FALSE else {is.tib = TRUE; data = as.data.frame(data)} myrank <- function(x){ rv <- rep(NA, length(x)) r <- rank(cout[!is.na(cout)]) rv[!is.na(cout)] <- r rv[is.na(rv)] <- max(r) + 1 rv } if (is.vector(formula)) ## E.g. c("Temp", "Month") formula <- as.formula(paste0("~ ", paste(formula, collapse=" + "))) form <- formula out <- data if(form[[1]] != "~") stop("Error: Formula must be one-sided.") formc <- as.character(form[2]) formc <- gsub(" ", "", formc) if(!is.element(substring(formc, 1, 1),c("+", "-"))) formc <- paste("+", formc, sep="") vars <- unlist(strsplit(formc, "[\\+\\-]")) vars <- vars[vars != ""] # Remove spurious "" terms signs <- formc for (i in 1:length(vars)){ signs <- gsub(vars[i], "", signs) } signs <- unlist(strsplit(signs, "")) orderlist <- list() for(i in 1:length(vars)){ csign <- signs[i] cvar <- vars[i] cout <- out[, cvar] if (is.factor(cout)){ orderlist[[i]] <- if (csign == "-") -myrank(cout) else myrank(cout) } else { orderlist[[i]] <- if (csign == "-") -cout else cout } } out <- out[do.call("order", orderlist),, drop=FALSE] if (is.tib) as_tibble(out) else out ##out } doBy/R/functional_coerce_expr_to_function.R0000644000176200001440000000471114746511461020636 0ustar liggesusers#' Convert expression into function object. #' #' @param expr_ R expression. #' @param order desired order of function argument. #' @param vec_arg should the function take vector valued argument. #' #' @examples #' #' ee <- expression(b1 + (b0 - b1)*exp(-k*x) + b2*x) #' ff1 <- expr_to_fun(ee) #' formals(ff1) #' #' ff2 <- expr_to_fun(ee, vec_arg=TRUE) #' formals(ff2) #' formals(ff2)$length_parm #' formals(ff2)$names_parm |> eval() #' #' ee <- expression(matrix(c(x1+x2, x1-x2, x1^2+x2^2, x1^3+x2^3), nrow=2)) #' ff1 <- expr_to_fun(ee) #' ff2 <- expr_to_fun(ee, vec_arg=TRUE) #' #' formals(ff2) #' formals(ff2)$length_parm #' formals(ff2)$names_parm |> eval() #' #' @export expr_to_fun <- function(expr_, order=NULL, vec_arg=FALSE) { if (vec_arg) { expr_to_one_param_fun(expr_, order=order) } else { expr_to_multi_param_fun(expr_, order=order) } } handle_order <- function(e, order) { if (is.null(order)) return(sort(all.vars(e))) nms <- all.vars(e) ss <- setdiff(nms, order) if (length(ss) > 0) { stop("some arguments are not given in 'order'") } return(order) } expr_to_one_param_fun <- function(e, order=NULL) { nms <- handle_order(e, order) e_str <- expr_to_string(e) if (length(nms)) { aux <- sapply(1:length(nms), function(i) { nm <- nms[i] paste0(nm, " = parm[", i, "]") } ) } else { aux <- NULL } comb <- c(aux, e_str) uuu <- paste("c(",paste0(sQuote(nms, q="'"), collapse=", "), ")") fun_str <- sprintf("function(parm, length_parm=%d, names_parm=%s)", length(nms), uuu) bd <- paste0("\n{ \n ", paste0(comb, collapse=";\n "), "\n}") ff <- paste0(fun_str, bd) fun <- eval(parse(text=ff)) return(fun) } expr_to_multi_param_fun <- function(ee, order=NULL) { nms <- handle_order(ee, order) ee_str <- expr_to_string(ee) fun_str <- paste0("function(", paste0(nms, collapse=", "), ")") bd <- paste0("\n{ \n", paste0(ee_str, collapse=";\n "), "\n}") ff <- paste0(fun_str, bd) fun <- eval(parse(text=ff)) return(fun) } expr_to_string <- function(ee) { ee_str <- lapply(ee, deparse) ee_str <- lapply(ee_str, function(e_) { paste0(e_, collapse="\n") }) ee_str } doBy/R/NAMESPACE_doby.R0000644000176200001440000000171714772257145014030 0ustar liggesusers#' @import methods #' @importFrom MASS ginv Null #' #' @importFrom Matrix rowMeans colMeans rowSums colSums #' @importFrom stats model.response model.weights rstandard rstudent residuals #' #' @importClassesFrom Matrix Matrix #' @importFrom Matrix Matrix sparseMatrix #' @importMethodsFrom Matrix t isSymmetric "%*%" solve diag #' @importFrom stats as.formula coef delete.response family fitted #' formula ftable lm logLik median model.frame model.matrix pchisq pnorm #' pt qnorm qt residuals sd summary.lm terms vcov aggregate #' printCoefmat confint predict resid rstandard step update sigma #' #' @importFrom modelr rmse #' @importFrom microbenchmark microbenchmark #' @importFrom Deriv Deriv #' @importFrom utils str help #' @importFrom methods setOldClass #' @importFrom cowplot plot_grid #' @importFrom rlang enquo #' @importFrom tidyr pivot_longer #' @importFrom dplyr mutate select #' @importFrom tibble as_tibble #' @importFrom broom tidy NULL doBy/vignettes/0000755000176200001440000000000015030320366013155 5ustar liggesusersdoBy/vignettes/linest_lsmeans.Rnw0000644000176200001440000012441514735726554016720 0ustar liggesusers% \VignetteEngine{knitr::knitr} %\VignettePackage{doBy} %\VignetteIndexEntry{doBy: Linear estimates and LSmeans} %\VignetteIndexEntry{LSmeans} %\VignetteIndexEntry{population means} %\VignetteIndexEntry{contrasts} %\VignetteIndexEntry{estimable functions} \documentclass[11pt]{article} \usepackage{hyperref,url,color,Sweave,a4wide} \usepackage[utf8]{inputenc} \usepackage[authoryear,round]{natbib} \bibliographystyle{plainnat} \RequirePackage{color,fancyvrb,amsmath,amsfonts} \def\textasciigrave{'} \def\tt#1{\texttt{#1}} \def\R{\texttt{R}} \def\pkg#1{{\bf #1}} \def\doby{\pkg{doBy}} \def\code#1{\texttt{#1}} \def\cc#1{\texttt{#1}} \def\comi#1{{\it #1}} \def\esticon{\code{esticon()}} \def\lsmeans{\code{LSmeans}} \def\linmat{\code{LE_matrix()}} \def\linest{\code{linest()}} % reduce whitespace between R code and R output % \let\oldknitrout\knitrout % \renewenvironment{knitrout}{ % \begin{oldknitrout} % \footnotesize % \topsep=0pt % }{ % \end{oldknitrout} % } \DeclareMathOperator{\EE}{\mathbb{E}} \DeclareMathOperator{\var}{\mathbb{V}ar} \DeclareMathOperator{\cov}{\mathbb{C}ov} \DeclareMathOperator{\norm}{N} \DeclareMathOperator{\spanx}{span} \DeclareMathOperator{\corr}{Corr} \DeclareMathOperator{\deter}{det} \DeclareMathOperator{\trace}{tr} \def\inv{^{-1}} \newcommand{\transp}{^{\top}} <>= require( doBy ) prettyVersion <- packageDescription("doBy")$Version prettyDate <- format(Sys.Date()) @ \title{Linear estimates and LS--means in the \texttt{doBy} package} \author{S{\o}ren H{\o}jsgaard and Ulrich Halekoh} \date{\pkg{doBy} version \Sexpr{prettyVersion} as of \Sexpr{prettyDate}} % show R> prompt before R commands <>= knitr::opts_chunk$set(prompt=TRUE) library(knitr) if (!dir.exists("figures")) dir.create("figures") opts_chunk$set( tidy=FALSE,fig.path='figures/LSmeans', fig.height=3.5 ) oopt <- options() options("digits"=4, "width"=90, "prompt"="> ", "continue"=" ") options(useFancyQuotes="UTF-8") library(ggplot2) @ \definecolor{darkred}{rgb}{.7,0,0} \definecolor{midnightblue}{rgb}{0.098,0.098,0.439} \begin{document} <>= library(knitr) opts_chunk$set( concordance=TRUE ) @ % \SweaveOpts{concordance=TRUE} \maketitle \tableofcontents \parindent0pt \parskip5pt \section{Introduction} \label{sec:introduction} \subsection{Linear functions of parameters} \label{sec:line-funct-param} A linear function of a $p$--dimensional parameter vector $\beta$ has the form \begin{displaymath} C=L\beta \end{displaymath} where $L$ is a $q\times p$ matrix which we call the \comi{Linear Estimate Matrix} of simply \comi{LE-matrix}. The corresponding linear estimate is $\hat C = L \hat \beta$. A \comi{linear hypothesis} has the form $H_0: L\beta=m$ for some $q$ dimensional vector $m$. \subsection{LSmeans (population means, marginal means)} \label{sec::lsmeans} A special type of linear estimates is the so called \comi{least-squares means} (or \comi{LS-means}). Other names for these estimates include \comi{population means} and \comi{marginal means}. Consider an imaginary field experiment analyzed with model of the form <>= lm( y ~ treat + block + year) @ %def where \cc{treat} is a treatment factor, \cc{block} is a blocking factor and \cc{year} is the year (a factor) where the experiment is repeated over several years. This model specifies the conditional mean $\EE(Y|\cc{treat}, \cc{block},\cc{year})$. One may be interested in predictions of the form $\EE(Y|\cc{treat})$. This quantity can not formally be found from the model. However, it is tempting to average the fitted values of $\EE(Y|\cc{treat}, \cc{block},\cc{year})$ across the levels of \cc{block} and \cc{year} and think of this average as $\EE(Y|\cc{treat})$. This average is precisely what is called the LS--means. If the experiment is balanced then this average is identical to the average of the observations when stratified according to \cc{treat}. An alternative is to think of \cc{block} and \cc{year} as random effects, for example: <>= library(lme4) lmer( y ~ treat + (1|block) + (1|year)) @ %def In this case one would directly obtain $\EE(Y|\cc{treat})$ from the model. However, there are at least two reasons why one may be hesitant to consider such a random effects model. \begin{itemize} \item Suppose there are three blocks and the experiment is repeated over three consecutive years. This means that the random effects are likely to be estimated with a large uncertainty (the estimates will have only two degrees of freedom). \item Furthermore, treating \cc{block} and \cc{year} as random effects means they should in principle come from a large population of possible blocks and years. This may or may not be reasonable for the blocks, but it is a questionable assumption for the years. \end{itemize} \subsection{A simulated dataset} \label{sec:simulated-dataset} In the following sections we consider these data: <<>>= library(doBy) set.seed(141164) dd <- expand.grid(A=factor(1:3), B=factor(1:3), C=factor(1:2)) dd$y <- rnorm(nrow(dd)) dd$x <- rnorm(nrow(dd))^2 dd$z <- rnorm(nrow(dd)) head(dd,10) @ %def Consider the additive model \begin{equation} \label{eq:1} y_i = \beta_0 + \beta^1_{A(i)}+\beta^2_{B(i)} + \beta^3_{C(i)} + \beta^4 x_i + e_i \end{equation} where $e_i \sim N(0,\sigma^2)$. We fit this model: <<>>= mm <- lm(y ~ A + B + C + x, data=dd) coef(mm) @ %def Notice that the parameters corresponding to the factor levels \code{A1}, \code{B1} and \code{C2} are set to zero to ensure identifiability of the remaining parameters. \subsection{LSmeans: details} \label{sec:what-are-these} LSmeans, population means and marginal means are used synonymously in the literature. These quantities are a special kind of contrasts as defined in Section~\ref{sec:line-funct-param}. LSmeans seems to be the most widely used term, so we shall adopt this terms here too. The model (\ref{eq:1}) is a model for the conditional mean $\EE(y|A,B,C,x)$. Sometimes one is interested in quantities like $\EE(y|A)$. This quantity can not formally be found because of the presences of unless $B$, $C$ and $x$. However, suppose that $A$ is a treatment of main interest, $B$ is a blocking factor, $C$ represents days on which the experiment was carried out and $x$ denotes e.g. temperature for the specific block on the specific date. Then it is tempting to fix $x$ at its average value $\bar x$ and average $\EE(y|A,B,C, \bar x)$ over $B$ and $C$ (average over block and day) and think of this average as $\EE(y|A)$. The population mean for $A=1$ is \begin{equation} \label{eq:2} \beta^0 + \beta^1_{A1} + \frac{1}{3} (\beta^2_{B1}+\beta^2_{B2}+\beta^2_{B3}) + \frac{1}{2}(\beta^3_{C1}+\beta^3_{C2}) \end{equation} Recall that the parameters corresponding to the factor levels \code{A1}, \code{B1} and \code{C2} are set to zero to ensure identifiability of the remaining parameters. Therefore we may also write the population mean for $A=1$ as \begin{equation} \label{eq:3} \beta^0 + \frac{1}{3} (\beta^2_{B2}+\beta^2_{B3}) + \frac{1}{2}(\beta^3_{C2}) + \beta^4 \bar x. \end{equation} This quantity can be estimated as (if $\bar x = 1.242$) : <<>>= w <- c(1, 0, 0, 1/3, 1/3, 1/2, 1.242) sum(coef(mm) * w) @ %def We may find the population mean for all three levels of $A$ as <<>>= L <- matrix(c(1, 0, 0, 1/3, 1/3, 1/2, 1.242, 1, 1, 0, 1/3, 1/3, 1/2, 1.242, 1, 0, 1, 1/3, 1/3, 1/2, 1.242), nr=3, byrow=TRUE) L %*% coef(mm) @ %def Notice that the matrix W is based on that the first level of $A$ is set as the reference level. If the reference level is changed then so must $W$ be. % Given that one has specified $W$, we can use the \esticon\ function in the % \code{doBy} as illustrated below: % <<>>= % esticon(mm, W) % @ %def \def\popmatrix{LEmatrix} \def\popmeans{LSmeans} \subsection{Using \popmatrix\ and \popmeans} Writing the matrix $W$ is somewhat tedious and hence error prone. In addition, there is a potential risk of getting the wrong answer if the the reference level of a factor has been changed. The \popmatrix\ function provides an automated way of generating such matrices. The above \verb+W+ matrix is constructed by <<>>= L <- LE_matrix(mm, effect='A') L @ %def The \verb+effect+ argument requires to calculate the population means for each level of $A$ aggregating across the levels of the other variables in the data. The \popmeans\ function is simply a wrapper around first a call to \popmatrix\ followed by a call to (by default) \linest: <<>>= linest(mm, L=L) LSM <- LSmeans(mm, effect='A') LSM @ %def \subsection{Special details} \label{sec:special-details} \subsubsection{Summary} \label{sec:summary} More details about how the matrix was constructed is provided by the \code{summary()} method; for example: <<>>= summary(LSM) @ %def \subsubsection{Specification of \code{effect}} \label{sec:spec-code} The \code{effect} argument can be specified in two ways: <<>>= ## 1) a vector of characters and LE_matrix(mm, effect= c("A", "C")) ## 2) a right hand sided formula: ## LE_matrix(mm, effect= ~ A + C) @ \subsubsection{Fixing values in the \code{at} list} \label{sec:fixing-valu-code} We can fix values of the explanatory variables using the \code{at=} argument: For example: <<>>= LE_matrix(mm, at=list(A=1, x=2)) @ \subsubsection{Combining \code{effect} and \code{at}} \label{sec:comb-code-code} <<>>= LE_matrix(mm, effect= ~ A + C, at=list(A=1)) @ \subsubsection{Total average} \label{sec:total-average} Omitting \code{effect} as in <<>>= LE_matrix(mm) LSmeans(mm) @ %def gives the ``total average''. \subsubsection{Log transformed covariates} Unless otherwise specified, numerical covariates are fixed at their average value, for example for \code{x} below: If we use \code{log(x)} instead we will get an error when calculating LS--means. Instead one must modify data: <<>>= mm2 <- lm(y ~ A + B + C + log(x), data=dd) ## LSmeans(mm2, effect=~A) ## Will fail mm2 <- lm(y ~ A + B + C + log.x, data=transform(dd, log.x=log(x))) LSmeans(mm2, effect=~A) LSmeans(mm2, effect=~A)$L @ This also highlights what is computed: The average of the log of \code{x}; not the log of the average of \code{x}. \subsubsection{Powers of covariates} Similar phenomenon for powers of covariates: <<>>= mm2 <- lm(y ~ A + B + C + x + I(x^2), data=dd) LSmeans(mm2, effect=~A) LSmeans(mm2, effect=~A)$L @ %def Above \verb|I(x^2)| is the average of the squared values of \code{x}; not the square of the average of \code{x}, cfr.\ the following. <<>>= mm2 <- lm(y ~ A + B + C + x + x2, data=transform(dd, x2=x^2)) LSmeans(mm2, effect=~A) LSmeans(mm2, effect=~A)$L @ %def % If we want to evaluate the LS--means at \code{conc=10} then we can do: % <<>>= % LSmeans(co2.lm4, effect="Treatment", at=list(conc=10, conc2=100)) % @ %def \section{Miscellaneous} \label{sec:miscellaneous} \subsection{Excerpt of the CO2 data} \label{sec:excerpt-co2-data} For illustration we use subsets of the CO2 data: <<>>= data(CO2) CO2 <- subset(CO2, conc < 300) ## OK CO2.bal <- CO2 rownames(CO2.bal) <- NULL @ % <<>>= % xtabs(~Type + Treatment + conc, data=CO2.bal) |> % ftable(row.vars = "Type") % @ Data is perfectly balanced. We also use an unbalanced subset of the data <<>>= CO2.ubal <- CO2.bal[-c(1, 5, 12, 17, 18, 19, 20, 28),] CO2.ubal |> head() xtabs(~Type + Treatment + conc, data=CO2.ubal) |> ftable(row.vars = "Type") @ % <<>>= % p1 <- ggplot(CO2.bal, aes(x=conc, y=uptake, color=Treatment, group=Plant)) + % geom_line() + geom_point() + facet_grid(~Type) % p2 <- ggplot(CO2.ubal, aes(x=conc, y=uptake, color=Treatment, group=Plant)) + % geom_line() + geom_point() + facet_grid(~Type) % library(patchwork) ## Multiple graphs % p1 / p2 % @ \subsection{Two linear models for CO2 data} \label{sec:two-linear-models} <<>>= library(broom) form.add <- uptake ~ conc + Treatment + Type form.int <- uptake ~ conc * Treatment + Type fm1.bal <- lm(form.add, data=CO2.bal) fm1.ubal <- lm(form.add, data=CO2.ubal) @ \subsection{Linear estimates and LSmeans} \label{sec:line-estim-using} \paragraph{Common task 1:} Consider this task: Estimate the value of the response \tt uptake for each combination of \tt Type and \tt Treatment . This can be obtained, for example, as follows: <<>>= LSmeans(fm1.bal, effect=~Type+Treatment, at=list(conc=100)) LSmeans(fm1.ubal, effect=~Type+Treatment, at=list(conc=100)) @ \paragraph{Common task 2:} Estimate the value of the response \tt uptake when \tt for each level of \tt Treatment. Formally this question can not be answered under the model because the model gives the conditional mean of \tt uptake given \tt conc, \tt Type and \tt Treatment. There is, so to speak, no way of getting rid of \tt Type. There are different workarounds for this situation: We can average over \tt Type and fix \t conc at its average. Alternatively, we can fit model without \tt Type effect and repeat the steps above. <<>>= LSmeans(fm1.bal, effect=~Treatment) LSmeans(fm1.ubal, effect=~Treatment) @ <<>>= ## Fit model without Type fm2.bal <- update(fm1.bal, .~. - Type) fm2.ubal <- update(fm1.ubal, .~. - Type) LSmeans(fm2.bal, effect=~Treatment) LSmeans(fm2.ubal, effect=~Treatment) @ Notice that the predictions (estimates) are identical for the balanced dataset but not for the unbalanced dataset. Notice also that the standard errors of the predictions are somewhat larger when fitting a model without \tt Type effect. This is to be expected as the variation due to \tt Type goes into the residual. \subsection{Generalized linear models} \label{sec:gener-line-models} We can calculate LS--means for e.g.\ a Poisson or a gamma model. Default is that the calculation is calculated on the scale of the linear predictor. <<>>= fm.glm <- glm(form.add, family=Gamma, data=CO2.ubal) LSmeans(fm.glm, effect=~Treatment, type="link") @ %def \subsection{Generalized estimating equations} \label{sec:gener-estim-equat} For gee-type models we get <<>>= library(geepack) fm.gee <- geeglm(uptake ~ conc + Treatment + Type, id=Plant, family=Gamma, data=CO2.ubal) LSmeans(fm.gee, effect=~Treatment) @ %def \subsection{Linear mixed effects model} \label{sec:linear-mixed-effects} For illustration we treat \verb|Plant| as a random effect: << >>= library(lme4) fm.mix <- lmer(uptake ~ conc + Treatment + Type + (1|Plant), data=CO2.ubal) LSmeans(fm.mix, effect=~Treatment) @ Notice that the degrees of freedom by default are adjusted using a Kenward--Roger approximation. Unadjusted degrees of freedom are obtained by setting \verb|adjust.df=FALSE|. \subsection{Pairwise comparisons} \label{sec:pairwise-comparisons} We will just mention that for certain other linear estimates, the matrix $L$ can be generated automatically using \cc{glht()} from the \pkg{multcomp} package. For example, pairwise comparisons of all levels of \code{dose} can be obtained with << >>= library("multcomp") g1 <- glht(fm2.ubal, mcp(Treatment="Tukey")) tidy(g1) @ The L matrix is << >>= L <- g1$linfct L linest(fm2.ubal, L=L) @ % \subsection{Manual computations} % \label{sec:manual-computations} % Averaging over predicted values amounts to averaging over weighted % sums of the regression coefficients. The weights can be generated % manually as: % <<>>= % coef(fm1.ubal) % L01 <- matrix(c(1, 100, 0, 0, % 1, 100, 1, 0, % 1, 100, 0, 1, % 1, 100, 1, 1), ncol=4, byrow=T) % L01 %*% coef(fm1.ubal) % L02 <- matrix(c(1, 100, 0, 0.5, % 1, 100, 1, 0.5), ncol=4, byrow=T) % L02 %*% coef(fm1.ubal) % L03 <- matrix(c(1, 179.8, 0, 0.5, % 1, 179.8, 1, 0.5), ncol=4, byrow=T) % L03 %*% coef(fm1.ubal) % @ % \subsection{Automatic generation of LEmatrix} % \label{sec:autom-gener-lematr} % Averaging over predicted values amounts to averaging over weighted % sums of the regression coefficients. The weights can be generated % automatically as follows: % \paragraph{Common task 1:} % We calulate genereate the $L$ matrix automaically and the matrix and the model object into a function that returns the predictions, standard errors etc. % <<>>= % L1 <- LE_matrix(fm1.ubal, effect=~Treatment + Type, at=list(conc=100)) % L1 % C1 <- linest(fm1.ubal, L=L1) % C1 % @ % The steps above can be made with one function call: % <<>>= % LSM1 <- LSmeans(fm1.ubal, effect=~Treatment + Type, at=list(conc=100)) % LSM1 % @ % \paragraph{Common task 2: } % Likewise, for the second situation: % <<>>= % L2 <- LE_matrix(fm1.ubal, effect=~Treatment, at=list(conc=100)); L2 % C2 <- linest(fm1.ubal, L=L2); C2 % LSM2 <- LSmeans(fm1.ubal, effect=~Treatment, at=list(conc=100)) % LSM2 % @ % \paragraph{Common task 3: } % Finally for the third situation: % <<>>= % L3 <- LE_matrix(fm1.ubal, effect=~Treatment); % L3 % C3 <- linest(fm1.ubal, L=L3); % C3 % LSM3 <- LSmeans(fm1.ubal, effect=~Treatment) % LSM3 % @ % \subsection{ssssssssssssssss} % \label{sec:ssssssssssssssss} % Notice this information: % <<>>= % summary(L3) % summary(C3) % summary(LSM3) % @ % \subsection{Using the \code{at=} argument} % \label{sec:at-argument} % <>= % library(ggplot2) % ChickWeight$Diet <- factor(ChickWeight$Diet) % qplot(Time, weight, data=ChickWeight, colour=Chick, facets=~Diet, % geom=c("point","line")) + theme(legend.position="none") % @ %def % Consider random regression model: % <<>>= % library(lme4) % chick <- lmer(weight ~ Time * Diet + (0 + Time | Chick), % data=ChickWeight) % coef(summary(chick)) % @ %def % The LE--matrix for \cc{Diet} becomes: % <<>>= % L <- LE_matrix(chick, effect="Diet") % t(L) % @ %def % The value of \cc{Time} is by default taken to be the average of that % variable. Hence the \lsmeans\ is the predicted weight for each diet at % that specific point of time. We can consider other points of time with % <<>>= % K1 <- LE_matrix(chick, effect="Diet", at=list(Time=1)) % t(K1) % @ %def % The \lsmeans\ for the intercepts is the predictions at % \cc{Time=0}. The \lsmeans\ for the slopes becomes % <<>>= % K0 <- LE_matrix(chick, effect="Diet", at=list(Time=0)) % t(K1 - K0) % linest(chick, L=K1 - K0) % @ %def % We may be interested in finding the population means % at all levels of $A$ % but only at $C=1$. This is obtained by using the \code{at} argument % (in which case the average is only over the remaining factor $B$): % <<>>= % popMatrix(mm, effect='A', at=list(C='1')) % @ %def % Another way of % creating the population means % at all levels of $(A,C)$ is therefore % <<>>= % popMatrix(mm, effect='A', at=list(C=c('1','2'))) % @ %def % We may have several variables in the \code{at} argument: % @ % <<>>= % popMatrix(mm, effect='A', at=list(C=c('1','2'), B='1')) % @ %def % We can create our own function for comparing trends: % <<>>= % LSmeans_trend <- function(object, effect, trend){ % L <- LE_matrix(object, effect=effect, at=as.list(setNames(1, trend))) - % LE_matrix(object, effect=effect, at=as.list(setNames(0, trend))) % linest(object, L=L) % } % ## LSmeans_trend(chick, effect="Diet", trend="Time") % @ %def % <<>>= % LSmeans(fm.mix, effect=~Treatment, adjust.df=FALSE) % LSmeans(fm.mix, effect=~Treatment, adjust.df=TRUE) % @ %def % \subsection{Example: Non--estimable linear functions} % \label{sec:exampl-non-estim} % <<>>= % ## Make balanced dataset % dat.bal <- expand.grid(list(AA=factor(1:2), BB=factor(1:3), CC=factor(1:3))) % dat.bal$y <- rnorm(nrow(dat.bal)) % ## Make unbalanced dataset: 'BB' is nested within 'CC' so BB=1 % ## is only found when CC=1 and BB=2,3 are found in each CC=2,3,4 % dat.nst <- dat.bal % dat.nst$CC <-factor(c(1,1,2,2,2,2,1,1,3,3,3,3,1,1,4,4,4,4)) % @ %def % << >>= % dat.nst % @ % Consider this simulated dataset: % <<>>= % head(dat.nst, 4) % ftable(xtabs( ~ AA + BB + CC, data=dat.nst), row.vars="AA") % @ %def % Data is highly "unbalanced": % Whenever \verb|BB=1| then \verb|CC| is always \verb|1|; whenever % \verb|BB| is not \verb|1| then \verb|CC| is never \verb|1|. % We have % <<>>= % mod.nst <- lm(y ~ AA + BB : CC, data=dat.nst) % coef(mod.nst) % coef(summary(mod.nst)) % @ %def % In this case some of the \lsmeans\ values are not estimable; for example: % <<>>= % lsm.BC <- LSmeans(mod.nst, effect=c("BB", "CC")) % lsm.BC % lsm.BC2 <- LSmeans(mod.nst, effect="BB", at=list(CC=2)) % lsm.BC2 % @ %def % We describe the situation in % Section~\ref{sec:handl-non-estim} where we focus on \verb|lsm.BC2|. % \subsection{Handling non--estimability} % \label{sec:handl-non-estim} % The model matrix for the model in Section~\ref{sec:exampl-non-estim} % does not have full column rank and therefore not all values are % calculated by \cc{LSmeans()}. % <<>>= % X <- model.matrix( mod.nst ) % Matrix::rankMatrix(X) % dim(X) % as(X, "Matrix") % @ %def % We consider a model, i.e.\ % an $n$ dimensional random vector $y=(y_i)$ for which % $\EE(y)=\mu=X\beta$ and $\cov(y)=V$ where $X$ does not have % full column rank % We are % interested in linear functions of $\beta$, say % \begin{displaymath} % c=l\transp\beta= \sum_j l_j \beta_j . % \end{displaymath} % <<>>= % L <- LE_matrix(mod.nst, effect="BB", at=list(CC=2)) % t(L) % linest(mod.nst, L=L) % @ %def % A least squares estimate of $\beta$ is % \begin{displaymath} % \hat \beta = G X\transp y % \end{displaymath} % where $G$ is a generalized inverse of $X\transp X$. Since the % generalized inverse is not unique then neither is the estimate % $\hat\beta$. Hence $\hat c = l\transp\hat \beta$ is in general not % unique. % One least squares estimate of $\beta$ and one corresponding linear estimate $L\hat\beta$ is: % <<>>= % XtXinv <- MASS::ginv(t(X)%*%X) % bhat <- as.numeric(XtXinv %*% t(X) %*% dat.nst$y) % zapsmall(bhat) % L %*% bhat % @ %def % For some values of $l$ (i.e.\ for some rows of $L$) % the estimate $\hat c=l\transp \beta$ is % unique (i.e.\ it does not depend on the choice of generalized % inverse). % Such linear functions are said to be estimable and can be % described as follows: % All we specify with $\mu=X\beta$ % is that $\mu$ is a vector in the column space $C(X)$ of $X$. % We can only learn about $\beta$ through $X\beta$ so the only thing we can % say something about is linear combinations $\rho\transp X\beta$. Hence % we can only say something about $l\transp\beta$ if there exists % $\rho$ such that % $$ % l\transp\beta=\rho\transp X \beta, % $$ % i.e., if % $l=X\transp\rho$ for some $\rho$, which is if $l$ is in the column space % $C(X\transp)$ of $X\transp$. This is the same as saying that $l$ must be % perpendicular to % all vectors in the null space $N(X)$ of $X$. To check % this, we find a basis $B$ for $N(X)$. This can be done in many ways, % for example via a singular value decomposition of $X$, i.e.\ % \begin{displaymath} % X = U D V\transp % \end{displaymath} % A basis for $N(X)$ is given by those columns of $V$ that corresponds % to zeros on the diagonal of $D$. % <<>>= % S <- svd(X) % B <- S$v[, S$d < 1e-10, drop=FALSE ]; % head(B) ## Basis for N(X) % @ %def % From % << >>= % rowSums(L %*% B) % @ % we conclude that the first row of $L$ is not perpendicular to all vectors in thenull space $N(X)$ whereas the two last rows of $L$ are. Hence these two linear estimates are estimable; their value does not depend on the choice of generalized inverse: % << >>= % lsm.BC2 % @ % \subsection{Alternatives - \texttt{esticon()}} % \label{sec:alternatives} % An alternative is to do: % << >>= % c1 <- esticon(fm1.ubal, L2) % c1 % @ % Notice: \verb|esticon| has been in the \doby\ package for many years; % \verb|linest| is a newer addition; \verb|esticon| is not actively % maintained but remains in \doby\ for historical reasons. % \subsection{Using the \code{at} argument} % We may be interested in finding the population means % at all levels of $A$ % but only at $C=1$. This is obtained by using the \code{at} argument % (in which case the average is only over the remaining factor $B$): % <<>>= % popMatrix(mm, effect='A', at=list(C='1')) % @ %def % Another way of % creating the population means % at all levels of $(A,C)$ is therefore % <<>>= % popMatrix(mm, effect='A', at=list(C=c('1','2'))) % @ %def % We may have several variables in the \code{at} argument: % @ % <<>>= % popMatrix(mm, effect='A', at=list(C=c('1','2'), B='1')) % @ %def % \subsection{Ambiguous specification when using the \texttt{effect} and % \texttt{at} arguments} % There is room for an ambiguous specification if a variable appears in % both the \code{effect} and the \code{at} argument, such as % @ % <<>>= % popMatrix(mm, effect=c('A','C'), at=list(C='1')) % @ %def % This ambiguity is due to the fact that the \verb+effect+ argument asks % for the populations means at all levels of the variables but the % \verb+at+ chooses only specific levels. % This ambiguity is resolved as follows: Any variable in the \code{at} % argument is removed from the \code{effect} argument such as the % statement above is equivalent to % @ % <>= % ## popMatrix(mm, effect='A', at=list(C='1')) % @ %def % \subsection{Using covariates} % Next consider the model where a covariate is included: % @ % <<>>= % mm2 <- lm(y~A+B+C+C:x, data=dd) % coef(mm2) % @ %def % In this case we get % <<>>= % popMatrix(mm2,effect='A', at=list(C='1')) % @ %def % Above, $x$ has been replaced by its average and that is the general % rule for models including covariates. However we may use the \code{at} % argument to ask for calculation of the population mean at some % user-specified value of $x$, say 12: % <<>>= % popMatrix(mm2,effect='A', at=list(C='1',x=12)) % @ %def % <<>>= % mm22 <- lm(y~A+B+C+C:x+I(x^2), data=dd) % coef(mm22) % @ %def % <<>>= % popMatrix(mm22,effect='A', at=list(C='1')) % @ %def % <<>>= % dd <- transform(dd, x.sq=x^2) % mm23 <- lm(y~A+B+C+C:x+x.sq, data=dd) % coef(mm23) % popMatrix(mm23,effect='A', at=list(C='1')) % @ %def % \subsection{Using transformed covariates} % Next consider the model where a transformation of a covariate is included: % @ % <<>>= % mm3 <- lm(y~A+B+C+C:I(log(x)), data=dd) % coef(summary(mm3)) % @ %def % In this case we can not use \popmatrix\ (and hence % \popmeans) directly. Instead we first have to % generate a new variable, say \verb+log.x+, with % \verb+log.x+$=\log(x)$, in the data and then proceed as % <<>>= % dd <- transform(dd, log.x = log(x)) % mm32 <- lm(y~A+B+C+C:log.x, data=dd) % popMatrix(mm32, effect='A', at=list(C='1')) % @ %def % \subsection{The \code{engine} argument of \popmeans} % The \popmatrix is a function to generate a linear tranformation matrix of the model % parameters with emphasis on constructing such matrices for population % means. % \popmeans\ invokes by default the \esticon\ function on this % linear transformation matrix for calculating parameter estimates and % confidecne intervals. % A similar function to \esticon\ is the \verb+glht+ function of the \verb+multcomp+ % package. % The \code{glht()} function % can be chosen via the \verb+engine+ argument of \popmeans: % <<>>= % library(multcomp) % g<-popMeans(mm,effect='A', at=list(C='1'),engine="glht") % g % @ %def % This allows to apply the methods available on the \verb+glht+ object like % <<>>= % summary(g,test=univariate()) % confint(g,calpha=univariate_calpha()) % @ % which yield the same results as the \esticon\ function. % By default the functions will adjust the tests and confidence intervals for multiplicity % <<>>= % summary(g) % confint(g) % @ % \subsection{Ambiguous specification when using the \texttt{effect} and % \texttt{at} arguments} % There is room for an ambiguous specification if a variable appears in % both the \code{effect} and the \code{at} argument, such as % <<>>= % LE_matrix(fm1.ubal, effect=c('Treatment','Type'), at=list(Type='Mississippi')) % LE_matrix(fm1.ubal, effect=~Treatment + Type, at=list(Type='Mississippi')) % @ %def % This ambiguity is due to the fact that the \verb+effect+ argument asks % for the populations means at all levels of the variables but the % \verb+at+ chooses only specific levels. % This ambiguity is resolved as follows: Any variable in the \code{at} % argument is removed from the \code{effect} argument such as the % statement above is equivalent to\footnote{FIXME} % <>= % LE_matrix(fm1.ubal, effect=c('Treatement'), at=list(Type='Mississippi')) % LE_matrix(fm1.ubal, effect=~Treatement, at=list(Type='Mississippi')) % @ %def %%\bibliography{doBy} \end{document} % \section{LS--means for linear models} % \label{sec:linear-model} % \subsection{LS--means -- a first example} % \label{sec:ls-means-population} % <>= % simdat<-structure(list(treat = structure(c(1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L % ), .Label = c("t1", "t2"), class = "factor"), year = structure(c(1L, % 1L, 1L, 1L, 2L, 2L, 2L, 2L), .Label = c("1", "2"), class = "factor"), % y = c(0.5, 1, 1.5, 3, 3, 4.5, 5, 5.5)), .Names = c("treat", "year", % "y"), row.names = c(NA, -8L), class = "data.frame") % @ %def % Consider these simulated data % <<>>= % simdat % @ %def % shown in the figure below. % <>= % library(ggplot2) % qplot(treat, y, data=simdat, color=year, size=I(3)) % @ %def % \includegraphics[height=6cm,width=12cm]{figures/LSmeans-simdat-fig} % The LS--means under an additive model for the factor \cc{treat} is % <<>>= % msim <- lm(y ~ treat + year, data=simdat) % LSmeans( msim, effect="treat") % @ %def % whereas the population means are % <<>>= % summaryBy(y~treat, data=simdat, FUN=mean) % @ %def % Had data been balanced (same number of observations for each % combination of \cc{treat} and \cc{year}) the results would have been the % same. An argument in favor of the LS--means is that these figures % better represent what one would expect on in an ``average year''. % \subsection{Example: Warpbreaks} % \label{sec:example:-warpbreaks} % <<>>= % summary( warpbreaks ) % head( warpbreaks, 4 ) % ftable(xtabs( ~ wool + tension, data=warpbreaks)) % @ %def % <>= % opar <- par(mfrow = c(1, 2), oma = c(0, 0, 1.1, 0)) % plot(breaks ~ tension, data = warpbreaks, col = "lightgray", % varwidth = TRUE, subset = wool == "A", main = "Wool A") % plot(breaks ~ tension, data = warpbreaks, col = "lightgray", % varwidth = TRUE, subset = wool == "B", main = "Wool B") % mtext("warpbreaks data", side = 3, outer = TRUE) % par(opar) % @ %def % <<>>= % (warp.lm <- lm(breaks ~ wool + tension, data=warpbreaks)) % @ %def % The fitted values are: % <<>>= % uni <- unique(warpbreaks[,2:3]) % prd <- cbind(breaks=predict(warp.lm, newdata=uni), uni); prd % @ %def % \subsection{The LS--means} % \label{sec:lsmeans} % We may be interested in making predictions of the number of breaks for % each level of \cc{tension} for \emph{any} type or an \emph{average} % type of \code{wool}. The idea behind LS--means is % to average the predictions above over the two % wool types. These quantities are the \lsmeans\ for the effect % \cc{tension}. % This is done with: % <<>>= % LSmeans(warp.lm, effect="tension") % @ %def % The term \lsmeans\ comes from that these quantities are the same as % the least squares main effects of \cc{tension} when data is balanced: % <<>>= % doBy::summaryBy(breaks ~ tension, data=warpbreaks, FUN=mean) % @ %def % When data is not balanced these quantities are in general not the same. % \subsection{LS--means for models with interactions} % \label{sec:models-with-inter} % Consider a model with interaction: % <<>>= % warp.lm2 <- update(warp.lm, .~.+wool:tension) % coef( summary( warp.lm2 )) % @ %def % In this case the contrast matrix becomes: % <<>>= % K2 <- LE_matrix(warp.lm2, effect="tension"); K2 % linest(warp.lm2, K=K2) % @ %def % For the sake of illustration we treat \cc{wool} as a random effect: % <<>>= % library(lme4) % warp.mm <- lmer(breaks ~ tension + (1|wool), data=warpbreaks) % LSmeans(warp.mm, effect="tension") % @ %def % Notice here that the estimates themselves are very similar to those % above but the standard errors are much larger. This comes from that % there that \code{wool} is treated as a random effect. % <<>>= % VarCorr(warp.mm) % @ %def %\usepackage{framed} %\usepackage{comment} %%\definecolor{shadecolor}{gray}{0.91} %%\definecolor{darkred}{rgb}{.7,0,0} %%\definecolor{midnightblue}{rgb}{0.098,0.098,0.439} %% \DefineVerbatimEnvironment{Sinput}{Verbatim}{ %% fontfamily=tt, %% %%fontseries=b, %% %% xleftmargin=2em, %% formatcom={\color{midnightblue}} %% } %% \DefineVerbatimEnvironment{Soutput}{Verbatim}{ %% fontfamily=tt, %% %%fontseries=b, %% %% xleftmargin=2em, %% formatcom={\color{blue}} %% } %% \DefineVerbatimEnvironment{Scode}{Verbatim}{ %% fontfamily=tt, %% %%fontseries=b, %% %% xleftmargin=2em, %% formatcom={\color{blue}} %% } %% %%\fvset{listparameters={\setlength{\topsep}{-2pt}}} %\renewenvironment{Schunk}{\linespread{.90}}{} %%\renewenvironment{Schunk}{\begin{shaded}\small}{\end{shaded}} %% A special type of linear estimates is the so called least--squares %% means (or LS--means). Other names for these estimates include %% population means and marginal means. Notice that the \pkg{lsmeans} package %% \cite{Lenth:2013} also provides computations of LS--means, see %% \url{http://cran.r-project.org/web/packages/lsmeans/}. %% <>= %% simdat<-structure(list(treat = structure(c(1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L %% ), .Label = c("t1", "t2"), class = "factor"), year = structure(c(1L, %% 1L, 1L, 1L, 2L, 2L, 2L, 2L), .Label = c("1", "2"), class = "factor"), %% y = c(0.5, 1, 1.5, 3, 3, 4.5, 5, 5.5)), .Names = c("treat", "year", %% "y"), row.names = c(NA, -8L), class = "data.frame") %% @ %def %% \subsection{LS--means on a simple example} %% \label{sec:ls-means-simple} %% Consider these simulated data, also shown in Fig.~\ref{fig:simdat2-fig}: %% <<>>= %% simdat %% @ %def %% <>= %% library(ggplot2) %% qplot(treat, y, data=simdat, color=year) %% @ %def %% The LS--means under an additive model for the factor \cc{treat} is the predicted outcome for each level of \cc{treat} for an ``average year'': %% <<>>= %% msim <- lm(y ~ treat + year, data=simdat) %% lsm <- LSmeans(msim, effect="treat") %% lsm %% @ %def %% Notice that the population means are %% <<>>= %% summaryBy(y ~ treat, data=simdat, FUN=function(x) c(m=mean(x), s=sd(x))) %% ## or aggregate(y ~ treat, data=simdat, FUN=function(x) c(m=mean(x), s=sd(x))) %% @ %def %% Had data been balanced (same number of observations for each %% combination of \cc{treat} and \cc{year}) the results would have been the %% same. An argument in favor of the LS--means is that these figures %% better represent what one would expect on in an ``average year''. %% An alternative is to think of \cc{year} as a random %% effect, for example: %% <>= %% library(lme4) %% lmer( y ~ treat + (1|year), data=simdat) %% @ %def %% In this case one would directly obtain $\EE(Y|\cc{treat})$ from the %% model. However, there are at least two reasons why one may be hesitant %% to consider such a random effects model. %% \begin{itemize} %% \item It is a ``never ending'' discussion whether \verb|year| should %% be treated as fixed or random. For \verb|year| to be a random %% effect, it should in principle come from a large population of %% possible years. This is certainly a dubious assumption for the %% years. %% \item If we accept to think of year as a random effect, then the %% variance describing this random effect will be poorly determined %% (because there are only two years in the study). %% \end{itemize} % <<>>= % warp.poi <- glm(breaks ~ wool + tension, family=poisson, data=warpbreaks) % LSmeans(warp.poi, effect="tension", type="link") % LSmeans(warp.poi, effect="tension", type="response") % @ %def %% SANITY CHECK %% @ %% <<>>= %% K <- LE_matrix(warp.poi, effect="tension") %% doBy::esticon(warp.poi, K) %% @ %def % <<>>= % warp.qpoi <- glm(breaks ~ wool + tension, family=quasipoisson, data=warpbreaks) % LSmeans(warp.qpoi, effect="tension", type="link") % LSmeans(warp.qpoi, effect="tension", type="response") % @ %def % For comparison with the linear model, we use identity link % <>= % warp.poi2 <- glm(breaks ~ wool + tension, family=poisson(link=identity), % data=warpbreaks) % LSmeans(warp.poi2, effect="tension", type="link") % @ %def %% SANITY CHECK %% @ %% <<>>= %% K <- LE_matrix(warp.poi2, effect="tension") %% doBy::esticon(warp.poi2, K) %% @ %def % <<>>= % warp.gam <- glm(breaks ~ wool + tension, family=Gamma(link=identity), % data=warpbreaks) % LSmeans(warp.gam, effect="tension", type="link") % @ %def %% SANITY CHECK %% @ %% <<>>= %% K <- LE_matrix(warp.gam, effect="tension") %% doBy::esticon(warp.gam, K) %% @ %def % Notice that the linear estimates are practically the same as for the % linear model, but the standard errors are smaller and hence the % confidence intervals are narrower. % An alternative is to fit a quasi Poisson ``model'' % <<>>= % warp.poi3 <- glm(breaks ~ wool + tension, family=quasipoisson(link=identity), % data=warpbreaks) % LSmeans(warp.poi3, effect="tension") % @ %def % \subsubsection{For balanced data} % \label{sec:balanced-case} % When the dataset is balanced the results will be the same % <<>>= % aggregate(pred ~ Treatment, data=bb1, FUN=mean) % fm2.bal <- update(fm1.bal, .~. - Type) % grid2 <- expand.grid( % Treatment = c("nonchilled", "chilled"), % conc = 100 % ) % bb2 <- cbind(pred=predict(fm2.bal, newdata=grid2), grid2) % bb2 % @ % \subsubsection{For unbalanced data} % \label{sec:unbalanced-case} % In general, however, the results will not be identical % <<>>= % L <- matrix(c(1, 100, 0, 0, % 1, 100, 0, 1, % 1, 100, 1, 0, % 1, 100, 1, 1), ncol=4, byrow=T) % @ % \subsection{Tooth growth} % \label{sec:tooth-growth} % % The response is the length of odontoblasts cells (cells responsible for % % tooth growth) in 60 guinea pigs. Each animal received one of % % three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of % % two delivery methods, (orange juice (coded as OJ) or ascorbic acid % % (a form of vitamin C and (coded as VC)). % % <<>>= % % head(ToothGrowth, 4) % % ftable(xtabs(~ dose + supp, data=ToothGrowth)) % % @ %def % % <>= % % opar <- par(mfrow = c(1, 2), oma = c(0, 0, 1.1, 0)) % % plot(len ~ dose, data = ToothGrowth, col = "lightgray", % % subset = supp == "OJ", main = "supp=OJ") % % plot(len ~ dose, data = ToothGrowth, col = "lightgray", % % subset = supp == "VC", main = "supp=VC") % % mtext("ToothGrowth data", side = 3, outer = TRUE) % % par(opar) % % @ %def % % <<>>= % % ToothGrowth |> interaction_plot(len ~ dose + supp) % % @ % % The interaction plot suggests a mild interaction which is supported by a formal comparison: % << >>= % ToothGrowth$dose <- factor(ToothGrowth$dose) % head(ToothGrowth) % tooth1 <- lm(len ~ dose + supp, data=ToothGrowth) % tooth2 <- lm(len ~ dose * supp, data=ToothGrowth) % anova(tooth1, tooth2) % @ %% <>= %% tooth_avglen <- ToothGrowth |> %% group_by(dose, supp) |> %% summarise(val = mean(len)) %% tooth_avglen %% ggplot(ToothGrowth, aes(x = factor(dose), y = len, colour = supp)) + %% geom_boxplot(outlier.shape = 4) + %% geom_point(data = tooth_avglen, aes(y = val)) + %% geom_line(data = tooth_avglen, aes(y = val, group = supp)) %%@ % \section{Computing linear estimates} % \label{sec:comp-line-estim} % For now, we focus on the additive model: % << >>= % tooth1 % @ % Consider computing the estimated length for each dose of orange juice (OJ): % One option: Construct the LE--matrix $L$ directly: % << >>= % L <- matrix(c(1, 0, 0, 0, % 1, 1, 0, 0, % 1, 0, 1, 0), nrow=3, byrow=T) % @ % Then do: % << >>= % c1 <- linest(tooth1, L) % c1 % @ % We can do: % << >>= % summary(c1) % coef(c1) % confint(c1) % @ % \subsection{Automatic generation of $L$} % \label{sec:autom-gener-l} % The matrix $L$ can be generated as follows: % << >>= % L <- LE_matrix(tooth1, effect="dose", at=list(supp="OJ")) % L % @ % \subsection{OLD: Least-squares means (LS--means)} % \label{sec:least-squares-means} % A related question could be: What is the estimated length for each % dose if we ignore the source of vitamin C (i.e.\ whether it is OJ or % VC). One approach would be to fit a model in which source does not appear: % % << >>= % % tooth0 <- update(tooth1, . ~ . - supp) % % L0 <- LE_matrix(tooth0, effect="dose") % % L0 % % linest(tooth0, L=L0) % % @ % An alternative would be to stick to the original model but compute the % estimate for an ``average vitamin C source''. That would correspond to % giving weight $1/2$ to each of the two vitamin C source % parameters. However, as one of the parameters is already set to zero % to obtain identifiability, we obtain the LE--matrix $L$ as % % << >>= % % L1 <- matrix(c(1, 0, 0, 0.5, % % 1, 1, 0, 0.5, % % 1, 0, 1, 0.5), nrow=3, byrow=T) % % linest(tooth1, L=L1) % % @ % Such a particular linear estimate is sometimes called a least-squares % mean or an LSmean or a marginal mean. Notice that the parameter % estimates under the two approaches are identical. This is is because % data is balanced: There are $10$ observations per supplementation % type. Had data not been balanced, the estimates would in general have been different. % % Notice: One may generate $L$ automatically with % % << >>= % % L1 <- LE_matrix(tooth1, effect="dose") % % L1 % % @ % Notice: One may obtain the LSmean directly as: % % << >>= % % LSmeans(tooth1, effect="dose") % % @ %% \subsection{Additive model} %% \label{sec:additive-model} %% Returning to the \verb|ToothGrowth| data, orange juice and ascorbic %% acid are just two of many ways of supplying vitamin C (citrus and lime %% juice would be two alternatives). Here one can therefore argue, that %% it would make sense to estimate the the effect for each dose for an %% ``average vitamin C source'': %% << >>= %% LSmeans(tooth1, effect="dose") %% @ % which is the same as % <>= % L <- LE_matrix(tooth1, effect="dose") % le <- linest(tooth1, L=L) % coef(le) % @ % %% \subsection{Interaction model} % %% \label{sec:interaction-model} % For a model with interactions, the LSmeans are % << >>= % LSmeans(tooth2, effect="dose") % @ % In this case, the LE--matrix is % << >>= % L <- LE_matrix(tooth2, effect="dose") % t(L) % @ %% \usepackage[inline,nomargin,draft]{fixme} %% \newcommand\FXInline[2]{\textbf{\color{blue}#1}: %% \emph{\color{blue} - #2}} %% \usepackage[cm]{fullpage} doBy/vignettes/section_fun.Rmd0000644000176200001440000001111114613706030016132 0ustar liggesusers--- title: "Sectioning a function" author: "Søren Højsgaard" output: html_document: toc: true number_sections: true vignette: > %\VignetteIndexEntry{section_fun: Section functions to a smaller domain} %\VignettePackage{doBy} %\VignetteEngine{knitr::knitr} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) options("digits"=3) library(doBy) library(boot) #devtools::load_all() ``` ## Section a functions domain: with `section_fun()` The function `section_fun` is used to create a new function that is a section of the original function. The section is defined by fixing some of the arguments of the original function. The new function is defined on the remaining arguments. For example, let $f(x,y)=x+y$. Then $f_x(y)=f(10, y)$ is a section of $f$ to be a function of $y$ alone. More generally, let $E$ be a subset of the cartesian product $X \times Y$ where $X$ and $Y$ are some sets. Consider a function $f(x,y)$ defined on $E$. Then for any $x \in X$, the section of $E$ defined by $x$ (denoted $E_x$) is the set of $y$'s in $Y$ such that $(x, y)$ is in $E$, i.e. $$ E_x = \{ y \in Y | (x,y) \in E\} $$ Correspondingly, the section of $f(x,y)$ defined by $x$ is the function $f_x$ defined on $E_x$ given by $f_x(y)=f(x,y)$. ## The `section_fun` function in `doBy` The function `section_fun` is used to create a new function that is a section of the original function. The section is defined by fixing some of the arguments of the original function. The new function is defined on the remaining arguments. There are the following approaches: 1) insert the section values as default values in the function definition (default), 2) insert the section values in the function body, 3) store the section values in an auxillary environment. Consider this function: ```{r} fun <- function(a, b, c=4, d=9){ a + b + c + d } ``` ```{r} fun_def <- section_fun(fun, list(b=7, d=10)) fun_def fun_body <- section_fun(fun, list(b=7, d=10), method="sub") fun_body fun_env <- section_fun(fun, list(b=7, d=10), method = "env") fun_env ``` In the last case, we can see the section and the original function definition as: ```{r} get_section(fun_env) ## same as: attr(fun_env, "arg_env")$args get_fun(fun_env) ## same as: environment(fun_env)$fun ``` We get: ```{r} fun(a=10, b=7, c=5, d=10) fun_def(a=10, c=5) fun_body(a=10, c=5) fun_env(a=10, c=5) ``` ## Example: benchmarking Consider a simple task: Creating and inverting Toeplitz matrices for increasing dimensions: ```{r} n <- 4 toeplitz(1:n) ``` An implementation is ```{r} inv_toep <- function(n) { solve(toeplitz(1:n)) } inv_toep(4) ``` We can benchmark timing for different values of $n$ as ```{r} library(microbenchmark) microbenchmark( inv_toep(4), inv_toep(8), inv_toep(16), inv_toep(32), inv_toep(64), times=5 ) ``` However, it is tedious (and hence error prone) to write these function calls. A programmatic approach using \code{section\_fun} is as follows: First create a list of sectioned functions: ```{r} n.vec <- c(3, 4, 5) fun_list <- lapply(n.vec, function(ni){ section_fun(inv_toep, list(n=ni))} ) ``` We can inspect and evaluate each / all functions as: ```{r} fun_list[[1]] fun_list[[1]]() ``` To use the list of functions in connection with microbenchmark we bquote all functions using ```{r} bquote_list <- function(fnlist){ lapply(fnlist, function(g) { bquote(.(g)()) } ) } ``` We get: ```{r} bq_fun_list <- bquote_list(fun_list) bq_fun_list[[1]] ## Evaluate one: eval(bq_fun_list[[1]]) ## Evaluate all: ## lapply(bq_fun_list, eval) ``` To use microbenchmark we must name the elements of the list: ```{r} names(bq_fun_list) <- n.vec microbenchmark( list = bq_fun_list, times = 5 ) ``` Running the code below provides a benchmark of the different ways of sectioning in terms of speed. ```{r} n.vec <- seq(20, 80, by=20) fun_def <- lapply(n.vec, function(ni){ section_fun(inv_toep, list(n=ni), method="def")} ) fun_body <- lapply(n.vec, function(ni){ section_fun(inv_toep, list(n=ni), method="sub")} ) fun_env <- lapply(n.vec, function(ni){ section_fun(inv_toep, list(n=ni), method="env")} ) bq_fun_list <- bquote_list(c(fun_def, fun_body, fun_env)) names(bq_fun_list) <- paste0(rep(c("def", "body", "env"), each=length(n.vec)), rep(n.vec, 3)) mb <- microbenchmark( list = bq_fun_list, times = 2 ) ``` doBy/vignettes/doby.Rmd0000644000176200001440000005054415030067407014572 0ustar liggesusers--- title: "doby: Groupwise computations and miscellaneous utilities" author: "Søren Højsgaard" output: html_document: toc: true number_sections: true vignette: > %\VignetteIndexEntry{doby: Groupwise computations and miscellaneous utilities} %\VignettePackage{doBy} %\VignetteEngine{knitr::knitr} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) options("digits"=3) library(doBy) library(boot) #devtools::load_all() ``` The \doby{} package contains a variety of utility functions. This working document describes some of these functions. The package originally grew out of a need to calculate groupwise summary statistics (much in the spirit of \code{PROC SUMMARY} of the \proglang{SAS} system), but today the package contains many different utilities. ```{r} library(doBy) ``` \section{Data used for illustration} \label{sec:co2data} The description of the \code{doBy} package is based on the \code{mtcars} dataset. ```{r} head(mtcars) tail(mtcars) ``` # Groupwise computations ## summaryBy() and summary_by() \label{sec:summaryBy} The \summaryby{} function is used for calculating quantities like ``the mean and variance of numerical variables $x$ and $y$ for each combination of two factors $A$ and $B$''. Notice: A functionality similar to \summaryby\ is provided by \code{aggregate()} from base \R. ```{r} myfun1 <- function(x){ c(m=mean(x), s=sd(x)) } summaryBy(cbind(mpg, cyl, lh=log(hp)) ~ vs, data=mtcars, FUN=myfun1) ``` A simpler call is ```{r} summaryBy(mpg ~ vs, data=mtcars, FUN=mean) ``` Instead of formula we may specify a list containing the left hand side and the right hand side of a formula\footnote{This is a feature of \summaryby\ and it does not work with \code{aggregate}.} but that is possible only for variables already in the dataframe: ```{r} summaryBy(list(c("mpg", "cyl"), "vs"), data=mtcars, FUN=myfun1) ``` Inspired by the \pkg{dplyr} package, there is a \verb|summary_by| function which does the samme as \summaryby{} but with the data argument being the first so that one may write ```{r} mtcars |> summary_by(cbind(mpg, cyl, lh=log(hp)) ~ vs, FUN=myfun1) ``` ## orderBy() and order_by() Ordering (or sorting) a data frame is possible with the \code{orderBy} function. For example, we order the rows according to \code{gear} and \code{carb} (within \code{gear}): ```{r} x1 <- orderBy(~ gear + carb, data=mtcars) head(x1, 4) tail(x1, 4) ``` If we want the ordering to be by decreasing values of one of the variables, we can do ```{r} x2 <- orderBy(~ -gear + carb, data=mtcars) ``` Alternative forms are: ```{r} x3 <- orderBy(c("gear", "carb"), data=mtcars) x4 <- orderBy(c("-gear", "carb"), data=mtcars) x5 <- mtcars |> order_by(c("gear", "carb")) x6 <- mtcars |> order_by(~ -gear + carb) ``` ## splitBy() and split_by() Suppose we want to split the \code{airquality} data into a list of dataframes, e.g.\ one dataframe for each month. This can be achieved by: ```{r} x <- splitBy(~ Month, data=airquality) x <- splitBy(~ vs, data=mtcars) lapply(x, head, 4) attributes(x) ``` Alternative forms are: ```{r} splitBy("vs", data=mtcars) mtcars |> split_by(~ vs) ``` ## subsetBy() and subset_by() Suppose we want to select those rows within each month for which the the wind speed is larger than the mean wind speed (within the month). This is achieved by: ```{r} x <- subsetBy(~am, subset=mpg > mean(mpg), data=mtcars) head(x) ``` Note that the statement \code{Wind > mean(Wind)} is evaluated within each month. Alternative forms are ```{r} x <- subsetBy("am", subset=mpg > mean(mpg), data=mtcars) x <- mtcars |> subset_by("vs", subset=mpg > mean(mpg)) x <- mtcars |> subset_by(~vs, subset=mpg > mean(mpg)) ``` ## transformBy() and transform_by() The \code{transformBy} function is analogous to the \code{transform} function except that it works within groups. For example: ```{r} head(x) x <- transformBy(~vs, data=mtcars, min.mpg=min(mpg), max.mpg=max(mpg)) head(x) ``` Alternative forms: ```{r} x <- transformBy("vs", data=mtcars, min.mpg=min(mpg), max.mpg=max(mpg)) x <- mtcars |> transform_by("vs", min.mpg=min(mpg), max.mpg=max(mpg)) ``` ## lapplyBy() and lapply_by() This \code{lapplyBy} function is a wrapper for first splitting data into a list according to the formula (using splitBy) and then applying a function to each element of the list (using lapply). ```{r} lapplyBy(~vs, data=mtcars, FUN=function(d) lm(mpg~cyl, data=d) |> summary() |> coef()) ``` # Miscellaneous utilities ## firstobs() and lastobs() To obtain the indices of the first/last occurences of an item in a vector do: ```{r} x <- c(1, 1, 1, 2, 2, 2, 1, 1, 1, 3) firstobs(x) lastobs(x) ``` The same can be done on variables in a data frame, e.g. ```{r} firstobs(~vs, data=mtcars) lastobs(~vs, data=mtcars) ``` \subsection{The \code{which.maxn()} and \code{which.minn()} functions} \label{sec:whichmaxn} The location of the $n$ largest / smallest entries in a numeric vector can be obtained with ```{r} x <- c(1:4, 0:5, 11, NA, NA) which.maxn(x, 3) which.minn(x, 5) ``` ## Subsequences - subSeq() Find (sub) sequences in a vector: ```{r} x <- c(1, 1, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 1) subSeq(x) subSeq(x, item=1) subSeq(letters[x]) subSeq(letters[x], item="a") ``` ## Recoding values of a vector - recodeVar() ```{r} x <- c("dec", "jan", "feb", "mar", "apr", "may") src1 <- list(c("dec", "jan", "feb"), c("mar", "apr", "may")) tgt1 <- list("winter", "spring") recodeVar(x, src=src1, tgt=tgt1) ``` ## Renaming columns of a dataframe or matrix - renameCol() ```{r} head(renameCol(mtcars, c("vs", "mpg"), c("vs_", "mpg_"))) ``` ## Time since an event - timeSinceEvent() Consider the vector ```{r} yvar <- c(0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0) ``` Imagine that "1" indicates an event of some kind which takes place at a certain time point. By default time points are assumed equidistant but for illustration we define time time variable ```{r} tvar <- seq_along(yvar) + c(0.1, 0.2) ``` Now we find time since event as ```{r} tse <- timeSinceEvent(yvar, tvar) tse ``` The output reads as follows: \begin{itemize} \item \verb"abs.tse": Absolute time since (nearest) event. \item \verb"sign.tse": Signed time since (nearest) event. \item \verb"ewin": Event window: Gives a symmetric window around each event. \item \verb"run": The value of \verb"run" is set to $1$ when the first event occurs and is increased by $1$ at each subsequent event. \item \verb"tae": Time after event. \item \verb"tbe": Time before event. \end{itemize} ```{r} plot(sign.tse ~ tvar, data=tse, type="b") grid() rug(tse$tvar[tse$yvar == 1], col="blue",lwd=4) points(scale(tse$run), col=tse$run, lwd=2) lines(abs.tse + .2 ~ tvar, data=tse, type="b",col=3) ``` ```{r} plot(tae ~ tvar, data=tse, ylim=c(-6,6), type="b") grid() lines(tbe ~ tvar, data=tse, type="b", col="red") rug(tse$tvar[tse$yvar==1], col="blue", lwd=4) lines(run ~ tvar, data=tse, col="cyan", lwd=2) ``` ```{r} plot(ewin ~ tvar, data=tse, ylim=c(1, 4)) rug(tse$tvar[tse$yvar==1], col="blue", lwd=4) grid() lines(run ~ tvar, data=tse, col="red") ``` We may now f ind times for which time since an event is at most 1 as ```{r} tse$tvar[tse$abs <= 1] ``` ## Example: Using subSeq() and timeSinceEvent() Consider the \verb|lynx| data: ```{r} lynx <- as.numeric(lynx) tvar <- 1821:1934 plot(tvar, lynx, type="l") ``` Suppose we want to estimate the cycle lengths. One way of doing this is as follows: ```{r} yyy <- lynx > mean(lynx) head(yyy) sss <- subSeq(yyy, TRUE) sss ``` ```{r} plot(tvar, lynx, type="l") rug(tvar[sss$midpoint], col="blue", lwd=4) ``` Create the "event vector" ```{r} yvar <- rep(0, length(lynx)) yvar[sss$midpoint] <- 1 str(yvar) ``` ```{r} tse <- timeSinceEvent(yvar,tvar) head(tse, 20) ``` We get two different (not that different) estimates of period lengths: ```{r} len1 <- tapply(tse$ewin, tse$ewin, length) len2 <- tapply(tse$run, tse$run, length) c(median(len1), median(len2), mean(len1), mean(len2)) ``` We can overlay the cycles as: ```{r} tse$lynx <- lynx tse2 <- na.omit(tse) plot(lynx ~ tae, data=tse2) ``` ```{r} plot(tvar, lynx, type="l", lty=2) mm <- lm(lynx ~ tae + I(tae^2) + I(tae^3), data=tse2) lines(fitted(mm) ~ tvar, data=tse2, col="red") ``` \section{Acknowledgements} \label{discussion} Credit is due to Dennis Chabot, Gabor Grothendieck, Paul Murrell and Jim Robison-Cox for reporting various bugs and making various suggestions to the functionality in the \doby{} package. doBy/vignettes/vtools.Rmd0000644000176200001440000000305615024600522015151 0ustar liggesusers--- title: "Working with vtools: Flexible Variable Selection" author: "Søren Højsgaard" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Working with vtools: Flexible Variable Selection} %\VignetteEngine{knitr::knitr} %\VignetteEncoding{UTF-8} --- ```r library(doBy) library(rlang) data(CO2) ``` ## Overview The `v*` functions in the `doBy` package allow for flexible and consistent handling of variable input. These tools support unquoted names, character vectors, and formulas. The main functions are: - `vparse()` – extract variable names from flexible input - `v()` – shorthand for `vparse()` - `vselect()` – select columns from a data frame using flexible input - `vcheck()` – verify that variables exist in a data frame - `vmap()` – apply a function to each parsed variable name - `vrename()` – rename variables in a data frame --- ## Examples ### `vparse()` ```r vparse(Treatment, Type) vparse(c("Treatment", "Type")) vparse(~Treatment + Type) ``` ### `v()` – shorthand for `vparse()` ```r v(Treatment, Type) v(~Treatment + Type) v(c("Treatment", "Type")) ``` ### `vselect()` ```r vselect(CO2, Treatment, Type) vselect(CO2, ~Treatment + Type) ``` ### `vcheck()` ```r vcheck(CO2, Treatment) vcheck(CO2, ~Treatment + Type) ``` ### `vmap()` ```r vmap(~Treatment + Type, toupper) ``` ### `vrename()` ```r vrename(CO2, c(Treatment = "Trt", Type = "Group")) ``` ## Summary The `v*` tools simplify working with variable names and selection in data frames, making it easier to write functions that accept flexible input formats. doBy/data/0000755000176200001440000000000015030320366012056 5ustar liggesusersdoBy/data/cad1.RData0000755000176200001440000000170414551451413013615 0ustar liggesusers[N@TE@j "BjǭC[?@po24efve(:Կ{x^@^ޞ[NBz||ƁdWt#}9"lnC%ccCYN7bj [1M9b[gr}MjX=a3:eϗmWιF٘YG=@&= / $W2ʼe;wm!W'^|"Y+0tDDeXL;W 9Gs0: e,uem)o Ibk%J)@{Z˱\}UY56=l>.\աۤglQA"ɎY(FFxCpTؔM: @[u&&uŢs w54gCS&tIrF%ѫ -b#I Ȇ/wW?0v4X\ ky ~H$*[lㆱJm}ƿ#/stƖl%6Ħ.$d@(5/#o C[w-hjh 'H vV٣kEH]BP]Zm4Fu#ޮs86QH?v}j;^Rz]jjcQLgK!4b B18?_{j&כ!.K|6ˣ4;)Vޜ>ܲ4KIuI2%XqOXe1879 hiFiF/M~qD]2,k'n)2r9doBy/data/potatoes.RData0000755000176200001440000000037714551451413014650 0ustar liggesusers]n0EHn ?bkAxH)K|2\۳^/ܵKD)U Ga[tHUodHyY >2x3G@M4.c0.D{F^w냖xVoE9=J48fapڛmH[$.P'ݭ6ŸA^Է/3,.W<doBy/data/personality.RData0000644000176200001440000001174514551451413015361 0ustar liggesusersMr# 9_@6aGh h93|d2 q=*3++3jp~篾ַ~:6_Z]5B~˽عӵsG=cs;?v/vdݾ+s|{#s'_~9^Mc4z3v2Iz7bn0`,'8nh{OcԮmdG̃ 2{Z_#>y<|7Əc{]q5uUMtg ˋqm{ַ=fg?sErؼz_~A9;}w۟3BLFm2O,ڗN֣'N#;zxǶ+}봉:ƶ^_y}Ydak(1-de e{jk[g޺_)bڿz#aI\$Ǝc"qExmSGCW>bn']`)J_tONDBU|3("?mcqR`Ls/ƻYs__7ikz+,21x9E uCCڷĠ޿4/i9[OB%-r#G>^CYKuk>qڜCj5 #f ;}cn.h1c\֔8eo#TcmŝYKws.?:ŘkkL4})f\s Ou <2+k>g[׎͋]E/nqwsg{fe}tTדEc\s]ƌ[ruc.Vs Sc+{/ٌ? 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Fixed now. * Version 4.5-11 uploaded. 2014-09-08 Søren Højsgaard =============================================== * Added the 'haldCement' data * lapplyBy: Argument keep.groupid added * nullBasis function/method added * popMeans function re-introduced (just a different name for LSmeans) * dose.LD50 removed; car::deltaMethod can probably do the same. * Version 4.5-11 uploaded. 2013-11-28 Søren Højsgaard =============================================== * LSmeans, LSmatrix and linest functions added. (There are not yet methods for `nlme` and `survival` objects but they will be added). * popMeans and popMatrix functions removed * Vignette on LSmeans added. * Version 4.5-10 uploaded 2013-07-20 Søren Højsgaard =============================================== * splitBy and summaryBy have been reworked. * formulaFunBy and xyFunBy functions added. * Vignettes expanded. * summaryBy, splitBy now also accept a list / vector as "formula" specification. * recodeVar works properly on formulas * Version 4.5-7 uploaded 2013-03-19 Søren Højsgaard =============================================== * parseGroupFormula function added. The function will take a "group formula" with a vertical bar apart into components. * createFunBy function added. The function can create groupwise functions. * documentation of lmBy improved * various utility functions related to lmBy added. * Version 4.5-6 uploaded 2012-11-16 Søren Højsgaard =============================================== * Rmarkup removed; the markdown package does a better job. * Version 4.5-5 uploaded 2012-05-28 Søren Højsgaard =============================================== * Updates of Rmarkup * Version 4.5-4 uploaded 2012-05-19 Søren Højsgaard =============================================== * Updates of Rmarkup * Version 4.5-3 uploaded 2012-02-17 Søren Højsgaard =============================================== * descStat function added. Thanks to Gregor Gorjanc. * popMeans extended so that the grid values appear in the output by default * Version 4.5.2 uploaded 2012-01-29 Søren Højsgaard =============================================== * Minor enhancement to Rmarkup() * Version 4.5.1 uploaded 2012-01-18 Søren Højsgaard =============================================== * popMeans method for lme objects added * lsmeans aliases for popMeans removed * Version 4.5.0 uploaded 2012-01-18 Søren Højsgaard =============================================== * Small changes to package structure * Version 4.4.6 uploaded 2012-01-17 Søren Højsgaard =============================================== * URL updated * Rscript2HTML changed to Rmarkup * Version 4.4.5 uploaded 2011-12-30 Søren Højsgaard =============================================== * Change of email address * Version 4.4.4 uploaded. 2011-11-01 Søren Højsgaard =============================================== * Function lmBy (and related methods) added * Version 4.4.3 uploaded. 2011-11-01 Søren Højsgaard =============================================== * Dataset milkman added * Version 4.4.2 uploaded. 2011-10-23 Søren Højsgaard =============================================== * funBy function added (documentation to be done) * scaleBy function added * Minor bug fixed in summaryBy * popMeans extended to `mer` objects * KRmodcomp, PBmodcomp etc moved to the pbkrtest package * Version 4.4.1 uploaded. 2011-06-05 Søren Højsgaard =============================================== * subSeq function speeded up by considerably * KRmodcomp: Kenward-Rogers method for calculating denominator degrees of freedom for F--tests in mixed models (as fitted with lmer) * PBmodcomp, BCmodcomp, PBrefdist: Functions for calculating p-values based on parametric bootstrap for tests in mixed models (as fitted with lmer) * Version 4.4.0 uploaded 2011-05-05 Søren Højsgaard =============================================== * HTMLreport changed to Rscript2HTML * timeSinceEvent function enhanced * Version 4.3.1 uploaded. 2011-03-28 Søren Højsgaard =============================================== * popMeans (lsMeans) and linMeans functions added. * A vignette describing the use of popMeans added. * Functions popMatrix and linMatrix added * Version 4.3.0 uploaded 2011-02-02 Søren Højsgaard =============================================== * Bug fixed in timeSinceEvent * More work done on HTMLreport * Version 4.2.3 uploaded 2011-01-12 Søren Højsgaard =============================================== * HTMLreport function extended * Version 4.2.1 uploaded 2010-12-12 Søren Højsgaard =============================================== * HTMLreport function has been added. * Version 4.2.0 has been uploaded. 2010-11-24 Søren Højsgaard =============================================== * timeSinceEvent function has been added * summaryBy now takes the additional argument full.dimension which when TRUE causes the result to have the same number of rows as the input dataframe. * Version 4.1.2 has been uploaded. 2010-11-11 Søren Højsgaard =============================================== * recodevar deprecated. * recodeVar introduced with extended functionality. * subSeq function added. * renameCol function added. * Version 4.1.1 has been uploaded. 2010-11-09 Søren Højsgaard =============================================== * lsmeans function has been added. * Version 4.1.0 has been uploaded. 2010-05-05 Søren Højsgaard =============================================== * The sugar beets dataset 'beets' has been added * doBy no longer depends on Hmisc * Version 4.0.6 has been uploaded 2009-10-21 Søren Højsgaard =============================================== * orderBy applied to a dataframe with one column only returned a vector. Has been fixed so that a dataframe is returned. * Version 4.0.5 uploaded 2009-10-17 Søren Højsgaard =============================================== * Bug in NAMESPACE file fixed * Version 4.0.4 uploaded 2009-10-17 Søren Højsgaard =============================================== * esticon method for 'mer' objects added. (Was previously for 'lmer' objects). * Version 4.0.3 uploaded 2009-09-07 Søren Højsgaard =============================================== * Bug in summaryBy fixed: Strata did not match their values (as defined by the `rh.variables`). Thanks to Pascal Hirsch for pointing this out. * Version 4.0.2 uploaded 2009-08-17 Søren Højsgaard =============================================== * Bug in summaryBy fixed: id vars were included in the stratification of data which they should not be. Thanks to Joshua Rest for pointing this out. * Version 4.0.1 uploaded. 2009-07-28 Søren Højsgaard =============================================== * coxph method added for the esticon function. Thanks to Alessandro A Leidi. * splitBy changed so that names of the entries in the list match the levels of the factors (it used to be that the names matched the levels of the factors in reverse order). * The formula in splitBy can now also be a character vector. * `which.maxn` and `which.minn` functions added * Version 4.0.0 uploaded 2009-01-12 Søren Højsgaard =============================================== * splitBy had inconsistent output across cases. Fixed now * Version 3.8 uploaded 2008-12-21 Søren Højsgaard =============================================== * splitBy failed if by-variables were constant. Fixed now. * Version 3.7 uploaded. 2008-10-14 Søren Højsgaard =============================================== * splitBy now follows convention that first variable varies fastest. * Version 3.6 uploaded 2008-10-14 Søren Højsgaard =============================================== * Updated description of dietox data * Version 3.5 uploaded 2008-09-26 Søren Højsgaard =============================================== * Small bug in summaryBy fixed * Version 3.4 uploaded 2008-09-26 Søren Højsgaard =============================================== * Small bug in summaryBy fixed * Version 3.3 uploaded 2008-09-18 Søren Højsgaard =============================================== * Longer formulas now accepted (bug caused by a naive use of deparse()) * Version 3.2 uploaded 2008-06-13 Søren Højsgaard =============================================== * Bugs in variable names in summaryBy fixed * Small update of documentation * Version 3.1 uploaded 2008-05-20 Søren Højsgaard =============================================== * summaryBy speeded up; postfix argument removed * Version 3.0 uploaded 2008-03-26 Søren Højsgaard =============================================== * Minor bugs removed. * Version 2.3 uploaded 2008-02-13 Søren Højsgaard =============================================== * `firstobs` and `lastobs` functions added * Version 2.2 uploaded 2007-12-05 Søren Højsgaard =============================================== * splitBy: Results becomes a `splitByData` object. Two attributes are added: 1) grps, which is the grouping factors coded into a single variable. 2) idxvec, (a list) which holds the position of each row in the original data set. * lapplyBy: Following the changes above, the results of lapplyBy follows the ordering given by formula. * subsetBy: Now subset is a logical expression, not a character string * Version 2.1 uploaded 2007-09-30 Søren Højsgaard =============================================== * DESCRIPTION file change from Depends: Hmisc to Imports: Hmisc; 2007-05-01 Søren Højsgaard =============================================== * summaryBy: Behaviour with . on rhs of formula when data contains no factors has been corrected. * Version 1.9 uploaded 2007-05-01 Søren Højsgaard =============================================== * Miscellaneous corrections in the .Rd files * lapplyBy function has been added * Vignette has been updated * Version 1.8 uploaded 2007-04-21 Søren Højsgaard =============================================== * orderBy changed so that signs in formula determines whether to sort ascendingly or decreasingly 2007-03-21 Søren Højsgaard =============================================== * esticon extended to handle gls objects. 2006-12-14 Søren Højsgaard =============================================== * Version 1.6 uploaded * .asNumericMatrix2 etc now work correctly with date formats 2006-11-05 Søren Højsgaard =============================================== * Version 1.5 uploaded * Typo in package title corrected 2006-11-05 Søren Højsgaard =============================================== * Version 1.4 built and uploaded * A warning from Kurt H (regarding gsub...) has been fixed * Internal functions have been 'dotted' * A NAMESPACE file has been added * A file describing how to generate the NAMESPACE file automatically has been added. 2006-10-23 Søren Højsgaard =============================================== * Version 1.3 built and uploaded * Bug in splitBy corrected so that it works when rhs includes a character variable. 2006-10-23 Søren Højsgaard =============================================== * Version 1.2 built and uploaded * Description of codstom data updated. * `matrix2dataFrame2` and `subsAttr2` added * postfix argument in summaryBy can be a list 2006-10-17 Søren Højsgaard =============================================== * Version 1.1 built and uploaded * codstom.Rd updated * asNumericMatrix2 added because asNumericMatrix in Hmisc does not work if a variable is of type 'character' 2006-10-16 Søren Højsgaard =============================================== * Version 1.0 built and uploaded * summaryBy: More issues about naming of variables have been sorted out * summaryBy: Argument order=TRUE has been added * summaryBy: prefix=NULL changed to postfix=NULL, argument p2d has been added 2006-10-10 Søren Højsgaard =============================================== * Version 0.9 built and uploaded * summaryBy assigns better names to generated variables * A vignette has been created 2006-10-03 Søren Højsgaard =============================================== * Version 0.8 built and uploaded * Handles now ordered factors * Fixed bug so that summaryBy now works with do.call 2006-10-01 Søren Højsgaard =============================================== * Version 0.7 built and uploaded * splitBy is now based on functions in the Hmisc package for representing dataframes as matrices, which makes the function work also for 'large' (10000 rows) datasets. * summaryBy can now take . on both lhs and rhs of "~" 2006-09-27 Søren Højsgaard =============================================== * Various bugs have been fixed 2006-09-18 Søren Højsgaard =============================================== * summaryBy has been reimplemented. * summaryBy takes . as argument on both lhs and rhs in formula * summaryBy, splitBy takes drop=FALSE argument (just like split does) * transformBy function has been added 2006-02-05 Søren Højsgaard =============================================== * sampleBy modified allow for also 'systematic samples', e.g. every 5th row. * summaryBy takes ... arguments to FUN. No default NA action is taken. 2006-02-02 Søren Højsgaard =============================================== * A sampleBy function has been added. * A subsetBy function has been added. 2006-01-26 Søren Højsgaard =============================================== * Function summaryBy modified so one can write log(x) on the left hand side of '~' * Function summaryBy modified such that NAs are removed. What to do about Inf?? 2006-01-17 Søren Højsgaard =============================================== * Function summaryBy modified to take dot (.) as lhs in formula and to take `idvar = ~formula` doBy/inst/0000755000176200001440000000000015030320366012122 5ustar liggesusersdoBy/inst/doc/0000755000176200001440000000000015030320366012667 5ustar liggesusersdoBy/inst/doc/linest_lsmeans.Rnw0000644000176200001440000012441514735726554016432 0ustar liggesusers% \VignetteEngine{knitr::knitr} %\VignettePackage{doBy} %\VignetteIndexEntry{doBy: Linear estimates and LSmeans} %\VignetteIndexEntry{LSmeans} %\VignetteIndexEntry{population means} %\VignetteIndexEntry{contrasts} %\VignetteIndexEntry{estimable functions} \documentclass[11pt]{article} \usepackage{hyperref,url,color,Sweave,a4wide} \usepackage[utf8]{inputenc} \usepackage[authoryear,round]{natbib} \bibliographystyle{plainnat} \RequirePackage{color,fancyvrb,amsmath,amsfonts} \def\textasciigrave{'} \def\tt#1{\texttt{#1}} \def\R{\texttt{R}} \def\pkg#1{{\bf #1}} \def\doby{\pkg{doBy}} \def\code#1{\texttt{#1}} \def\cc#1{\texttt{#1}} \def\comi#1{{\it #1}} \def\esticon{\code{esticon()}} \def\lsmeans{\code{LSmeans}} \def\linmat{\code{LE_matrix()}} \def\linest{\code{linest()}} % reduce whitespace between R code and R output % \let\oldknitrout\knitrout % \renewenvironment{knitrout}{ % \begin{oldknitrout} % \footnotesize % \topsep=0pt % }{ % \end{oldknitrout} % } \DeclareMathOperator{\EE}{\mathbb{E}} \DeclareMathOperator{\var}{\mathbb{V}ar} \DeclareMathOperator{\cov}{\mathbb{C}ov} \DeclareMathOperator{\norm}{N} \DeclareMathOperator{\spanx}{span} \DeclareMathOperator{\corr}{Corr} \DeclareMathOperator{\deter}{det} \DeclareMathOperator{\trace}{tr} \def\inv{^{-1}} \newcommand{\transp}{^{\top}} <>= require( doBy ) prettyVersion <- packageDescription("doBy")$Version prettyDate <- format(Sys.Date()) @ \title{Linear estimates and LS--means in the \texttt{doBy} package} \author{S{\o}ren H{\o}jsgaard and Ulrich Halekoh} \date{\pkg{doBy} version \Sexpr{prettyVersion} as of \Sexpr{prettyDate}} % show R> prompt before R commands <>= knitr::opts_chunk$set(prompt=TRUE) library(knitr) if (!dir.exists("figures")) dir.create("figures") opts_chunk$set( tidy=FALSE,fig.path='figures/LSmeans', fig.height=3.5 ) oopt <- options() options("digits"=4, "width"=90, "prompt"="> ", "continue"=" ") options(useFancyQuotes="UTF-8") library(ggplot2) @ \definecolor{darkred}{rgb}{.7,0,0} \definecolor{midnightblue}{rgb}{0.098,0.098,0.439} \begin{document} <>= library(knitr) opts_chunk$set( concordance=TRUE ) @ % \SweaveOpts{concordance=TRUE} \maketitle \tableofcontents \parindent0pt \parskip5pt \section{Introduction} \label{sec:introduction} \subsection{Linear functions of parameters} \label{sec:line-funct-param} A linear function of a $p$--dimensional parameter vector $\beta$ has the form \begin{displaymath} C=L\beta \end{displaymath} where $L$ is a $q\times p$ matrix which we call the \comi{Linear Estimate Matrix} of simply \comi{LE-matrix}. The corresponding linear estimate is $\hat C = L \hat \beta$. A \comi{linear hypothesis} has the form $H_0: L\beta=m$ for some $q$ dimensional vector $m$. \subsection{LSmeans (population means, marginal means)} \label{sec::lsmeans} A special type of linear estimates is the so called \comi{least-squares means} (or \comi{LS-means}). Other names for these estimates include \comi{population means} and \comi{marginal means}. Consider an imaginary field experiment analyzed with model of the form <>= lm( y ~ treat + block + year) @ %def where \cc{treat} is a treatment factor, \cc{block} is a blocking factor and \cc{year} is the year (a factor) where the experiment is repeated over several years. This model specifies the conditional mean $\EE(Y|\cc{treat}, \cc{block},\cc{year})$. One may be interested in predictions of the form $\EE(Y|\cc{treat})$. This quantity can not formally be found from the model. However, it is tempting to average the fitted values of $\EE(Y|\cc{treat}, \cc{block},\cc{year})$ across the levels of \cc{block} and \cc{year} and think of this average as $\EE(Y|\cc{treat})$. This average is precisely what is called the LS--means. If the experiment is balanced then this average is identical to the average of the observations when stratified according to \cc{treat}. An alternative is to think of \cc{block} and \cc{year} as random effects, for example: <>= library(lme4) lmer( y ~ treat + (1|block) + (1|year)) @ %def In this case one would directly obtain $\EE(Y|\cc{treat})$ from the model. However, there are at least two reasons why one may be hesitant to consider such a random effects model. \begin{itemize} \item Suppose there are three blocks and the experiment is repeated over three consecutive years. This means that the random effects are likely to be estimated with a large uncertainty (the estimates will have only two degrees of freedom). \item Furthermore, treating \cc{block} and \cc{year} as random effects means they should in principle come from a large population of possible blocks and years. This may or may not be reasonable for the blocks, but it is a questionable assumption for the years. \end{itemize} \subsection{A simulated dataset} \label{sec:simulated-dataset} In the following sections we consider these data: <<>>= library(doBy) set.seed(141164) dd <- expand.grid(A=factor(1:3), B=factor(1:3), C=factor(1:2)) dd$y <- rnorm(nrow(dd)) dd$x <- rnorm(nrow(dd))^2 dd$z <- rnorm(nrow(dd)) head(dd,10) @ %def Consider the additive model \begin{equation} \label{eq:1} y_i = \beta_0 + \beta^1_{A(i)}+\beta^2_{B(i)} + \beta^3_{C(i)} + \beta^4 x_i + e_i \end{equation} where $e_i \sim N(0,\sigma^2)$. We fit this model: <<>>= mm <- lm(y ~ A + B + C + x, data=dd) coef(mm) @ %def Notice that the parameters corresponding to the factor levels \code{A1}, \code{B1} and \code{C2} are set to zero to ensure identifiability of the remaining parameters. \subsection{LSmeans: details} \label{sec:what-are-these} LSmeans, population means and marginal means are used synonymously in the literature. These quantities are a special kind of contrasts as defined in Section~\ref{sec:line-funct-param}. LSmeans seems to be the most widely used term, so we shall adopt this terms here too. The model (\ref{eq:1}) is a model for the conditional mean $\EE(y|A,B,C,x)$. Sometimes one is interested in quantities like $\EE(y|A)$. This quantity can not formally be found because of the presences of unless $B$, $C$ and $x$. However, suppose that $A$ is a treatment of main interest, $B$ is a blocking factor, $C$ represents days on which the experiment was carried out and $x$ denotes e.g. temperature for the specific block on the specific date. Then it is tempting to fix $x$ at its average value $\bar x$ and average $\EE(y|A,B,C, \bar x)$ over $B$ and $C$ (average over block and day) and think of this average as $\EE(y|A)$. The population mean for $A=1$ is \begin{equation} \label{eq:2} \beta^0 + \beta^1_{A1} + \frac{1}{3} (\beta^2_{B1}+\beta^2_{B2}+\beta^2_{B3}) + \frac{1}{2}(\beta^3_{C1}+\beta^3_{C2}) \end{equation} Recall that the parameters corresponding to the factor levels \code{A1}, \code{B1} and \code{C2} are set to zero to ensure identifiability of the remaining parameters. Therefore we may also write the population mean for $A=1$ as \begin{equation} \label{eq:3} \beta^0 + \frac{1}{3} (\beta^2_{B2}+\beta^2_{B3}) + \frac{1}{2}(\beta^3_{C2}) + \beta^4 \bar x. \end{equation} This quantity can be estimated as (if $\bar x = 1.242$) : <<>>= w <- c(1, 0, 0, 1/3, 1/3, 1/2, 1.242) sum(coef(mm) * w) @ %def We may find the population mean for all three levels of $A$ as <<>>= L <- matrix(c(1, 0, 0, 1/3, 1/3, 1/2, 1.242, 1, 1, 0, 1/3, 1/3, 1/2, 1.242, 1, 0, 1, 1/3, 1/3, 1/2, 1.242), nr=3, byrow=TRUE) L %*% coef(mm) @ %def Notice that the matrix W is based on that the first level of $A$ is set as the reference level. If the reference level is changed then so must $W$ be. % Given that one has specified $W$, we can use the \esticon\ function in the % \code{doBy} as illustrated below: % <<>>= % esticon(mm, W) % @ %def \def\popmatrix{LEmatrix} \def\popmeans{LSmeans} \subsection{Using \popmatrix\ and \popmeans} Writing the matrix $W$ is somewhat tedious and hence error prone. In addition, there is a potential risk of getting the wrong answer if the the reference level of a factor has been changed. The \popmatrix\ function provides an automated way of generating such matrices. The above \verb+W+ matrix is constructed by <<>>= L <- LE_matrix(mm, effect='A') L @ %def The \verb+effect+ argument requires to calculate the population means for each level of $A$ aggregating across the levels of the other variables in the data. The \popmeans\ function is simply a wrapper around first a call to \popmatrix\ followed by a call to (by default) \linest: <<>>= linest(mm, L=L) LSM <- LSmeans(mm, effect='A') LSM @ %def \subsection{Special details} \label{sec:special-details} \subsubsection{Summary} \label{sec:summary} More details about how the matrix was constructed is provided by the \code{summary()} method; for example: <<>>= summary(LSM) @ %def \subsubsection{Specification of \code{effect}} \label{sec:spec-code} The \code{effect} argument can be specified in two ways: <<>>= ## 1) a vector of characters and LE_matrix(mm, effect= c("A", "C")) ## 2) a right hand sided formula: ## LE_matrix(mm, effect= ~ A + C) @ \subsubsection{Fixing values in the \code{at} list} \label{sec:fixing-valu-code} We can fix values of the explanatory variables using the \code{at=} argument: For example: <<>>= LE_matrix(mm, at=list(A=1, x=2)) @ \subsubsection{Combining \code{effect} and \code{at}} \label{sec:comb-code-code} <<>>= LE_matrix(mm, effect= ~ A + C, at=list(A=1)) @ \subsubsection{Total average} \label{sec:total-average} Omitting \code{effect} as in <<>>= LE_matrix(mm) LSmeans(mm) @ %def gives the ``total average''. \subsubsection{Log transformed covariates} Unless otherwise specified, numerical covariates are fixed at their average value, for example for \code{x} below: If we use \code{log(x)} instead we will get an error when calculating LS--means. Instead one must modify data: <<>>= mm2 <- lm(y ~ A + B + C + log(x), data=dd) ## LSmeans(mm2, effect=~A) ## Will fail mm2 <- lm(y ~ A + B + C + log.x, data=transform(dd, log.x=log(x))) LSmeans(mm2, effect=~A) LSmeans(mm2, effect=~A)$L @ This also highlights what is computed: The average of the log of \code{x}; not the log of the average of \code{x}. \subsubsection{Powers of covariates} Similar phenomenon for powers of covariates: <<>>= mm2 <- lm(y ~ A + B + C + x + I(x^2), data=dd) LSmeans(mm2, effect=~A) LSmeans(mm2, effect=~A)$L @ %def Above \verb|I(x^2)| is the average of the squared values of \code{x}; not the square of the average of \code{x}, cfr.\ the following. <<>>= mm2 <- lm(y ~ A + B + C + x + x2, data=transform(dd, x2=x^2)) LSmeans(mm2, effect=~A) LSmeans(mm2, effect=~A)$L @ %def % If we want to evaluate the LS--means at \code{conc=10} then we can do: % <<>>= % LSmeans(co2.lm4, effect="Treatment", at=list(conc=10, conc2=100)) % @ %def \section{Miscellaneous} \label{sec:miscellaneous} \subsection{Excerpt of the CO2 data} \label{sec:excerpt-co2-data} For illustration we use subsets of the CO2 data: <<>>= data(CO2) CO2 <- subset(CO2, conc < 300) ## OK CO2.bal <- CO2 rownames(CO2.bal) <- NULL @ % <<>>= % xtabs(~Type + Treatment + conc, data=CO2.bal) |> % ftable(row.vars = "Type") % @ Data is perfectly balanced. We also use an unbalanced subset of the data <<>>= CO2.ubal <- CO2.bal[-c(1, 5, 12, 17, 18, 19, 20, 28),] CO2.ubal |> head() xtabs(~Type + Treatment + conc, data=CO2.ubal) |> ftable(row.vars = "Type") @ % <<>>= % p1 <- ggplot(CO2.bal, aes(x=conc, y=uptake, color=Treatment, group=Plant)) + % geom_line() + geom_point() + facet_grid(~Type) % p2 <- ggplot(CO2.ubal, aes(x=conc, y=uptake, color=Treatment, group=Plant)) + % geom_line() + geom_point() + facet_grid(~Type) % library(patchwork) ## Multiple graphs % p1 / p2 % @ \subsection{Two linear models for CO2 data} \label{sec:two-linear-models} <<>>= library(broom) form.add <- uptake ~ conc + Treatment + Type form.int <- uptake ~ conc * Treatment + Type fm1.bal <- lm(form.add, data=CO2.bal) fm1.ubal <- lm(form.add, data=CO2.ubal) @ \subsection{Linear estimates and LSmeans} \label{sec:line-estim-using} \paragraph{Common task 1:} Consider this task: Estimate the value of the response \tt uptake for each combination of \tt Type and \tt Treatment . This can be obtained, for example, as follows: <<>>= LSmeans(fm1.bal, effect=~Type+Treatment, at=list(conc=100)) LSmeans(fm1.ubal, effect=~Type+Treatment, at=list(conc=100)) @ \paragraph{Common task 2:} Estimate the value of the response \tt uptake when \tt for each level of \tt Treatment. Formally this question can not be answered under the model because the model gives the conditional mean of \tt uptake given \tt conc, \tt Type and \tt Treatment. There is, so to speak, no way of getting rid of \tt Type. There are different workarounds for this situation: We can average over \tt Type and fix \t conc at its average. Alternatively, we can fit model without \tt Type effect and repeat the steps above. <<>>= LSmeans(fm1.bal, effect=~Treatment) LSmeans(fm1.ubal, effect=~Treatment) @ <<>>= ## Fit model without Type fm2.bal <- update(fm1.bal, .~. - Type) fm2.ubal <- update(fm1.ubal, .~. - Type) LSmeans(fm2.bal, effect=~Treatment) LSmeans(fm2.ubal, effect=~Treatment) @ Notice that the predictions (estimates) are identical for the balanced dataset but not for the unbalanced dataset. Notice also that the standard errors of the predictions are somewhat larger when fitting a model without \tt Type effect. This is to be expected as the variation due to \tt Type goes into the residual. \subsection{Generalized linear models} \label{sec:gener-line-models} We can calculate LS--means for e.g.\ a Poisson or a gamma model. Default is that the calculation is calculated on the scale of the linear predictor. <<>>= fm.glm <- glm(form.add, family=Gamma, data=CO2.ubal) LSmeans(fm.glm, effect=~Treatment, type="link") @ %def \subsection{Generalized estimating equations} \label{sec:gener-estim-equat} For gee-type models we get <<>>= library(geepack) fm.gee <- geeglm(uptake ~ conc + Treatment + Type, id=Plant, family=Gamma, data=CO2.ubal) LSmeans(fm.gee, effect=~Treatment) @ %def \subsection{Linear mixed effects model} \label{sec:linear-mixed-effects} For illustration we treat \verb|Plant| as a random effect: << >>= library(lme4) fm.mix <- lmer(uptake ~ conc + Treatment + Type + (1|Plant), data=CO2.ubal) LSmeans(fm.mix, effect=~Treatment) @ Notice that the degrees of freedom by default are adjusted using a Kenward--Roger approximation. Unadjusted degrees of freedom are obtained by setting \verb|adjust.df=FALSE|. \subsection{Pairwise comparisons} \label{sec:pairwise-comparisons} We will just mention that for certain other linear estimates, the matrix $L$ can be generated automatically using \cc{glht()} from the \pkg{multcomp} package. For example, pairwise comparisons of all levels of \code{dose} can be obtained with << >>= library("multcomp") g1 <- glht(fm2.ubal, mcp(Treatment="Tukey")) tidy(g1) @ The L matrix is << >>= L <- g1$linfct L linest(fm2.ubal, L=L) @ % \subsection{Manual computations} % \label{sec:manual-computations} % Averaging over predicted values amounts to averaging over weighted % sums of the regression coefficients. The weights can be generated % manually as: % <<>>= % coef(fm1.ubal) % L01 <- matrix(c(1, 100, 0, 0, % 1, 100, 1, 0, % 1, 100, 0, 1, % 1, 100, 1, 1), ncol=4, byrow=T) % L01 %*% coef(fm1.ubal) % L02 <- matrix(c(1, 100, 0, 0.5, % 1, 100, 1, 0.5), ncol=4, byrow=T) % L02 %*% coef(fm1.ubal) % L03 <- matrix(c(1, 179.8, 0, 0.5, % 1, 179.8, 1, 0.5), ncol=4, byrow=T) % L03 %*% coef(fm1.ubal) % @ % \subsection{Automatic generation of LEmatrix} % \label{sec:autom-gener-lematr} % Averaging over predicted values amounts to averaging over weighted % sums of the regression coefficients. The weights can be generated % automatically as follows: % \paragraph{Common task 1:} % We calulate genereate the $L$ matrix automaically and the matrix and the model object into a function that returns the predictions, standard errors etc. % <<>>= % L1 <- LE_matrix(fm1.ubal, effect=~Treatment + Type, at=list(conc=100)) % L1 % C1 <- linest(fm1.ubal, L=L1) % C1 % @ % The steps above can be made with one function call: % <<>>= % LSM1 <- LSmeans(fm1.ubal, effect=~Treatment + Type, at=list(conc=100)) % LSM1 % @ % \paragraph{Common task 2: } % Likewise, for the second situation: % <<>>= % L2 <- LE_matrix(fm1.ubal, effect=~Treatment, at=list(conc=100)); L2 % C2 <- linest(fm1.ubal, L=L2); C2 % LSM2 <- LSmeans(fm1.ubal, effect=~Treatment, at=list(conc=100)) % LSM2 % @ % \paragraph{Common task 3: } % Finally for the third situation: % <<>>= % L3 <- LE_matrix(fm1.ubal, effect=~Treatment); % L3 % C3 <- linest(fm1.ubal, L=L3); % C3 % LSM3 <- LSmeans(fm1.ubal, effect=~Treatment) % LSM3 % @ % \subsection{ssssssssssssssss} % \label{sec:ssssssssssssssss} % Notice this information: % <<>>= % summary(L3) % summary(C3) % summary(LSM3) % @ % \subsection{Using the \code{at=} argument} % \label{sec:at-argument} % <>= % library(ggplot2) % ChickWeight$Diet <- factor(ChickWeight$Diet) % qplot(Time, weight, data=ChickWeight, colour=Chick, facets=~Diet, % geom=c("point","line")) + theme(legend.position="none") % @ %def % Consider random regression model: % <<>>= % library(lme4) % chick <- lmer(weight ~ Time * Diet + (0 + Time | Chick), % data=ChickWeight) % coef(summary(chick)) % @ %def % The LE--matrix for \cc{Diet} becomes: % <<>>= % L <- LE_matrix(chick, effect="Diet") % t(L) % @ %def % The value of \cc{Time} is by default taken to be the average of that % variable. Hence the \lsmeans\ is the predicted weight for each diet at % that specific point of time. We can consider other points of time with % <<>>= % K1 <- LE_matrix(chick, effect="Diet", at=list(Time=1)) % t(K1) % @ %def % The \lsmeans\ for the intercepts is the predictions at % \cc{Time=0}. The \lsmeans\ for the slopes becomes % <<>>= % K0 <- LE_matrix(chick, effect="Diet", at=list(Time=0)) % t(K1 - K0) % linest(chick, L=K1 - K0) % @ %def % We may be interested in finding the population means % at all levels of $A$ % but only at $C=1$. This is obtained by using the \code{at} argument % (in which case the average is only over the remaining factor $B$): % <<>>= % popMatrix(mm, effect='A', at=list(C='1')) % @ %def % Another way of % creating the population means % at all levels of $(A,C)$ is therefore % <<>>= % popMatrix(mm, effect='A', at=list(C=c('1','2'))) % @ %def % We may have several variables in the \code{at} argument: % @ % <<>>= % popMatrix(mm, effect='A', at=list(C=c('1','2'), B='1')) % @ %def % We can create our own function for comparing trends: % <<>>= % LSmeans_trend <- function(object, effect, trend){ % L <- LE_matrix(object, effect=effect, at=as.list(setNames(1, trend))) - % LE_matrix(object, effect=effect, at=as.list(setNames(0, trend))) % linest(object, L=L) % } % ## LSmeans_trend(chick, effect="Diet", trend="Time") % @ %def % <<>>= % LSmeans(fm.mix, effect=~Treatment, adjust.df=FALSE) % LSmeans(fm.mix, effect=~Treatment, adjust.df=TRUE) % @ %def % \subsection{Example: Non--estimable linear functions} % \label{sec:exampl-non-estim} % <<>>= % ## Make balanced dataset % dat.bal <- expand.grid(list(AA=factor(1:2), BB=factor(1:3), CC=factor(1:3))) % dat.bal$y <- rnorm(nrow(dat.bal)) % ## Make unbalanced dataset: 'BB' is nested within 'CC' so BB=1 % ## is only found when CC=1 and BB=2,3 are found in each CC=2,3,4 % dat.nst <- dat.bal % dat.nst$CC <-factor(c(1,1,2,2,2,2,1,1,3,3,3,3,1,1,4,4,4,4)) % @ %def % << >>= % dat.nst % @ % Consider this simulated dataset: % <<>>= % head(dat.nst, 4) % ftable(xtabs( ~ AA + BB + CC, data=dat.nst), row.vars="AA") % @ %def % Data is highly "unbalanced": % Whenever \verb|BB=1| then \verb|CC| is always \verb|1|; whenever % \verb|BB| is not \verb|1| then \verb|CC| is never \verb|1|. % We have % <<>>= % mod.nst <- lm(y ~ AA + BB : CC, data=dat.nst) % coef(mod.nst) % coef(summary(mod.nst)) % @ %def % In this case some of the \lsmeans\ values are not estimable; for example: % <<>>= % lsm.BC <- LSmeans(mod.nst, effect=c("BB", "CC")) % lsm.BC % lsm.BC2 <- LSmeans(mod.nst, effect="BB", at=list(CC=2)) % lsm.BC2 % @ %def % We describe the situation in % Section~\ref{sec:handl-non-estim} where we focus on \verb|lsm.BC2|. % \subsection{Handling non--estimability} % \label{sec:handl-non-estim} % The model matrix for the model in Section~\ref{sec:exampl-non-estim} % does not have full column rank and therefore not all values are % calculated by \cc{LSmeans()}. % <<>>= % X <- model.matrix( mod.nst ) % Matrix::rankMatrix(X) % dim(X) % as(X, "Matrix") % @ %def % We consider a model, i.e.\ % an $n$ dimensional random vector $y=(y_i)$ for which % $\EE(y)=\mu=X\beta$ and $\cov(y)=V$ where $X$ does not have % full column rank % We are % interested in linear functions of $\beta$, say % \begin{displaymath} % c=l\transp\beta= \sum_j l_j \beta_j . % \end{displaymath} % <<>>= % L <- LE_matrix(mod.nst, effect="BB", at=list(CC=2)) % t(L) % linest(mod.nst, L=L) % @ %def % A least squares estimate of $\beta$ is % \begin{displaymath} % \hat \beta = G X\transp y % \end{displaymath} % where $G$ is a generalized inverse of $X\transp X$. Since the % generalized inverse is not unique then neither is the estimate % $\hat\beta$. Hence $\hat c = l\transp\hat \beta$ is in general not % unique. % One least squares estimate of $\beta$ and one corresponding linear estimate $L\hat\beta$ is: % <<>>= % XtXinv <- MASS::ginv(t(X)%*%X) % bhat <- as.numeric(XtXinv %*% t(X) %*% dat.nst$y) % zapsmall(bhat) % L %*% bhat % @ %def % For some values of $l$ (i.e.\ for some rows of $L$) % the estimate $\hat c=l\transp \beta$ is % unique (i.e.\ it does not depend on the choice of generalized % inverse). % Such linear functions are said to be estimable and can be % described as follows: % All we specify with $\mu=X\beta$ % is that $\mu$ is a vector in the column space $C(X)$ of $X$. % We can only learn about $\beta$ through $X\beta$ so the only thing we can % say something about is linear combinations $\rho\transp X\beta$. Hence % we can only say something about $l\transp\beta$ if there exists % $\rho$ such that % $$ % l\transp\beta=\rho\transp X \beta, % $$ % i.e., if % $l=X\transp\rho$ for some $\rho$, which is if $l$ is in the column space % $C(X\transp)$ of $X\transp$. This is the same as saying that $l$ must be % perpendicular to % all vectors in the null space $N(X)$ of $X$. To check % this, we find a basis $B$ for $N(X)$. This can be done in many ways, % for example via a singular value decomposition of $X$, i.e.\ % \begin{displaymath} % X = U D V\transp % \end{displaymath} % A basis for $N(X)$ is given by those columns of $V$ that corresponds % to zeros on the diagonal of $D$. % <<>>= % S <- svd(X) % B <- S$v[, S$d < 1e-10, drop=FALSE ]; % head(B) ## Basis for N(X) % @ %def % From % << >>= % rowSums(L %*% B) % @ % we conclude that the first row of $L$ is not perpendicular to all vectors in thenull space $N(X)$ whereas the two last rows of $L$ are. Hence these two linear estimates are estimable; their value does not depend on the choice of generalized inverse: % << >>= % lsm.BC2 % @ % \subsection{Alternatives - \texttt{esticon()}} % \label{sec:alternatives} % An alternative is to do: % << >>= % c1 <- esticon(fm1.ubal, L2) % c1 % @ % Notice: \verb|esticon| has been in the \doby\ package for many years; % \verb|linest| is a newer addition; \verb|esticon| is not actively % maintained but remains in \doby\ for historical reasons. % \subsection{Using the \code{at} argument} % We may be interested in finding the population means % at all levels of $A$ % but only at $C=1$. This is obtained by using the \code{at} argument % (in which case the average is only over the remaining factor $B$): % <<>>= % popMatrix(mm, effect='A', at=list(C='1')) % @ %def % Another way of % creating the population means % at all levels of $(A,C)$ is therefore % <<>>= % popMatrix(mm, effect='A', at=list(C=c('1','2'))) % @ %def % We may have several variables in the \code{at} argument: % @ % <<>>= % popMatrix(mm, effect='A', at=list(C=c('1','2'), B='1')) % @ %def % \subsection{Ambiguous specification when using the \texttt{effect} and % \texttt{at} arguments} % There is room for an ambiguous specification if a variable appears in % both the \code{effect} and the \code{at} argument, such as % @ % <<>>= % popMatrix(mm, effect=c('A','C'), at=list(C='1')) % @ %def % This ambiguity is due to the fact that the \verb+effect+ argument asks % for the populations means at all levels of the variables but the % \verb+at+ chooses only specific levels. % This ambiguity is resolved as follows: Any variable in the \code{at} % argument is removed from the \code{effect} argument such as the % statement above is equivalent to % @ % <>= % ## popMatrix(mm, effect='A', at=list(C='1')) % @ %def % \subsection{Using covariates} % Next consider the model where a covariate is included: % @ % <<>>= % mm2 <- lm(y~A+B+C+C:x, data=dd) % coef(mm2) % @ %def % In this case we get % <<>>= % popMatrix(mm2,effect='A', at=list(C='1')) % @ %def % Above, $x$ has been replaced by its average and that is the general % rule for models including covariates. However we may use the \code{at} % argument to ask for calculation of the population mean at some % user-specified value of $x$, say 12: % <<>>= % popMatrix(mm2,effect='A', at=list(C='1',x=12)) % @ %def % <<>>= % mm22 <- lm(y~A+B+C+C:x+I(x^2), data=dd) % coef(mm22) % @ %def % <<>>= % popMatrix(mm22,effect='A', at=list(C='1')) % @ %def % <<>>= % dd <- transform(dd, x.sq=x^2) % mm23 <- lm(y~A+B+C+C:x+x.sq, data=dd) % coef(mm23) % popMatrix(mm23,effect='A', at=list(C='1')) % @ %def % \subsection{Using transformed covariates} % Next consider the model where a transformation of a covariate is included: % @ % <<>>= % mm3 <- lm(y~A+B+C+C:I(log(x)), data=dd) % coef(summary(mm3)) % @ %def % In this case we can not use \popmatrix\ (and hence % \popmeans) directly. Instead we first have to % generate a new variable, say \verb+log.x+, with % \verb+log.x+$=\log(x)$, in the data and then proceed as % <<>>= % dd <- transform(dd, log.x = log(x)) % mm32 <- lm(y~A+B+C+C:log.x, data=dd) % popMatrix(mm32, effect='A', at=list(C='1')) % @ %def % \subsection{The \code{engine} argument of \popmeans} % The \popmatrix is a function to generate a linear tranformation matrix of the model % parameters with emphasis on constructing such matrices for population % means. % \popmeans\ invokes by default the \esticon\ function on this % linear transformation matrix for calculating parameter estimates and % confidecne intervals. % A similar function to \esticon\ is the \verb+glht+ function of the \verb+multcomp+ % package. % The \code{glht()} function % can be chosen via the \verb+engine+ argument of \popmeans: % <<>>= % library(multcomp) % g<-popMeans(mm,effect='A', at=list(C='1'),engine="glht") % g % @ %def % This allows to apply the methods available on the \verb+glht+ object like % <<>>= % summary(g,test=univariate()) % confint(g,calpha=univariate_calpha()) % @ % which yield the same results as the \esticon\ function. % By default the functions will adjust the tests and confidence intervals for multiplicity % <<>>= % summary(g) % confint(g) % @ % \subsection{Ambiguous specification when using the \texttt{effect} and % \texttt{at} arguments} % There is room for an ambiguous specification if a variable appears in % both the \code{effect} and the \code{at} argument, such as % <<>>= % LE_matrix(fm1.ubal, effect=c('Treatment','Type'), at=list(Type='Mississippi')) % LE_matrix(fm1.ubal, effect=~Treatment + Type, at=list(Type='Mississippi')) % @ %def % This ambiguity is due to the fact that the \verb+effect+ argument asks % for the populations means at all levels of the variables but the % \verb+at+ chooses only specific levels. % This ambiguity is resolved as follows: Any variable in the \code{at} % argument is removed from the \code{effect} argument such as the % statement above is equivalent to\footnote{FIXME} % <>= % LE_matrix(fm1.ubal, effect=c('Treatement'), at=list(Type='Mississippi')) % LE_matrix(fm1.ubal, effect=~Treatement, at=list(Type='Mississippi')) % @ %def %%\bibliography{doBy} \end{document} % \section{LS--means for linear models} % \label{sec:linear-model} % \subsection{LS--means -- a first example} % \label{sec:ls-means-population} % <>= % simdat<-structure(list(treat = structure(c(1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L % ), .Label = c("t1", "t2"), class = "factor"), year = structure(c(1L, % 1L, 1L, 1L, 2L, 2L, 2L, 2L), .Label = c("1", "2"), class = "factor"), % y = c(0.5, 1, 1.5, 3, 3, 4.5, 5, 5.5)), .Names = c("treat", "year", % "y"), row.names = c(NA, -8L), class = "data.frame") % @ %def % Consider these simulated data % <<>>= % simdat % @ %def % shown in the figure below. % <>= % library(ggplot2) % qplot(treat, y, data=simdat, color=year, size=I(3)) % @ %def % \includegraphics[height=6cm,width=12cm]{figures/LSmeans-simdat-fig} % The LS--means under an additive model for the factor \cc{treat} is % <<>>= % msim <- lm(y ~ treat + year, data=simdat) % LSmeans( msim, effect="treat") % @ %def % whereas the population means are % <<>>= % summaryBy(y~treat, data=simdat, FUN=mean) % @ %def % Had data been balanced (same number of observations for each % combination of \cc{treat} and \cc{year}) the results would have been the % same. An argument in favor of the LS--means is that these figures % better represent what one would expect on in an ``average year''. % \subsection{Example: Warpbreaks} % \label{sec:example:-warpbreaks} % <<>>= % summary( warpbreaks ) % head( warpbreaks, 4 ) % ftable(xtabs( ~ wool + tension, data=warpbreaks)) % @ %def % <>= % opar <- par(mfrow = c(1, 2), oma = c(0, 0, 1.1, 0)) % plot(breaks ~ tension, data = warpbreaks, col = "lightgray", % varwidth = TRUE, subset = wool == "A", main = "Wool A") % plot(breaks ~ tension, data = warpbreaks, col = "lightgray", % varwidth = TRUE, subset = wool == "B", main = "Wool B") % mtext("warpbreaks data", side = 3, outer = TRUE) % par(opar) % @ %def % <<>>= % (warp.lm <- lm(breaks ~ wool + tension, data=warpbreaks)) % @ %def % The fitted values are: % <<>>= % uni <- unique(warpbreaks[,2:3]) % prd <- cbind(breaks=predict(warp.lm, newdata=uni), uni); prd % @ %def % \subsection{The LS--means} % \label{sec:lsmeans} % We may be interested in making predictions of the number of breaks for % each level of \cc{tension} for \emph{any} type or an \emph{average} % type of \code{wool}. The idea behind LS--means is % to average the predictions above over the two % wool types. These quantities are the \lsmeans\ for the effect % \cc{tension}. % This is done with: % <<>>= % LSmeans(warp.lm, effect="tension") % @ %def % The term \lsmeans\ comes from that these quantities are the same as % the least squares main effects of \cc{tension} when data is balanced: % <<>>= % doBy::summaryBy(breaks ~ tension, data=warpbreaks, FUN=mean) % @ %def % When data is not balanced these quantities are in general not the same. % \subsection{LS--means for models with interactions} % \label{sec:models-with-inter} % Consider a model with interaction: % <<>>= % warp.lm2 <- update(warp.lm, .~.+wool:tension) % coef( summary( warp.lm2 )) % @ %def % In this case the contrast matrix becomes: % <<>>= % K2 <- LE_matrix(warp.lm2, effect="tension"); K2 % linest(warp.lm2, K=K2) % @ %def % For the sake of illustration we treat \cc{wool} as a random effect: % <<>>= % library(lme4) % warp.mm <- lmer(breaks ~ tension + (1|wool), data=warpbreaks) % LSmeans(warp.mm, effect="tension") % @ %def % Notice here that the estimates themselves are very similar to those % above but the standard errors are much larger. This comes from that % there that \code{wool} is treated as a random effect. % <<>>= % VarCorr(warp.mm) % @ %def %\usepackage{framed} %\usepackage{comment} %%\definecolor{shadecolor}{gray}{0.91} %%\definecolor{darkred}{rgb}{.7,0,0} %%\definecolor{midnightblue}{rgb}{0.098,0.098,0.439} %% \DefineVerbatimEnvironment{Sinput}{Verbatim}{ %% fontfamily=tt, %% %%fontseries=b, %% %% xleftmargin=2em, %% formatcom={\color{midnightblue}} %% } %% \DefineVerbatimEnvironment{Soutput}{Verbatim}{ %% fontfamily=tt, %% %%fontseries=b, %% %% xleftmargin=2em, %% formatcom={\color{blue}} %% } %% \DefineVerbatimEnvironment{Scode}{Verbatim}{ %% fontfamily=tt, %% %%fontseries=b, %% %% xleftmargin=2em, %% formatcom={\color{blue}} %% } %% %%\fvset{listparameters={\setlength{\topsep}{-2pt}}} %\renewenvironment{Schunk}{\linespread{.90}}{} %%\renewenvironment{Schunk}{\begin{shaded}\small}{\end{shaded}} %% A special type of linear estimates is the so called least--squares %% means (or LS--means). Other names for these estimates include %% population means and marginal means. Notice that the \pkg{lsmeans} package %% \cite{Lenth:2013} also provides computations of LS--means, see %% \url{http://cran.r-project.org/web/packages/lsmeans/}. %% <>= %% simdat<-structure(list(treat = structure(c(1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L %% ), .Label = c("t1", "t2"), class = "factor"), year = structure(c(1L, %% 1L, 1L, 1L, 2L, 2L, 2L, 2L), .Label = c("1", "2"), class = "factor"), %% y = c(0.5, 1, 1.5, 3, 3, 4.5, 5, 5.5)), .Names = c("treat", "year", %% "y"), row.names = c(NA, -8L), class = "data.frame") %% @ %def %% \subsection{LS--means on a simple example} %% \label{sec:ls-means-simple} %% Consider these simulated data, also shown in Fig.~\ref{fig:simdat2-fig}: %% <<>>= %% simdat %% @ %def %% <>= %% library(ggplot2) %% qplot(treat, y, data=simdat, color=year) %% @ %def %% The LS--means under an additive model for the factor \cc{treat} is the predicted outcome for each level of \cc{treat} for an ``average year'': %% <<>>= %% msim <- lm(y ~ treat + year, data=simdat) %% lsm <- LSmeans(msim, effect="treat") %% lsm %% @ %def %% Notice that the population means are %% <<>>= %% summaryBy(y ~ treat, data=simdat, FUN=function(x) c(m=mean(x), s=sd(x))) %% ## or aggregate(y ~ treat, data=simdat, FUN=function(x) c(m=mean(x), s=sd(x))) %% @ %def %% Had data been balanced (same number of observations for each %% combination of \cc{treat} and \cc{year}) the results would have been the %% same. An argument in favor of the LS--means is that these figures %% better represent what one would expect on in an ``average year''. %% An alternative is to think of \cc{year} as a random %% effect, for example: %% <>= %% library(lme4) %% lmer( y ~ treat + (1|year), data=simdat) %% @ %def %% In this case one would directly obtain $\EE(Y|\cc{treat})$ from the %% model. However, there are at least two reasons why one may be hesitant %% to consider such a random effects model. %% \begin{itemize} %% \item It is a ``never ending'' discussion whether \verb|year| should %% be treated as fixed or random. For \verb|year| to be a random %% effect, it should in principle come from a large population of %% possible years. This is certainly a dubious assumption for the %% years. %% \item If we accept to think of year as a random effect, then the %% variance describing this random effect will be poorly determined %% (because there are only two years in the study). %% \end{itemize} % <<>>= % warp.poi <- glm(breaks ~ wool + tension, family=poisson, data=warpbreaks) % LSmeans(warp.poi, effect="tension", type="link") % LSmeans(warp.poi, effect="tension", type="response") % @ %def %% SANITY CHECK %% @ %% <<>>= %% K <- LE_matrix(warp.poi, effect="tension") %% doBy::esticon(warp.poi, K) %% @ %def % <<>>= % warp.qpoi <- glm(breaks ~ wool + tension, family=quasipoisson, data=warpbreaks) % LSmeans(warp.qpoi, effect="tension", type="link") % LSmeans(warp.qpoi, effect="tension", type="response") % @ %def % For comparison with the linear model, we use identity link % <>= % warp.poi2 <- glm(breaks ~ wool + tension, family=poisson(link=identity), % data=warpbreaks) % LSmeans(warp.poi2, effect="tension", type="link") % @ %def %% SANITY CHECK %% @ %% <<>>= %% K <- LE_matrix(warp.poi2, effect="tension") %% doBy::esticon(warp.poi2, K) %% @ %def % <<>>= % warp.gam <- glm(breaks ~ wool + tension, family=Gamma(link=identity), % data=warpbreaks) % LSmeans(warp.gam, effect="tension", type="link") % @ %def %% SANITY CHECK %% @ %% <<>>= %% K <- LE_matrix(warp.gam, effect="tension") %% doBy::esticon(warp.gam, K) %% @ %def % Notice that the linear estimates are practically the same as for the % linear model, but the standard errors are smaller and hence the % confidence intervals are narrower. % An alternative is to fit a quasi Poisson ``model'' % <<>>= % warp.poi3 <- glm(breaks ~ wool + tension, family=quasipoisson(link=identity), % data=warpbreaks) % LSmeans(warp.poi3, effect="tension") % @ %def % \subsubsection{For balanced data} % \label{sec:balanced-case} % When the dataset is balanced the results will be the same % <<>>= % aggregate(pred ~ Treatment, data=bb1, FUN=mean) % fm2.bal <- update(fm1.bal, .~. - Type) % grid2 <- expand.grid( % Treatment = c("nonchilled", "chilled"), % conc = 100 % ) % bb2 <- cbind(pred=predict(fm2.bal, newdata=grid2), grid2) % bb2 % @ % \subsubsection{For unbalanced data} % \label{sec:unbalanced-case} % In general, however, the results will not be identical % <<>>= % L <- matrix(c(1, 100, 0, 0, % 1, 100, 0, 1, % 1, 100, 1, 0, % 1, 100, 1, 1), ncol=4, byrow=T) % @ % \subsection{Tooth growth} % \label{sec:tooth-growth} % % The response is the length of odontoblasts cells (cells responsible for % % tooth growth) in 60 guinea pigs. Each animal received one of % % three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of % % two delivery methods, (orange juice (coded as OJ) or ascorbic acid % % (a form of vitamin C and (coded as VC)). % % <<>>= % % head(ToothGrowth, 4) % % ftable(xtabs(~ dose + supp, data=ToothGrowth)) % % @ %def % % <>= % % opar <- par(mfrow = c(1, 2), oma = c(0, 0, 1.1, 0)) % % plot(len ~ dose, data = ToothGrowth, col = "lightgray", % % subset = supp == "OJ", main = "supp=OJ") % % plot(len ~ dose, data = ToothGrowth, col = "lightgray", % % subset = supp == "VC", main = "supp=VC") % % mtext("ToothGrowth data", side = 3, outer = TRUE) % % par(opar) % % @ %def % % <<>>= % % ToothGrowth |> interaction_plot(len ~ dose + supp) % % @ % % The interaction plot suggests a mild interaction which is supported by a formal comparison: % << >>= % ToothGrowth$dose <- factor(ToothGrowth$dose) % head(ToothGrowth) % tooth1 <- lm(len ~ dose + supp, data=ToothGrowth) % tooth2 <- lm(len ~ dose * supp, data=ToothGrowth) % anova(tooth1, tooth2) % @ %% <>= %% tooth_avglen <- ToothGrowth |> %% group_by(dose, supp) |> %% summarise(val = mean(len)) %% tooth_avglen %% ggplot(ToothGrowth, aes(x = factor(dose), y = len, colour = supp)) + %% geom_boxplot(outlier.shape = 4) + %% geom_point(data = tooth_avglen, aes(y = val)) + %% geom_line(data = tooth_avglen, aes(y = val, group = supp)) %%@ % \section{Computing linear estimates} % \label{sec:comp-line-estim} % For now, we focus on the additive model: % << >>= % tooth1 % @ % Consider computing the estimated length for each dose of orange juice (OJ): % One option: Construct the LE--matrix $L$ directly: % << >>= % L <- matrix(c(1, 0, 0, 0, % 1, 1, 0, 0, % 1, 0, 1, 0), nrow=3, byrow=T) % @ % Then do: % << >>= % c1 <- linest(tooth1, L) % c1 % @ % We can do: % << >>= % summary(c1) % coef(c1) % confint(c1) % @ % \subsection{Automatic generation of $L$} % \label{sec:autom-gener-l} % The matrix $L$ can be generated as follows: % << >>= % L <- LE_matrix(tooth1, effect="dose", at=list(supp="OJ")) % L % @ % \subsection{OLD: Least-squares means (LS--means)} % \label{sec:least-squares-means} % A related question could be: What is the estimated length for each % dose if we ignore the source of vitamin C (i.e.\ whether it is OJ or % VC). One approach would be to fit a model in which source does not appear: % % << >>= % % tooth0 <- update(tooth1, . ~ . - supp) % % L0 <- LE_matrix(tooth0, effect="dose") % % L0 % % linest(tooth0, L=L0) % % @ % An alternative would be to stick to the original model but compute the % estimate for an ``average vitamin C source''. That would correspond to % giving weight $1/2$ to each of the two vitamin C source % parameters. However, as one of the parameters is already set to zero % to obtain identifiability, we obtain the LE--matrix $L$ as % % << >>= % % L1 <- matrix(c(1, 0, 0, 0.5, % % 1, 1, 0, 0.5, % % 1, 0, 1, 0.5), nrow=3, byrow=T) % % linest(tooth1, L=L1) % % @ % Such a particular linear estimate is sometimes called a least-squares % mean or an LSmean or a marginal mean. Notice that the parameter % estimates under the two approaches are identical. This is is because % data is balanced: There are $10$ observations per supplementation % type. Had data not been balanced, the estimates would in general have been different. % % Notice: One may generate $L$ automatically with % % << >>= % % L1 <- LE_matrix(tooth1, effect="dose") % % L1 % % @ % Notice: One may obtain the LSmean directly as: % % << >>= % % LSmeans(tooth1, effect="dose") % % @ %% \subsection{Additive model} %% \label{sec:additive-model} %% Returning to the \verb|ToothGrowth| data, orange juice and ascorbic %% acid are just two of many ways of supplying vitamin C (citrus and lime %% juice would be two alternatives). Here one can therefore argue, that %% it would make sense to estimate the the effect for each dose for an %% ``average vitamin C source'': %% << >>= %% LSmeans(tooth1, effect="dose") %% @ % which is the same as % <>= % L <- LE_matrix(tooth1, effect="dose") % le <- linest(tooth1, L=L) % coef(le) % @ % %% \subsection{Interaction model} % %% \label{sec:interaction-model} % For a model with interactions, the LSmeans are % << >>= % LSmeans(tooth2, effect="dose") % @ % In this case, the LE--matrix is % << >>= % L <- LE_matrix(tooth2, effect="dose") % t(L) % @ %% \usepackage[inline,nomargin,draft]{fixme} %% \newcommand\FXInline[2]{\textbf{\color{blue}#1}: %% \emph{\color{blue} - #2}} %% \usepackage[cm]{fullpage} doBy/inst/doc/vtools.html0000644000176200001440000001467115030320366015114 0ustar liggesusers Working with vtools: Flexible Variable Selection

Working with vtools: Flexible Variable Selection

Søren Højsgaard

library(doBy)
library(rlang)
data(CO2)

Overview

The v* functions in the doBy package allow for flexible and consistent handling of variable input. These tools support unquoted names, character vectors, and formulas.

The main functions are:

  • vparse() – extract variable names from flexible input
  • v() – shorthand for vparse()
  • vselect() – select columns from a data frame using flexible input
  • vcheck() – verify that variables exist in a data frame
  • vmap() – apply a function to each parsed variable name
  • vrename() – rename variables in a data frame

Examples

vparse()

vparse(Treatment, Type)
vparse(c("Treatment", "Type"))
vparse(~Treatment + Type)

v() – shorthand for vparse()

v(Treatment, Type)
v(~Treatment + Type)
v(c("Treatment", "Type"))

vselect()

vselect(CO2, Treatment, Type)
vselect(CO2, ~Treatment + Type)

vcheck()

vcheck(CO2, Treatment)
vcheck(CO2, ~Treatment + Type)

vmap()

vmap(~Treatment + Type, toupper)

vrename()

vrename(CO2, c(Treatment = "Trt", Type = "Group"))

Summary

The v* tools simplify working with variable names and selection in data frames, making it easier to write functions that accept flexible input formats.

doBy/inst/doc/section_fun.R0000644000176200001440000000632115030320366015330 0ustar liggesusers## ----include = FALSE-------------------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) options("digits"=3) library(doBy) library(boot) #devtools::load_all() ## --------------------------------------------------------------------------------------- fun <- function(a, b, c=4, d=9){ a + b + c + d } ## --------------------------------------------------------------------------------------- fun_def <- section_fun(fun, list(b=7, d=10)) fun_def fun_body <- section_fun(fun, list(b=7, d=10), method="sub") fun_body fun_env <- section_fun(fun, list(b=7, d=10), method = "env") fun_env ## --------------------------------------------------------------------------------------- get_section(fun_env) ## same as: attr(fun_env, "arg_env")$args get_fun(fun_env) ## same as: environment(fun_env)$fun ## --------------------------------------------------------------------------------------- fun(a=10, b=7, c=5, d=10) fun_def(a=10, c=5) fun_body(a=10, c=5) fun_env(a=10, c=5) ## --------------------------------------------------------------------------------------- n <- 4 toeplitz(1:n) ## --------------------------------------------------------------------------------------- inv_toep <- function(n) { solve(toeplitz(1:n)) } inv_toep(4) ## --------------------------------------------------------------------------------------- library(microbenchmark) microbenchmark( inv_toep(4), inv_toep(8), inv_toep(16), inv_toep(32), inv_toep(64), times=5 ) ## --------------------------------------------------------------------------------------- n.vec <- c(3, 4, 5) fun_list <- lapply(n.vec, function(ni){ section_fun(inv_toep, list(n=ni))} ) ## --------------------------------------------------------------------------------------- fun_list[[1]] fun_list[[1]]() ## --------------------------------------------------------------------------------------- bquote_list <- function(fnlist){ lapply(fnlist, function(g) { bquote(.(g)()) } ) } ## --------------------------------------------------------------------------------------- bq_fun_list <- bquote_list(fun_list) bq_fun_list[[1]] ## Evaluate one: eval(bq_fun_list[[1]]) ## Evaluate all: ## lapply(bq_fun_list, eval) ## --------------------------------------------------------------------------------------- names(bq_fun_list) <- n.vec microbenchmark( list = bq_fun_list, times = 5 ) ## --------------------------------------------------------------------------------------- n.vec <- seq(20, 80, by=20) fun_def <- lapply(n.vec, function(ni){ section_fun(inv_toep, list(n=ni), method="def")} ) fun_body <- lapply(n.vec, function(ni){ section_fun(inv_toep, list(n=ni), method="sub")} ) fun_env <- lapply(n.vec, function(ni){ section_fun(inv_toep, list(n=ni), method="env")} ) bq_fun_list <- bquote_list(c(fun_def, fun_body, fun_env)) names(bq_fun_list) <- paste0(rep(c("def", "body", "env"), each=length(n.vec)), rep(n.vec, 3)) mb <- microbenchmark( list = bq_fun_list, times = 2 ) doBy/inst/doc/doby.R0000644000176200001440000001465715030320362013760 0ustar liggesusers## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) options("digits"=3) library(doBy) library(boot) #devtools::load_all() ## ----------------------------------------------------------------------------- library(doBy) ## ----------------------------------------------------------------------------- head(mtcars) tail(mtcars) ## ----------------------------------------------------------------------------- myfun1 <- function(x){ c(m=mean(x), s=sd(x)) } summaryBy(cbind(mpg, cyl, lh=log(hp)) ~ vs, data=mtcars, FUN=myfun1) ## ----------------------------------------------------------------------------- summaryBy(mpg ~ vs, data=mtcars, FUN=mean) ## ----------------------------------------------------------------------------- summaryBy(list(c("mpg", "cyl"), "vs"), data=mtcars, FUN=myfun1) ## ----------------------------------------------------------------------------- mtcars |> summary_by(cbind(mpg, cyl, lh=log(hp)) ~ vs, FUN=myfun1) ## ----------------------------------------------------------------------------- x1 <- orderBy(~ gear + carb, data=mtcars) head(x1, 4) tail(x1, 4) ## ----------------------------------------------------------------------------- x2 <- orderBy(~ -gear + carb, data=mtcars) ## ----------------------------------------------------------------------------- x3 <- orderBy(c("gear", "carb"), data=mtcars) x4 <- orderBy(c("-gear", "carb"), data=mtcars) x5 <- mtcars |> order_by(c("gear", "carb")) x6 <- mtcars |> order_by(~ -gear + carb) ## ----------------------------------------------------------------------------- x <- splitBy(~ Month, data=airquality) x <- splitBy(~ vs, data=mtcars) lapply(x, head, 4) attributes(x) ## ----------------------------------------------------------------------------- splitBy("vs", data=mtcars) mtcars |> split_by(~ vs) ## ----------------------------------------------------------------------------- x <- subsetBy(~am, subset=mpg > mean(mpg), data=mtcars) head(x) ## ----------------------------------------------------------------------------- x <- subsetBy("am", subset=mpg > mean(mpg), data=mtcars) x <- mtcars |> subset_by("vs", subset=mpg > mean(mpg)) x <- mtcars |> subset_by(~vs, subset=mpg > mean(mpg)) ## ----------------------------------------------------------------------------- head(x) x <- transformBy(~vs, data=mtcars, min.mpg=min(mpg), max.mpg=max(mpg)) head(x) ## ----------------------------------------------------------------------------- x <- transformBy("vs", data=mtcars, min.mpg=min(mpg), max.mpg=max(mpg)) x <- mtcars |> transform_by("vs", min.mpg=min(mpg), max.mpg=max(mpg)) ## ----------------------------------------------------------------------------- lapplyBy(~vs, data=mtcars, FUN=function(d) lm(mpg~cyl, data=d) |> summary() |> coef()) ## ----------------------------------------------------------------------------- x <- c(1, 1, 1, 2, 2, 2, 1, 1, 1, 3) firstobs(x) lastobs(x) ## ----------------------------------------------------------------------------- firstobs(~vs, data=mtcars) lastobs(~vs, data=mtcars) ## ----------------------------------------------------------------------------- x <- c(1:4, 0:5, 11, NA, NA) which.maxn(x, 3) which.minn(x, 5) ## ----------------------------------------------------------------------------- x <- c(1, 1, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 1) subSeq(x) subSeq(x, item=1) subSeq(letters[x]) subSeq(letters[x], item="a") ## ----------------------------------------------------------------------------- x <- c("dec", "jan", "feb", "mar", "apr", "may") src1 <- list(c("dec", "jan", "feb"), c("mar", "apr", "may")) tgt1 <- list("winter", "spring") recodeVar(x, src=src1, tgt=tgt1) ## ----------------------------------------------------------------------------- head(renameCol(mtcars, c("vs", "mpg"), c("vs_", "mpg_"))) ## ----------------------------------------------------------------------------- yvar <- c(0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0) ## ----------------------------------------------------------------------------- tvar <- seq_along(yvar) + c(0.1, 0.2) ## ----------------------------------------------------------------------------- tse <- timeSinceEvent(yvar, tvar) tse ## ----------------------------------------------------------------------------- plot(sign.tse ~ tvar, data=tse, type="b") grid() rug(tse$tvar[tse$yvar == 1], col="blue",lwd=4) points(scale(tse$run), col=tse$run, lwd=2) lines(abs.tse + .2 ~ tvar, data=tse, type="b",col=3) ## ----------------------------------------------------------------------------- plot(tae ~ tvar, data=tse, ylim=c(-6,6), type="b") grid() lines(tbe ~ tvar, data=tse, type="b", col="red") rug(tse$tvar[tse$yvar==1], col="blue", lwd=4) lines(run ~ tvar, data=tse, col="cyan", lwd=2) ## ----------------------------------------------------------------------------- plot(ewin ~ tvar, data=tse, ylim=c(1, 4)) rug(tse$tvar[tse$yvar==1], col="blue", lwd=4) grid() lines(run ~ tvar, data=tse, col="red") ## ----------------------------------------------------------------------------- tse$tvar[tse$abs <= 1] ## ----------------------------------------------------------------------------- lynx <- as.numeric(lynx) tvar <- 1821:1934 plot(tvar, lynx, type="l") ## ----------------------------------------------------------------------------- yyy <- lynx > mean(lynx) head(yyy) sss <- subSeq(yyy, TRUE) sss ## ----------------------------------------------------------------------------- plot(tvar, lynx, type="l") rug(tvar[sss$midpoint], col="blue", lwd=4) ## ----------------------------------------------------------------------------- yvar <- rep(0, length(lynx)) yvar[sss$midpoint] <- 1 str(yvar) ## ----------------------------------------------------------------------------- tse <- timeSinceEvent(yvar,tvar) head(tse, 20) ## ----------------------------------------------------------------------------- len1 <- tapply(tse$ewin, tse$ewin, length) len2 <- tapply(tse$run, tse$run, length) c(median(len1), median(len2), mean(len1), mean(len2)) ## ----------------------------------------------------------------------------- tse$lynx <- lynx tse2 <- na.omit(tse) plot(lynx ~ tae, data=tse2) ## ----------------------------------------------------------------------------- plot(tvar, lynx, type="l", lty=2) mm <- lm(lynx ~ tae + I(tae^2) + I(tae^3), data=tse2) lines(fitted(mm) ~ tvar, data=tse2, col="red") doBy/inst/doc/section_fun.html0000644000176200001440000003052715030320366016100 0ustar liggesusers Sectioning a function

Sectioning a function

Søren Højsgaard

1 Section a functions domain: with section_fun()

The function section_fun is used to create a new function that is a section of the original function. The section is defined by fixing some of the arguments of the original function. The new function is defined on the remaining arguments.

For example, let \(f(x,y)=x+y\). Then \(f_x(y)=f(10, y)\) is a section of \(f\) to be a function of \(y\) alone.

More generally, let \(E\) be a subset of the cartesian product \(X \times Y\) where \(X\) and \(Y\) are some sets. Consider a function \(f(x,y)\) defined on \(E\). Then for any \(x \in X\), the section of \(E\) defined by \(x\) (denoted \(E_x\)) is the set of \(y\)’s in \(Y\) such that \((x, y)\) is in \(E\), i.e. $$ E_x = { y \in Y | (x,y) \in E} $$

Correspondingly, the section of \(f(x,y)\) defined by \(x\) is the function \(f_x\) defined on \(E_x\) given by \(f_x(y)=f(x,y)\).

2 The section_fun function in doBy

The function section_fun is used to create a new function that is a section of the original function. The section is defined by fixing some of the arguments of the original function. The new function is defined on the remaining arguments. There are the following approaches:

  1. insert the section values as default values in the function definition (default),
  2. insert the section values in the function body,
  3. store the section values in an auxillary environment.

Consider this function:

fun  <- function(a, b, c=4, d=9){
    a + b + c + d
}

fun_def <- section_fun(fun, list(b=7, d=10))
fun_def
#> function (a, c = 4, b = 7, d = 10) 
#> {
#>     a + b + c + d
#> }
fun_body <- section_fun(fun, list(b=7, d=10), method="sub")
fun_body
#> function (a, c = 4) 
#> {
#>     b = 7
#>     d = 10
#>     a + b + c + d
#> }
fun_env <- section_fun(fun, list(b=7, d=10), method = "env")
fun_env
#> function (a, c = 4) 
#> {
#>     . <- "use get_section(function_name) to see section"
#>     . <- "use get_fun(function_name) to see original function"
#>     args <- arg_getter()
#>     do.call(fun, args)
#> }
#> <environment: 0x5869dd137e08>

In the last case, we can see the section and the original function definition as:

get_section(fun_env) 
#> $b
#> [1] 7
#> 
#> $d
#> [1] 10
## same as: attr(fun_env, "arg_env")$args 
get_fun(fun_env) 
#> <srcref: file "" chars 1:9 to 3:1>
## same as: environment(fun_env)$fun

We get:

fun(a=10, b=7, c=5, d=10)
#> [1] 32
fun_def(a=10, c=5)
#> [1] 32
fun_body(a=10, c=5)
#> [1] 32
fun_env(a=10, c=5)
#> [1] 32

3 Example: benchmarking

Consider a simple task: Creating and inverting Toeplitz matrices for increasing dimensions:

n <- 4
toeplitz(1:n)
#>      [,1] [,2] [,3] [,4]
#> [1,]    1    2    3    4
#> [2,]    2    1    2    3
#> [3,]    3    2    1    2
#> [4,]    4    3    2    1

An implementation is

inv_toep <- function(n) {
    solve(toeplitz(1:n))
}
inv_toep(4)
#>      [,1] [,2] [,3] [,4]
#> [1,] -0.4  0.5  0.0  0.1
#> [2,]  0.5 -1.0  0.5  0.0
#> [3,]  0.0  0.5 -1.0  0.5
#> [4,]  0.1  0.0  0.5 -0.4

We can benchmark timing for different values of \(n\) as

library(microbenchmark)
microbenchmark(
    inv_toep(4), inv_toep(8), inv_toep(16),
    inv_toep(32), inv_toep(64),
    times=5
)
#> Unit: microseconds
#>          expr   min    lq  mean median    uq    max neval cld
#>   inv_toep(4)  13.7  14.0  14.5   14.3  15.0   15.4     5   a
#>   inv_toep(8)  16.4  16.6  17.3   16.9  17.9   18.9     5   a
#>  inv_toep(16)  22.9  23.0  26.8   23.8  28.2   35.9     5   a
#>  inv_toep(32)  49.6  50.8 385.9   51.1  52.6 1725.6     5   a
#>  inv_toep(64) 156.5 156.8 162.3  158.9 169.2  170.0     5   a

However, it is tedious (and hence error prone) to write these function calls.

A programmatic approach using \code{section_fun} is as follows: First create a list of sectioned functions:

n.vec  <- c(3, 4, 5)
fun_list <- lapply(n.vec,
                  function(ni){
                      section_fun(inv_toep, list(n=ni))}
                  )

We can inspect and evaluate each / all functions as:

fun_list[[1]]
#> function (n = 3) 
#> {
#>     solve(toeplitz(1:n))
#> }
fun_list[[1]]()
#>        [,1] [,2]   [,3]
#> [1,] -0.375  0.5  0.125
#> [2,]  0.500 -1.0  0.500
#> [3,]  0.125  0.5 -0.375

To use the list of functions in connection with microbenchmark we bquote all functions using

bquote_list <- function(fnlist){
    lapply(fnlist, function(g) {
        bquote(.(g)())
    }
    )
}

We get:

bq_fun_list <- bquote_list(fun_list)
bq_fun_list[[1]]
#> (function (n = 3) 
#> {
#>     solve(toeplitz(1:n))
#> })()
## Evaluate one:
eval(bq_fun_list[[1]])
#>        [,1] [,2]   [,3]
#> [1,] -0.375  0.5  0.125
#> [2,]  0.500 -1.0  0.500
#> [3,]  0.125  0.5 -0.375
## Evaluate all:
## lapply(bq_fun_list, eval)

To use microbenchmark we must name the elements of the list:

names(bq_fun_list) <- n.vec
microbenchmark(
  list  = bq_fun_list,
  times = 5
)
#> Unit: microseconds
#>  expr  min   lq mean median   uq  max neval cld
#>     3 12.9 13.0 26.0   13.2 13.4 77.2     5   a
#>     4 13.5 13.9 16.9   14.0 18.3 24.9     5   a
#>     5 13.7 13.8 14.9   14.7 15.5 17.0     5   a

Running the code below provides a benchmark of the different ways of sectioning in terms of speed.

n.vec  <- seq(20, 80, by=20)
fun_def <- lapply(n.vec,
                  function(ni){
                      section_fun(inv_toep, list(n=ni), method="def")}
                  )
fun_body <- lapply(n.vec,
                  function(ni){
                      section_fun(inv_toep, list(n=ni), method="sub")}
                  )
fun_env <- lapply(n.vec,
                  function(ni){
                      section_fun(inv_toep, list(n=ni), method="env")}
                  )

bq_fun_list <- bquote_list(c(fun_def, fun_body, fun_env))
names(bq_fun_list) <- paste0(rep(c("def", "body", "env"), each=length(n.vec)), rep(n.vec, 3))

mb <- microbenchmark(
  list  = bq_fun_list,
  times = 2
)
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The section is defined by fixing some of the arguments of the original function. The new function is defined on the remaining arguments. For example, let $f(x,y)=x+y$. Then $f_x(y)=f(10, y)$ is a section of $f$ to be a function of $y$ alone. More generally, let $E$ be a subset of the cartesian product $X \times Y$ where $X$ and $Y$ are some sets. Consider a function $f(x,y)$ defined on $E$. Then for any $x \in X$, the section of $E$ defined by $x$ (denoted $E_x$) is the set of $y$'s in $Y$ such that $(x, y)$ is in $E$, i.e. $$ E_x = \{ y \in Y | (x,y) \in E\} $$ Correspondingly, the section of $f(x,y)$ defined by $x$ is the function $f_x$ defined on $E_x$ given by $f_x(y)=f(x,y)$. ## The `section_fun` function in `doBy` The function `section_fun` is used to create a new function that is a section of the original function. The section is defined by fixing some of the arguments of the original function. The new function is defined on the remaining arguments. There are the following approaches: 1) insert the section values as default values in the function definition (default), 2) insert the section values in the function body, 3) store the section values in an auxillary environment. Consider this function: ```{r} fun <- function(a, b, c=4, d=9){ a + b + c + d } ``` ```{r} fun_def <- section_fun(fun, list(b=7, d=10)) fun_def fun_body <- section_fun(fun, list(b=7, d=10), method="sub") fun_body fun_env <- section_fun(fun, list(b=7, d=10), method = "env") fun_env ``` In the last case, we can see the section and the original function definition as: ```{r} get_section(fun_env) ## same as: attr(fun_env, "arg_env")$args get_fun(fun_env) ## same as: environment(fun_env)$fun ``` We get: ```{r} fun(a=10, b=7, c=5, d=10) fun_def(a=10, c=5) fun_body(a=10, c=5) fun_env(a=10, c=5) ``` ## Example: benchmarking Consider a simple task: Creating and inverting Toeplitz matrices for increasing dimensions: ```{r} n <- 4 toeplitz(1:n) ``` An implementation is ```{r} inv_toep <- function(n) { solve(toeplitz(1:n)) } inv_toep(4) ``` We can benchmark timing for different values of $n$ as ```{r} library(microbenchmark) microbenchmark( inv_toep(4), inv_toep(8), inv_toep(16), inv_toep(32), inv_toep(64), times=5 ) ``` However, it is tedious (and hence error prone) to write these function calls. A programmatic approach using \code{section\_fun} is as follows: First create a list of sectioned functions: ```{r} n.vec <- c(3, 4, 5) fun_list <- lapply(n.vec, function(ni){ section_fun(inv_toep, list(n=ni))} ) ``` We can inspect and evaluate each / all functions as: ```{r} fun_list[[1]] fun_list[[1]]() ``` To use the list of functions in connection with microbenchmark we bquote all functions using ```{r} bquote_list <- function(fnlist){ lapply(fnlist, function(g) { bquote(.(g)()) } ) } ``` We get: ```{r} bq_fun_list <- bquote_list(fun_list) bq_fun_list[[1]] ## Evaluate one: eval(bq_fun_list[[1]]) ## Evaluate all: ## lapply(bq_fun_list, eval) ``` To use microbenchmark we must name the elements of the list: ```{r} names(bq_fun_list) <- n.vec microbenchmark( list = bq_fun_list, times = 5 ) ``` Running the code below provides a benchmark of the different ways of sectioning in terms of speed. ```{r} n.vec <- seq(20, 80, by=20) fun_def <- lapply(n.vec, function(ni){ section_fun(inv_toep, list(n=ni), method="def")} ) fun_body <- lapply(n.vec, function(ni){ section_fun(inv_toep, list(n=ni), method="sub")} ) fun_env <- lapply(n.vec, function(ni){ section_fun(inv_toep, list(n=ni), method="env")} ) bq_fun_list <- bquote_list(c(fun_def, fun_body, fun_env)) names(bq_fun_list) <- paste0(rep(c("def", "body", "env"), each=length(n.vec)), rep(n.vec, 3)) mb <- microbenchmark( list = bq_fun_list, times = 2 ) ``` doBy/inst/doc/doby.Rmd0000644000176200001440000005054415030067407014304 0ustar liggesusers--- title: "doby: Groupwise computations and miscellaneous utilities" author: "Søren Højsgaard" output: html_document: toc: true number_sections: true vignette: > %\VignetteIndexEntry{doby: Groupwise computations and miscellaneous utilities} %\VignettePackage{doBy} %\VignetteEngine{knitr::knitr} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) options("digits"=3) library(doBy) library(boot) #devtools::load_all() ``` The \doby{} package contains a variety of utility functions. This working document describes some of these functions. The package originally grew out of a need to calculate groupwise summary statistics (much in the spirit of \code{PROC SUMMARY} of the \proglang{SAS} system), but today the package contains many different utilities. ```{r} library(doBy) ``` \section{Data used for illustration} \label{sec:co2data} The description of the \code{doBy} package is based on the \code{mtcars} dataset. ```{r} head(mtcars) tail(mtcars) ``` # Groupwise computations ## summaryBy() and summary_by() \label{sec:summaryBy} The \summaryby{} function is used for calculating quantities like ``the mean and variance of numerical variables $x$ and $y$ for each combination of two factors $A$ and $B$''. Notice: A functionality similar to \summaryby\ is provided by \code{aggregate()} from base \R. ```{r} myfun1 <- function(x){ c(m=mean(x), s=sd(x)) } summaryBy(cbind(mpg, cyl, lh=log(hp)) ~ vs, data=mtcars, FUN=myfun1) ``` A simpler call is ```{r} summaryBy(mpg ~ vs, data=mtcars, FUN=mean) ``` Instead of formula we may specify a list containing the left hand side and the right hand side of a formula\footnote{This is a feature of \summaryby\ and it does not work with \code{aggregate}.} but that is possible only for variables already in the dataframe: ```{r} summaryBy(list(c("mpg", "cyl"), "vs"), data=mtcars, FUN=myfun1) ``` Inspired by the \pkg{dplyr} package, there is a \verb|summary_by| function which does the samme as \summaryby{} but with the data argument being the first so that one may write ```{r} mtcars |> summary_by(cbind(mpg, cyl, lh=log(hp)) ~ vs, FUN=myfun1) ``` ## orderBy() and order_by() Ordering (or sorting) a data frame is possible with the \code{orderBy} function. For example, we order the rows according to \code{gear} and \code{carb} (within \code{gear}): ```{r} x1 <- orderBy(~ gear + carb, data=mtcars) head(x1, 4) tail(x1, 4) ``` If we want the ordering to be by decreasing values of one of the variables, we can do ```{r} x2 <- orderBy(~ -gear + carb, data=mtcars) ``` Alternative forms are: ```{r} x3 <- orderBy(c("gear", "carb"), data=mtcars) x4 <- orderBy(c("-gear", "carb"), data=mtcars) x5 <- mtcars |> order_by(c("gear", "carb")) x6 <- mtcars |> order_by(~ -gear + carb) ``` ## splitBy() and split_by() Suppose we want to split the \code{airquality} data into a list of dataframes, e.g.\ one dataframe for each month. This can be achieved by: ```{r} x <- splitBy(~ Month, data=airquality) x <- splitBy(~ vs, data=mtcars) lapply(x, head, 4) attributes(x) ``` Alternative forms are: ```{r} splitBy("vs", data=mtcars) mtcars |> split_by(~ vs) ``` ## subsetBy() and subset_by() Suppose we want to select those rows within each month for which the the wind speed is larger than the mean wind speed (within the month). This is achieved by: ```{r} x <- subsetBy(~am, subset=mpg > mean(mpg), data=mtcars) head(x) ``` Note that the statement \code{Wind > mean(Wind)} is evaluated within each month. Alternative forms are ```{r} x <- subsetBy("am", subset=mpg > mean(mpg), data=mtcars) x <- mtcars |> subset_by("vs", subset=mpg > mean(mpg)) x <- mtcars |> subset_by(~vs, subset=mpg > mean(mpg)) ``` ## transformBy() and transform_by() The \code{transformBy} function is analogous to the \code{transform} function except that it works within groups. For example: ```{r} head(x) x <- transformBy(~vs, data=mtcars, min.mpg=min(mpg), max.mpg=max(mpg)) head(x) ``` Alternative forms: ```{r} x <- transformBy("vs", data=mtcars, min.mpg=min(mpg), max.mpg=max(mpg)) x <- mtcars |> transform_by("vs", min.mpg=min(mpg), max.mpg=max(mpg)) ``` ## lapplyBy() and lapply_by() This \code{lapplyBy} function is a wrapper for first splitting data into a list according to the formula (using splitBy) and then applying a function to each element of the list (using lapply). ```{r} lapplyBy(~vs, data=mtcars, FUN=function(d) lm(mpg~cyl, data=d) |> summary() |> coef()) ``` # Miscellaneous utilities ## firstobs() and lastobs() To obtain the indices of the first/last occurences of an item in a vector do: ```{r} x <- c(1, 1, 1, 2, 2, 2, 1, 1, 1, 3) firstobs(x) lastobs(x) ``` The same can be done on variables in a data frame, e.g. ```{r} firstobs(~vs, data=mtcars) lastobs(~vs, data=mtcars) ``` \subsection{The \code{which.maxn()} and \code{which.minn()} functions} \label{sec:whichmaxn} The location of the $n$ largest / smallest entries in a numeric vector can be obtained with ```{r} x <- c(1:4, 0:5, 11, NA, NA) which.maxn(x, 3) which.minn(x, 5) ``` ## Subsequences - subSeq() Find (sub) sequences in a vector: ```{r} x <- c(1, 1, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 1) subSeq(x) subSeq(x, item=1) subSeq(letters[x]) subSeq(letters[x], item="a") ``` ## Recoding values of a vector - recodeVar() ```{r} x <- c("dec", "jan", "feb", "mar", "apr", "may") src1 <- list(c("dec", "jan", "feb"), c("mar", "apr", "may")) tgt1 <- list("winter", "spring") recodeVar(x, src=src1, tgt=tgt1) ``` ## Renaming columns of a dataframe or matrix - renameCol() ```{r} head(renameCol(mtcars, c("vs", "mpg"), c("vs_", "mpg_"))) ``` ## Time since an event - timeSinceEvent() Consider the vector ```{r} yvar <- c(0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0) ``` Imagine that "1" indicates an event of some kind which takes place at a certain time point. By default time points are assumed equidistant but for illustration we define time time variable ```{r} tvar <- seq_along(yvar) + c(0.1, 0.2) ``` Now we find time since event as ```{r} tse <- timeSinceEvent(yvar, tvar) tse ``` The output reads as follows: \begin{itemize} \item \verb"abs.tse": Absolute time since (nearest) event. \item \verb"sign.tse": Signed time since (nearest) event. \item \verb"ewin": Event window: Gives a symmetric window around each event. \item \verb"run": The value of \verb"run" is set to $1$ when the first event occurs and is increased by $1$ at each subsequent event. \item \verb"tae": Time after event. \item \verb"tbe": Time before event. \end{itemize} ```{r} plot(sign.tse ~ tvar, data=tse, type="b") grid() rug(tse$tvar[tse$yvar == 1], col="blue",lwd=4) points(scale(tse$run), col=tse$run, lwd=2) lines(abs.tse + .2 ~ tvar, data=tse, type="b",col=3) ``` ```{r} plot(tae ~ tvar, data=tse, ylim=c(-6,6), type="b") grid() lines(tbe ~ tvar, data=tse, type="b", col="red") rug(tse$tvar[tse$yvar==1], col="blue", lwd=4) lines(run ~ tvar, data=tse, col="cyan", lwd=2) ``` ```{r} plot(ewin ~ tvar, data=tse, ylim=c(1, 4)) rug(tse$tvar[tse$yvar==1], col="blue", lwd=4) grid() lines(run ~ tvar, data=tse, col="red") ``` We may now f ind times for which time since an event is at most 1 as ```{r} tse$tvar[tse$abs <= 1] ``` ## Example: Using subSeq() and timeSinceEvent() Consider the \verb|lynx| data: ```{r} lynx <- as.numeric(lynx) tvar <- 1821:1934 plot(tvar, lynx, type="l") ``` Suppose we want to estimate the cycle lengths. One way of doing this is as follows: ```{r} yyy <- lynx > mean(lynx) head(yyy) sss <- subSeq(yyy, TRUE) sss ``` ```{r} plot(tvar, lynx, type="l") rug(tvar[sss$midpoint], col="blue", lwd=4) ``` Create the "event vector" ```{r} yvar <- rep(0, length(lynx)) yvar[sss$midpoint] <- 1 str(yvar) ``` ```{r} tse <- timeSinceEvent(yvar,tvar) head(tse, 20) ``` We get two different (not that different) estimates of period lengths: ```{r} len1 <- tapply(tse$ewin, tse$ewin, length) len2 <- tapply(tse$run, tse$run, length) c(median(len1), median(len2), mean(len1), mean(len2)) ``` We can overlay the cycles as: ```{r} tse$lynx <- lynx tse2 <- na.omit(tse) plot(lynx ~ tae, data=tse2) ``` ```{r} plot(tvar, lynx, type="l", lty=2) mm <- lm(lynx ~ tae + I(tae^2) + I(tae^3), data=tse2) lines(fitted(mm) ~ tvar, data=tse2, col="red") ``` \section{Acknowledgements} \label{discussion} Credit is due to Dennis Chabot, Gabor Grothendieck, Paul Murrell and Jim Robison-Cox for reporting various bugs and making various suggestions to the functionality in the \doby{} package. doBy/inst/doc/linest_lsmeans.R0000644000176200001440000001331615030320363016033 0ustar liggesusers## ----echo=FALSE--------------------------------------------------------------- require( doBy ) prettyVersion <- packageDescription("doBy")$Version prettyDate <- format(Sys.Date()) ## ----r setup, echo=FALSE---------------------------------------------------------------- knitr::opts_chunk$set(prompt=TRUE) library(knitr) if (!dir.exists("figures")) dir.create("figures") opts_chunk$set( tidy=FALSE,fig.path='figures/LSmeans', fig.height=3.5 ) oopt <- options() options("digits"=4, "width"=90, "prompt"="> ", "continue"=" ") options(useFancyQuotes="UTF-8") library(ggplot2) ## ----include=FALSE---------------------------------------------------------------------- library(knitr) opts_chunk$set( concordance=TRUE ) ## ----eval=F----------------------------------------------------------------------------- # lm( y ~ treat + block + year) ## ----eval=F----------------------------------------------------------------------------- # library(lme4) # lmer( y ~ treat + (1|block) + (1|year)) ## --------------------------------------------------------------------------------------- library(doBy) set.seed(141164) dd <- expand.grid(A=factor(1:3), B=factor(1:3), C=factor(1:2)) dd$y <- rnorm(nrow(dd)) dd$x <- rnorm(nrow(dd))^2 dd$z <- rnorm(nrow(dd)) head(dd,10) ## --------------------------------------------------------------------------------------- mm <- lm(y ~ A + B + C + x, data=dd) coef(mm) ## --------------------------------------------------------------------------------------- w <- c(1, 0, 0, 1/3, 1/3, 1/2, 1.242) sum(coef(mm) * w) ## --------------------------------------------------------------------------------------- L <- matrix(c(1, 0, 0, 1/3, 1/3, 1/2, 1.242, 1, 1, 0, 1/3, 1/3, 1/2, 1.242, 1, 0, 1, 1/3, 1/3, 1/2, 1.242), nr=3, byrow=TRUE) L %*% coef(mm) ## --------------------------------------------------------------------------------------- L <- LE_matrix(mm, effect='A') L ## --------------------------------------------------------------------------------------- linest(mm, L=L) LSM <- LSmeans(mm, effect='A') LSM ## --------------------------------------------------------------------------------------- summary(LSM) ## --------------------------------------------------------------------------------------- ## 1) a vector of characters and LE_matrix(mm, effect= c("A", "C")) ## 2) a right hand sided formula: ## LE_matrix(mm, effect= ~ A + C) ## --------------------------------------------------------------------------------------- LE_matrix(mm, at=list(A=1, x=2)) ## --------------------------------------------------------------------------------------- LE_matrix(mm, effect= ~ A + C, at=list(A=1)) ## --------------------------------------------------------------------------------------- LE_matrix(mm) LSmeans(mm) ## --------------------------------------------------------------------------------------- mm2 <- lm(y ~ A + B + C + log(x), data=dd) ## LSmeans(mm2, effect=~A) ## Will fail mm2 <- lm(y ~ A + B + C + log.x, data=transform(dd, log.x=log(x))) LSmeans(mm2, effect=~A) LSmeans(mm2, effect=~A)$L ## --------------------------------------------------------------------------------------- mm2 <- lm(y ~ A + B + C + x + I(x^2), data=dd) LSmeans(mm2, effect=~A) LSmeans(mm2, effect=~A)$L ## --------------------------------------------------------------------------------------- mm2 <- lm(y ~ A + B + C + x + x2, data=transform(dd, x2=x^2)) LSmeans(mm2, effect=~A) LSmeans(mm2, effect=~A)$L ## --------------------------------------------------------------------------------------- data(CO2) CO2 <- subset(CO2, conc < 300) ## OK CO2.bal <- CO2 rownames(CO2.bal) <- NULL ## --------------------------------------------------------------------------------------- CO2.ubal <- CO2.bal[-c(1, 5, 12, 17, 18, 19, 20, 28),] CO2.ubal |> head() xtabs(~Type + Treatment + conc, data=CO2.ubal) |> ftable(row.vars = "Type") ## --------------------------------------------------------------------------------------- library(broom) form.add <- uptake ~ conc + Treatment + Type form.int <- uptake ~ conc * Treatment + Type fm1.bal <- lm(form.add, data=CO2.bal) fm1.ubal <- lm(form.add, data=CO2.ubal) ## --------------------------------------------------------------------------------------- LSmeans(fm1.bal, effect=~Type+Treatment, at=list(conc=100)) LSmeans(fm1.ubal, effect=~Type+Treatment, at=list(conc=100)) ## --------------------------------------------------------------------------------------- LSmeans(fm1.bal, effect=~Treatment) LSmeans(fm1.ubal, effect=~Treatment) ## --------------------------------------------------------------------------------------- ## Fit model without Type fm2.bal <- update(fm1.bal, .~. - Type) fm2.ubal <- update(fm1.ubal, .~. - Type) LSmeans(fm2.bal, effect=~Treatment) LSmeans(fm2.ubal, effect=~Treatment) ## --------------------------------------------------------------------------------------- fm.glm <- glm(form.add, family=Gamma, data=CO2.ubal) LSmeans(fm.glm, effect=~Treatment, type="link") ## --------------------------------------------------------------------------------------- library(geepack) fm.gee <- geeglm(uptake ~ conc + Treatment + Type, id=Plant, family=Gamma, data=CO2.ubal) LSmeans(fm.gee, effect=~Treatment) ## --------------------------------------------------------------------------------------- library(lme4) fm.mix <- lmer(uptake ~ conc + Treatment + Type + (1|Plant), data=CO2.ubal) LSmeans(fm.mix, effect=~Treatment) ## --------------------------------------------------------------------------------------- library("multcomp") g1 <- glht(fm2.ubal, mcp(Treatment="Tukey")) tidy(g1) ## --------------------------------------------------------------------------------------- L <- g1$linfct L linest(fm2.ubal, L=L) doBy/inst/doc/doby.html0000644000176200001440000112772215030320362014522 0ustar liggesusers doby: Groupwise computations and miscellaneous utilities

doby: Groupwise computations and miscellaneous utilities

Søren Højsgaard

The \doby{} package contains a variety of utility functions. This working document describes some of these functions. The package originally grew out of a need to calculate groupwise summary statistics (much in the spirit of \code{PROC SUMMARY} of the \proglang{SAS} system), but today the package contains many different utilities.

library(doBy)

\section{Data used for illustration} \label{sec:co2data}

The description of the \code{doBy} package is based on the \code{mtcars} dataset.

head(mtcars)
#>                    mpg cyl disp  hp drat   wt qsec vs am gear carb
#> Mazda RX4         21.0   6  160 110 3.90 2.62 16.5  0  1    4    4
#> Mazda RX4 Wag     21.0   6  160 110 3.90 2.88 17.0  0  1    4    4
#> Datsun 710        22.8   4  108  93 3.85 2.32 18.6  1  1    4    1
#> Hornet 4 Drive    21.4   6  258 110 3.08 3.21 19.4  1  0    3    1
#> Hornet Sportabout 18.7   8  360 175 3.15 3.44 17.0  0  0    3    2
#> Valiant           18.1   6  225 105 2.76 3.46 20.2  1  0    3    1
tail(mtcars)
#>                 mpg cyl  disp  hp drat   wt qsec vs am gear carb
#> Porsche 914-2  26.0   4 120.3  91 4.43 2.14 16.7  0  1    5    2
#> Lotus Europa   30.4   4  95.1 113 3.77 1.51 16.9  1  1    5    2
#> Ford Pantera L 15.8   8 351.0 264 4.22 3.17 14.5  0  1    5    4
#> Ferrari Dino   19.7   6 145.0 175 3.62 2.77 15.5  0  1    5    6
#> Maserati Bora  15.0   8 301.0 335 3.54 3.57 14.6  0  1    5    8
#> Volvo 142E     21.4   4 121.0 109 4.11 2.78 18.6  1  1    4    2

1 Groupwise computations

1.1 summaryBy() and summary_by()

\label{sec:summaryBy}

The \summaryby{} function is used for calculating quantities like ``the mean and variance of numerical variables \(x\) and \(y\) for each combination of two factors \(A\) and \(B\)’’. Notice: A functionality similar to \summaryby\ is provided by \code{aggregate()} from base \R.

myfun1 <- function(x){
    c(m=mean(x), s=sd(x))
}
summaryBy(cbind(mpg, cyl, lh=log(hp)) ~ vs, 
          data=mtcars, FUN=myfun1)
#>   vs mpg.m mpg.s cyl.m cyl.s lh.m  lh.s
#> 1  0  16.6  3.86  7.44 1.149 5.20 0.330
#> 2  1  24.6  5.38  4.57 0.938 4.48 0.289

A simpler call is

summaryBy(mpg ~ vs, data=mtcars, FUN=mean)
#>   vs mpg.mean
#> 1  0     16.6
#> 2  1     24.6

Instead of formula we may specify a list containing the left hand side and the right hand side of a formula\footnote{This is a feature of \summaryby\ and it does not work with \code{aggregate}.} but that is possible only for variables already in the dataframe:

summaryBy(list(c("mpg", "cyl"), "vs"), 
          data=mtcars, FUN=myfun1)
#>   vs mpg.m mpg.s cyl.m cyl.s
#> 1  0  16.6  3.86  7.44 1.149
#> 2  1  24.6  5.38  4.57 0.938

Inspired by the \pkg{dplyr} package, there is a \verb|summary_by| function which does the samme as \summaryby{} but with the data argument being the first so that one may write

mtcars |> summary_by(cbind(mpg, cyl, lh=log(hp)) ~ vs,
                      FUN=myfun1)
#>   vs mpg.m mpg.s cyl.m cyl.s lh.m  lh.s
#> 1  0  16.6  3.86  7.44 1.149 5.20 0.330
#> 2  1  24.6  5.38  4.57 0.938 4.48 0.289

1.2 orderBy() and order_by()

Ordering (or sorting) a data frame is possible with the \code{orderBy} function. For example, we order the rows according to \code{gear} and \code{carb} (within \code{gear}):

x1 <- orderBy(~ gear + carb, data=mtcars)
head(x1, 4)
#>                    mpg cyl disp  hp drat   wt qsec vs am gear carb
#> Hornet 4 Drive    21.4   6  258 110 3.08 3.21 19.4  1  0    3    1
#> Valiant           18.1   6  225 105 2.76 3.46 20.2  1  0    3    1
#> Toyota Corona     21.5   4  120  97 3.70 2.46 20.0  1  0    3    1
#> Hornet Sportabout 18.7   8  360 175 3.15 3.44 17.0  0  0    3    2
tail(x1, 4)
#>                 mpg cyl  disp  hp drat   wt qsec vs am gear carb
#> Lotus Europa   30.4   4  95.1 113 3.77 1.51 16.9  1  1    5    2
#> Ford Pantera L 15.8   8 351.0 264 4.22 3.17 14.5  0  1    5    4
#> Ferrari Dino   19.7   6 145.0 175 3.62 2.77 15.5  0  1    5    6
#> Maserati Bora  15.0   8 301.0 335 3.54 3.57 14.6  0  1    5    8

If we want the ordering to be by decreasing values of one of the variables, we can do

x2 <- orderBy(~ -gear + carb, data=mtcars)

Alternative forms are:

x3 <- orderBy(c("gear", "carb"), data=mtcars)
x4 <- orderBy(c("-gear", "carb"), data=mtcars)
x5 <- mtcars |> order_by(c("gear", "carb"))
x6 <- mtcars |> order_by(~ -gear + carb)

1.3 splitBy() and split_by()

Suppose we want to split the \code{airquality} data into a list of dataframes, e.g.\ one dataframe for each month. This can be achieved by:

x <- splitBy(~ Month, data=airquality)
x <- splitBy(~ vs, data=mtcars)
lapply(x, head, 4)
#> $`0`
#>                    mpg cyl disp  hp drat   wt qsec am gear carb
#> Mazda RX4         21.0   6  160 110 3.90 2.62 16.5  1    4    4
#> Mazda RX4 Wag     21.0   6  160 110 3.90 2.88 17.0  1    4    4
#> Hornet Sportabout 18.7   8  360 175 3.15 3.44 17.0  0    3    2
#> Duster 360        14.3   8  360 245 3.21 3.57 15.8  0    3    4
#> 
#> $`1`
#>                 mpg cyl disp  hp drat   wt qsec am gear carb
#> Datsun 710     22.8   4  108  93 3.85 2.32 18.6  1    4    1
#> Hornet 4 Drive 21.4   6  258 110 3.08 3.21 19.4  0    3    1
#> Valiant        18.1   6  225 105 2.76 3.46 20.2  0    3    1
#> Merc 240D      24.4   4  147  62 3.69 3.19 20.0  0    4    2
attributes(x)
#> $names
#> [1] "0" "1"
#> 
#> $groupid
#>            vs
#> Mazda RX4   0
#> Datsun 710  1
#> 
#> $idxvec
#> $idxvec$`0`
#>  [1]  1  2  5  7 12 13 14 15 16 17 22 23 24 25 27 29 30 31
#> 
#> $idxvec$`1`
#>  [1]  3  4  6  8  9 10 11 18 19 20 21 26 28 32
#> 
#> 
#> $grps
#>           Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive 
#>                 "0"                 "0"                 "1"                 "1" 
#>   Hornet Sportabout             Valiant          Duster 360           Merc 240D 
#>                 "0"                 "1"                 "0"                 "1" 
#>            Merc 230            Merc 280           Merc 280C          Merc 450SE 
#>                 "1"                 "1"                 "1"                 "0" 
#>          Merc 450SL         Merc 450SLC  Cadillac Fleetwood Lincoln Continental 
#>                 "0"                 "0"                 "0"                 "0" 
#>   Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla 
#>                 "0"                 "1"                 "1"                 "1" 
#>       Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28 
#>                 "1"                 "0"                 "0"                 "0" 
#>    Pontiac Firebird           Fiat X1-9       Porsche 914-2        Lotus Europa 
#>                 "0"                 "1"                 "0"                 "1" 
#>      Ford Pantera L        Ferrari Dino       Maserati Bora          Volvo 142E 
#>                 "0"                 "0"                 "0"                 "1" 
#> 
#> $class
#> [1] "splitByData" "list"

Alternative forms are:

splitBy("vs", data=mtcars)
#>            listentry vs
#> Mazda RX4          0  0
#> Datsun 710         1  1
mtcars |> split_by(~ vs)
#>            listentry vs
#> Mazda RX4          0  0
#> Datsun 710         1  1

1.4 subsetBy() and subset_by()

Suppose we want to select those rows within each month for which the the wind speed is larger than the mean wind speed (within the month). This is achieved by:

x <- subsetBy(~am, subset=mpg > mean(mpg), data=mtcars)
head(x)
#>                      mpg cyl disp  hp drat   wt qsec vs gear carb
#> 0.Hornet 4 Drive    21.4   6  258 110 3.08 3.21 19.4  1    3    1
#> 0.Hornet Sportabout 18.7   8  360 175 3.15 3.44 17.0  0    3    2
#> 0.Valiant           18.1   6  225 105 2.76 3.46 20.2  1    3    1
#> 0.Merc 240D         24.4   4  147  62 3.69 3.19 20.0  1    4    2
#> 0.Merc 230          22.8   4  141  95 3.92 3.15 22.9  1    4    2
#> 0.Merc 280          19.2   6  168 123 3.92 3.44 18.3  1    4    4

Note that the statement \code{Wind > mean(Wind)} is evaluated within each month.

Alternative forms are

x <- subsetBy("am", subset=mpg > mean(mpg), data=mtcars)
x <- mtcars  |> subset_by("vs", subset=mpg > mean(mpg))
x <- mtcars  |> subset_by(~vs, subset=mpg > mean(mpg))

1.5 transformBy() and transform_by()

The \code{transformBy} function is analogous to the \code{transform} function except that it works within groups. For example:

head(x)
#>                      mpg cyl disp  hp drat   wt qsec am gear carb
#> 0.Mazda RX4         21.0   6  160 110 3.90 2.62 16.5  1    4    4
#> 0.Mazda RX4 Wag     21.0   6  160 110 3.90 2.88 17.0  1    4    4
#> 0.Hornet Sportabout 18.7   8  360 175 3.15 3.44 17.0  0    3    2
#> 0.Merc 450SL        17.3   8  276 180 3.07 3.73 17.6  0    3    3
#> 0.Pontiac Firebird  19.2   8  400 175 3.08 3.85 17.1  0    3    2
#> 0.Porsche 914-2     26.0   4  120  91 4.43 2.14 16.7  1    5    2
x <- transformBy(~vs, data=mtcars, 
                 min.mpg=min(mpg), max.mpg=max(mpg))
head(x)
#>    mpg cyl disp  hp drat   wt qsec am gear carb min.mpg max.mpg
#> 1 21.0   6  160 110 3.90 2.62 16.5  1    4    4    10.4      26
#> 2 21.0   6  160 110 3.90 2.88 17.0  1    4    4    10.4      26
#> 3 18.7   8  360 175 3.15 3.44 17.0  0    3    2    10.4      26
#> 4 14.3   8  360 245 3.21 3.57 15.8  0    3    4    10.4      26
#> 5 16.4   8  276 180 3.07 4.07 17.4  0    3    3    10.4      26
#> 6 17.3   8  276 180 3.07 3.73 17.6  0    3    3    10.4      26

Alternative forms:

x <- transformBy("vs", data=mtcars, 
                 min.mpg=min(mpg), max.mpg=max(mpg))
x <- mtcars |> transform_by("vs",
                             min.mpg=min(mpg), max.mpg=max(mpg))

1.6 lapplyBy() and lapply_by()

This \code{lapplyBy} function is a wrapper for first splitting data into a list according to the formula (using splitBy) and then applying a function to each element of the list (using lapply).

lapplyBy(~vs, data=mtcars,
         FUN=function(d) lm(mpg~cyl, data=d)  |> summary()  |> coef())
#> $`0`
#>             Estimate Std. Error t value Pr(>|t|)
#> (Intercept)    36.93       3.69   10.01 2.73e-08
#> cyl            -2.73       0.49   -5.56 4.27e-05
#> 
#> $`1`
#>             Estimate Std. Error t value Pr(>|t|)
#> (Intercept)     41.9       5.78    7.26  0.00001
#> cyl             -3.8       1.24   -3.07  0.00978

2 Miscellaneous utilities

2.1 firstobs() and lastobs()

To obtain the indices of the first/last occurences of an item in a vector do:

x <- c(1, 1, 1, 2, 2, 2, 1, 1, 1, 3)
firstobs(x)
#> [1]  1  4 10
lastobs(x)
#> [1]  6  9 10

The same can be done on variables in a data frame, e.g.

firstobs(~vs, data=mtcars)
#> [1] 1 3
lastobs(~vs, data=mtcars)
#> [1] 31 32

\subsection{The \code{which.maxn()} and \code{which.minn()} functions} \label{sec:whichmaxn}

The location of the \(n\) largest / smallest entries in a numeric vector can be obtained with

x <- c(1:4, 0:5, 11, NA, NA)
which.maxn(x, 3)
#> [1] 11 10  4
which.minn(x, 5)
#> [1] 5 1 6 2 7

2.2 Subsequences - subSeq()

Find (sub) sequences in a vector:

x <- c(1, 1, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 1)
subSeq(x)
#>   first last slength midpoint value
#> 1     1    2       2        2     1
#> 2     3    5       3        4     2
#> 3     6    7       2        7     1
#> 4     8   11       4       10     3
#> 5    12   14       3       13     1
subSeq(x, item=1)
#>   first last slength midpoint value
#> 1     1    2       2        2     1
#> 2     6    7       2        7     1
#> 3    12   14       3       13     1
subSeq(letters[x])
#>   first last slength midpoint value
#> 1     1    2       2        2     a
#> 2     3    5       3        4     b
#> 3     6    7       2        7     a
#> 4     8   11       4       10     c
#> 5    12   14       3       13     a
subSeq(letters[x], item="a")
#>   first last slength midpoint value
#> 1     1    2       2        2     a
#> 2     6    7       2        7     a
#> 3    12   14       3       13     a

2.3 Recoding values of a vector - recodeVar()

x <- c("dec", "jan", "feb", "mar", "apr", "may")
src1 <- list(c("dec", "jan", "feb"), c("mar", "apr", "may"))
tgt1 <- list("winter", "spring")
recodeVar(x, src=src1, tgt=tgt1)
#> [1] "winter" "winter" "winter" "spring" "spring" "spring"

2.4 Renaming columns of a dataframe or matrix - renameCol()

head(renameCol(mtcars, c("vs", "mpg"), c("vs_", "mpg_")))
#>                   mpg_ cyl disp  hp drat   wt qsec vs_ am gear carb
#> Mazda RX4         21.0   6  160 110 3.90 2.62 16.5   0  1    4    4
#> Mazda RX4 Wag     21.0   6  160 110 3.90 2.88 17.0   0  1    4    4
#> Datsun 710        22.8   4  108  93 3.85 2.32 18.6   1  1    4    1
#> Hornet 4 Drive    21.4   6  258 110 3.08 3.21 19.4   1  0    3    1
#> Hornet Sportabout 18.7   8  360 175 3.15 3.44 17.0   0  0    3    2
#> Valiant           18.1   6  225 105 2.76 3.46 20.2   1  0    3    1

2.5 Time since an event - timeSinceEvent()

Consider the vector

yvar <- c(0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0)

Imagine that “1” indicates an event of some kind which takes place at a certain time point. By default time points are assumed equidistant but for illustration we define time time variable

tvar <- seq_along(yvar) + c(0.1, 0.2)

Now we find time since event as

tse <- timeSinceEvent(yvar, tvar)
#> Warning: 'timeSinceEvent' is deprecated.
#> Use 'time_since_event' instead.
#> See help("Deprecated")
tse
#>    yvar tvar abs.tse sign.tse ewin run tae  tbe
#> 1     0  1.1     3.1     -3.1    1  NA  NA -3.1
#> 2     0  2.2     2.0     -2.0    1  NA  NA -2.0
#> 3     0  3.1     1.1     -1.1    1  NA  NA -1.1
#> 4     1  4.2     0.0      0.0    1   1 0.0  0.0
#> 5     0  5.1     0.9      0.9    1   1 0.9 -5.1
#> 6     0  6.2     2.0      2.0    1   1 2.0 -4.0
#> 7     0  7.1     2.9      2.9    1   1 2.9 -3.1
#> 8     0  8.2     2.0     -2.0    2   1 4.0 -2.0
#> 9     0  9.1     1.1     -1.1    2   1 4.9 -1.1
#> 10    1 10.2     0.0      0.0    2   2 0.0  0.0
#> 11    0 11.1     0.9      0.9    2   2 0.9 -3.1
#> 12    0 12.2     2.0      2.0    2   2 2.0 -2.0
#> 13    0 13.1     1.1     -1.1    3   2 2.9 -1.1
#> 14    1 14.2     0.0      0.0    3   3 0.0  0.0
#> 15    1 15.1     0.0      0.0    4   4 0.0  0.0
#> 16    0 16.2     1.1      1.1    4   4 1.1   NA
#> 17    0 17.1     2.0      2.0    4   4 2.0   NA
#> 18    0 18.2     3.1      3.1    4   4 3.1   NA
#> 19    0 19.1     4.0      4.0    4   4 4.0   NA
#> 20    0 20.2     5.1      5.1    4   4 5.1   NA

The output reads as follows: \begin{itemize} \item \verb"abs.tse": Absolute time since (nearest) event. \item \verb"sign.tse": Signed time since (nearest) event. \item \verb"ewin": Event window: Gives a symmetric window around each event. \item \verb"run": The value of \verb"run" is set to \(1\) when the first event occurs and is increased by \(1\) at each subsequent event. \item \verb”tae”: Time after event. \item \verb”tbe”: Time before event. \end{itemize}CG;O3`

plot(sign.tse ~ tvar, data=tse, type="b")
grid()
rug(tse$tvar[tse$yvar == 1], col="blue",lwd=4)
points(scale(tse$run), col=tse$run, lwd=2)
lines(abs.tse + .2 ~ tvar, data=tse, type="b",col=3)

plot of chunk unnamed-chunk-27

plot(tae ~ tvar, data=tse, ylim=c(-6,6), type="b")
grid()
lines(tbe ~ tvar, data=tse, type="b", col="red")
rug(tse$tvar[tse$yvar==1], col="blue", lwd=4)
lines(run ~ tvar, data=tse, col="cyan", lwd=2)

plot of chunk unnamed-chunk-28

plot(ewin ~ tvar, data=tse, ylim=c(1, 4))
rug(tse$tvar[tse$yvar==1], col="blue", lwd=4)
grid()
lines(run ~ tvar, data=tse, col="red")

plot of chunk unnamed-chunk-29

We may now f ind times for which time since an event is at most 1 as

tse$tvar[tse$abs <= 1]
#> [1]  4.2  5.1 10.2 11.1 14.2 15.1

2.6 Example: Using subSeq() and timeSinceEvent()

Consider the \verb|lynx| data:

lynx <- as.numeric(lynx)
tvar <- 1821:1934
plot(tvar, lynx, type="l")

plot of chunk unnamed-chunk-31

Suppose we want to estimate the cycle lengths. One way of doing this is as follows:

yyy <- lynx > mean(lynx)
head(yyy)
#> [1] FALSE FALSE FALSE FALSE FALSE  TRUE
sss <- subSeq(yyy, TRUE)
sss
#>    first last slength midpoint value
#> 1      6   10       5        8  TRUE
#> 2     16   19       4       18  TRUE
#> 3     27   28       2       28  TRUE
#> 4     35   38       4       37  TRUE
#> 5     44   47       4       46  TRUE
#> 6     53   55       3       54  TRUE
#> 7     63   66       4       65  TRUE
#> 8     75   76       2       76  TRUE
#> 9     83   87       5       85  TRUE
#> 10    92   96       5       94  TRUE
#> 11   104  106       3      105  TRUE
#> 12   112  114       3      113  TRUE

plot(tvar, lynx, type="l")
rug(tvar[sss$midpoint], col="blue", lwd=4)

plot of chunk unnamed-chunk-33

Create the “event vector”

yvar <- rep(0, length(lynx))
yvar[sss$midpoint] <- 1
str(yvar)
#>  num [1:114] 0 0 0 0 0 0 0 1 0 0 ...

tse <- timeSinceEvent(yvar,tvar)
#> Warning: 'timeSinceEvent' is deprecated.
#> Use 'time_since_event' instead.
#> See help("Deprecated")
head(tse, 20)
#>    yvar tvar abs.tse sign.tse ewin run tae tbe
#> 1     0 1821       7       -7    1  NA  NA  -7
#> 2     0 1822       6       -6    1  NA  NA  -6
#> 3     0 1823       5       -5    1  NA  NA  -5
#> 4     0 1824       4       -4    1  NA  NA  -4
#> 5     0 1825       3       -3    1  NA  NA  -3
#> 6     0 1826       2       -2    1  NA  NA  -2
#> 7     0 1827       1       -1    1  NA  NA  -1
#> 8     1 1828       0        0    1   1   0   0
#> 9     0 1829       1        1    1   1   1  -9
#> 10    0 1830       2        2    1   1   2  -8
#> 11    0 1831       3        3    1   1   3  -7
#> 12    0 1832       4        4    1   1   4  -6
#> 13    0 1833       5        5    1   1   5  -5
#> 14    0 1834       4       -4    2   1   6  -4
#> 15    0 1835       3       -3    2   1   7  -3
#> 16    0 1836       2       -2    2   1   8  -2
#> 17    0 1837       1       -1    2   1   9  -1
#> 18    1 1838       0        0    2   2   0   0
#> 19    0 1839       1        1    2   2   1  -9
#> 20    0 1840       2        2    2   2   2  -8

We get two different (not that different) estimates of period lengths:

len1 <- tapply(tse$ewin, tse$ewin, length)
len2 <- tapply(tse$run, tse$run, length)
c(median(len1), median(len2), mean(len1), mean(len2))
#> [1] 9.50 9.00 9.50 8.92

We can overlay the cycles as:

tse$lynx <- lynx
tse2 <- na.omit(tse)
plot(lynx ~ tae, data=tse2)

plot of chunk unnamed-chunk-37

plot(tvar, lynx, type="l", lty=2)
mm <- lm(lynx ~ tae + I(tae^2) + I(tae^3), data=tse2)
lines(fitted(mm) ~ tvar, data=tse2, col="red")

plot of chunk unnamed-chunk-38

\section{Acknowledgements} \label{discussion}

Credit is due to Dennis Chabot, Gabor Grothendieck, Paul Murrell and Jim Robison-Cox for reporting various bugs and making various suggestions to the functionality in the \doby{} package.

doBy/inst/doc/vtools.Rmd0000644000176200001440000000305615024600522014663 0ustar liggesusers--- title: "Working with vtools: Flexible Variable Selection" author: "Søren Højsgaard" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Working with vtools: Flexible Variable Selection} %\VignetteEngine{knitr::knitr} %\VignetteEncoding{UTF-8} --- ```r library(doBy) library(rlang) data(CO2) ``` ## Overview The `v*` functions in the `doBy` package allow for flexible and consistent handling of variable input. These tools support unquoted names, character vectors, and formulas. The main functions are: - `vparse()` – extract variable names from flexible input - `v()` – shorthand for `vparse()` - `vselect()` – select columns from a data frame using flexible input - `vcheck()` – verify that variables exist in a data frame - `vmap()` – apply a function to each parsed variable name - `vrename()` – rename variables in a data frame --- ## Examples ### `vparse()` ```r vparse(Treatment, Type) vparse(c("Treatment", "Type")) vparse(~Treatment + Type) ``` ### `v()` – shorthand for `vparse()` ```r v(Treatment, Type) v(~Treatment + Type) v(c("Treatment", "Type")) ``` ### `vselect()` ```r vselect(CO2, Treatment, Type) vselect(CO2, ~Treatment + Type) ``` ### `vcheck()` ```r vcheck(CO2, Treatment) vcheck(CO2, ~Treatment + Type) ``` ### `vmap()` ```r vmap(~Treatment + Type, toupper) ``` ### `vrename()` ```r vrename(CO2, c(Treatment = "Trt", Type = "Group")) ``` ## Summary The `v*` tools simplify working with variable names and selection in data frames, making it easier to write functions that accept flexible input formats. doBy/inst/WORDLIST0000644000176200001440000000401514551451413013321 0ustar liggesusersyvar tvar tse withdrw worryin sociabl respnsi organiz outgoin persevr laidbck hardwrk givinup friendl easygon discipl disorgn criticl contrar coopera Heliothis virescens pyrethroid summaryBy subsetBy splitBy sapplyBy sampleBy scaleBy transformBy powerBy orderBy lapplyBy lapply talkatv opposng taylor tumours synonymously timeSinceEvent renameCol reimplemented recodevar recodeVar qqnormBy formulaFunBy speeded popMatrix popMeans pectoris th subSeq statistik rle parseGroupFormula xyplot xyFunBy xxxBy xxBy proc myocardic Malkekoens energibalance og mobilisering microbenchmark roxygen recoded rowwise lmer lme lmBy lm geeglm glm glht mer lsMeans lsmeans preprocessed nullBasis pbkrtest postfix lactations linMatrix linMeans linest groupwise esticon multcomp sapply tocopheryl recodeVar firstobs lastobs ischaemic expiratory github gillnets Aarhus Acknowledgements Akademisk Alessandro Anders Anim Autofom Auxillary BCmodcomp BIFROST BagBoosting Behaviour Benchmarking Beuhlmann Bioinformatics Biosens Bo Brøndum Budworm Bulow Chabot Collett DAmico Danmarks Dettling Evit FIXME FatXX Febbo Forlag Friggens Funcetion Gabor Gadus Golub Gorjanc Gregor Grothendieck HTMLreport Hald Halekoh Hirsch Hmisc Hyperchol Hypercholesterolemia KRmodcomp Kantoff Keles Kenward LD LEmatrix LSmatrix LSmeans Ladd Lauridsen LeanMeat Leidi Loda Løvendahl Manola Metoder Milimeter Modelling Multichip Murrell Mx NA's NIR NIRmilk Octobre Oli PBmodcomp PBrefdist PubMed QWave RMA Recode Recodes Recoding Renshaw Ridder Rmarkup Robison Rosner Rscript STchange Speizer Springer Statistiske Sørensen Tager Tamayo Thiesson Toeplitz Unadjusted Venables Verlag acetat agreebl airquality al analysed approvn asNumericMatrix ascendingly assesment auxillary bquote budworm budworms carb carcassall carelss cfr chr codstom commerical conc cowno coxph createFunBy crimeRate cropyield cypermethrin deltaMethod descStat dietox dl dm doi dplyr env et ewin fev fn frac funBy ggplot gls groupid grps gsub haldCement idxvec isdals jds lactno maxn mb minn morhua mtcars occurences samme tae tbe doBy/README.md0000755000176200001440000000111614613707303012434 0ustar liggesusers## doBy Groupwise Statistics, LSmeans, Linear Contrasts, Utilities 1. Facilities for working with grouped data: 'do' something to data stratified 'by' some variables. 2. LSmeans (least-squares means), general linear contrasts. 3. Miscellaneous utilities 4. Miscellaneous datasets Author: Søren Højsgaard, Ulrich Halekoh ### Online documentation https://hojsgaard.github.io/doBy ### Development site https://github.com/hojsgaard/doBy ### CRAN page: https://cran.r-project.org/web/packages/doBy/index.html ### Further info at: http://people.math.aau.dk/~sorenh/software/doBy/ doBy/build/0000755000176200001440000000000015030320366012244 5ustar liggesusersdoBy/build/vignette.rds0000644000176200001440000000057715030320366014614 0ustar liggesusersRN0LPڢ D" r"VZ7>Q6J 񮟇At( npS}׻x!W`J Tx rj \&W j-LiS_r<-h斖$Pň&![.`<QZp#&d FrYM(1 aZR binomial_to_bernoulli_data(ndead, ntotal, type="total") dat.b <- dat |> dplyr::mutate(nalive=ntotal-ndead) |> dplyr::select(-ntotal) |> binomial_to_bernoulli_data(ndead, nalive, type="rest") m0 <- glm(cbind(ndead, ntotal-ndead) ~ dose + sex, data=dat, family=binomial()) m1 <- glm(ndead / ntotal ~ dose + sex, data=dat, weight=ntotal, family=binomial()) ma <- glm(response ~ dose + sex, data=dat.a, family=binomial()) mb <- glm(response ~ dose + sex, data=dat.b, family=binomial()) dat.a$response dat.b$response ## Not same and therefore the following do not match all.equal(coef(m0), coef(ma)) all.equal(coef(m0), coef(mb)) all.equal(coef(m1), coef(ma)) all.equal(coef(m1), coef(mb)) } doBy/man/response.Rd0000644000176200001440000000073314767113540014062 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/linear_modelling.R \name{response} \alias{response} \title{Get response variable from model} \usage{ response(object) } \arguments{ \item{object}{lm or glm object} } \description{ Get response variable from model } \examples{ data(cars) lm1 <- lm(dist ~ speed + I(speed^2), data=cars) lm1 |> response() |> head() cars <- cars |> add_pred(lm1) cars |> head() cars <- cars |> add_resid(lm1) cars } doBy/man/expr_to_fun.Rd0000644000176200001440000000160115030023277014536 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/functional_coerce_expr_to_function.R \name{expr_to_fun} \alias{expr_to_fun} \title{Convert expression into function object.} \usage{ expr_to_fun(expr_, order = NULL, vec_arg = FALSE) } \arguments{ \item{expr_}{R expression.} \item{order}{desired order of function argument.} \item{vec_arg}{should the function take vector valued argument.} } \description{ Convert expression into function object. } \examples{ ee <- expression(b1 + (b0 - b1)*exp(-k*x) + b2*x) ff1 <- expr_to_fun(ee) formals(ff1) ff2 <- expr_to_fun(ee, vec_arg=TRUE) formals(ff2) formals(ff2)$length_parm formals(ff2)$names_parm |> eval() ee <- expression(matrix(c(x1+x2, x1-x2, x1^2+x2^2, x1^3+x2^3), nrow=2)) ff1 <- expr_to_fun(ee) ff2 <- expr_to_fun(ee, vec_arg=TRUE) formals(ff2) formals(ff2)$length_parm formals(ff2)$names_parm |> eval() } doBy/man/dietox.Rd0000644000176200001440000000267014634662434013526 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{dietox} \alias{dietox} \title{Growth curves of pigs in a 3x3 factorial experiment} \format{ This data frame contains the following columns: \describe{ \item{Weight}{Weight in Kg} \item{Feed}{Cumulated feed intake in Kg} \item{Time}{Time (in weeks) in the experiment} \item{Pig}{Factor; id of each pig} \item{Evit}{Factor; vitamin E dose; see 'details'.} \item{Cu}{Factor, copper dose; see 'details'} \item{Start}{Start weight in experiment, i.e. weight at week 1.} \item{Litter}{Factor, id of litter of each pig} } } \source{ Lauridsen, C., Højsgaard, S.,Sørensen, M.T. C. (1999) Influence of Dietary Rapeseed Oli, Vitamin E, and Copper on Performance and Antioxidant and Oxidative Status of Pigs. J. Anim. Sci.77:906-916 } \usage{ dietox } \description{ The \code{dietox} data frame has 861 rows and 7 columns. } \details{ Data contains weight of slaughter pigs measured weekly for 12 weeks. Data also contains the start weight (i.e. the weight at week 1). The treatments are 3 different levels of Evit = vitamin E (dose: 0, 100, 200 mg dl-alpha-tocopheryl acetat /kg feed) in combination with 3 different levels of Cu=copper (dose: 0, 35, 175 mg/kg feed) in the feed. The cumulated feed intake is also recorded. The pigs are litter mates. } \examples{ data(dietox) head(dietox) coplot(Weight ~ Time | Evit * Cu, data=dietox) } \keyword{datasets} doBy/man/potatoes.Rd0000644000176200001440000000137014551451413014053 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{potatoes} \alias{potatoes} \title{Weight and size of 20 potatoes} \format{ A data frame with 20 observations on the following 3 variables. \describe{ \item{\code{weight}}{a numeric vector} \item{\code{length}}{a numeric vector} \item{\code{width}}{a numeric vector} } } \source{ My own garden; autumn 2015. } \usage{ potatoes } \description{ Weight and size of 20 potatoes. Weight in grams; size in millimeter. There are two sizes: \code{length} is the longest length and \code{width} is the shortest length across a potato. } \examples{ data(potatoes) plot(potatoes) } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{datasets} doBy/man/get_formulas.Rd0000644000176200001440000000075314607145223014711 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model_stability.r \name{get_formulas} \alias{get_formulas} \title{Get formulas from model_stability_glm_class object} \usage{ get_formulas(object, unique = TRUE, text = FALSE) } \arguments{ \item{object}{A model_stability_glm_class object} \item{unique}{If TRUE, return unique models} \item{text}{If TRUE, return text (rather than formula).} } \description{ Get formulas from model_stability_glm_class object } doBy/man/cv_glm_fitlist.Rd0000644000176200001440000000062514664066102015226 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model_stability.r \name{cv_glm_fitlist} \alias{cv_glm_fitlist} \title{Cross-validation for list of glm objects} \usage{ cv_glm_fitlist(data., fit_list, K = 10) } \arguments{ \item{data.}{A data frame} \item{fit_list}{A list of glm objects} \item{K}{Number of folds} } \description{ Cross-validation for list of glm objects } doBy/man/doBy-internal.Rd0000755000176200001440000000230514551451413014726 0ustar liggesusers\name{internal} \alias{internal} \alias{orderByOLD} \alias{splitByOLD} \alias{summaryByOLD} \alias{linest_matrix_class-class} \alias{print.linest_class} \alias{print.esticon_class} \alias{print.linest_matrix_class} \alias{summary.linest_matrix_class} \alias{asNumericMatrix2} \alias{matrix2dataFrame2} \alias{subsAttr2} \alias{doby.xtabs} \alias{print.lmBy} \alias{print.summary_lmBy} \alias{print.splitByData} \alias{linest_class-class} \alias{\%>\%} \title{Internal functions for the doBy package} \description{Internal functions for the doBy package} \keyword{internal}% at least one, from doc/KEYWORDS %%\alias{funBy} %%\alias{pdiff} %\alias{} %% \alias{nullBasis} %% \alias{nullBasis.lm} %% \alias{nullBasis.glm} %% \alias{nullBasis.geeglm} %% \alias{nullBasis.lmerMod} %% \alias{nullBasis.matrix} %% \alias{nullBasis.Matrix} %% \alias{null_basis} %%\alias{is_estimable} %% \alias{get_null_basis.matrix} %% \alias{get_null_basis.Matrix} %% \alias{get_null_basis.default} % \alias{HTMLCommand2} % \alias{HTMLdefault2} % \alias{RweaveHTML2} % \alias{RweaveHTMLRuncode2} % \alias{RweaveHTMLreport} % \alias{RweaveHTMLreportSetup} % \alias{RweaveHTMLreportWritedoc} % \alias{RweaveHTMLreportFinish} doBy/man/vrename.Rd0000644000176200001440000000060315024532741013650 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/vtools.R \name{vrename} \alias{vrename} \title{Rename columns in a data frame} \usage{ vrename(df, rename_map) } \arguments{ \item{df}{A data frame} \item{rename_map}{A named character vector (old_name = new_name)} } \value{ A data frame with renamed columns } \description{ Rename columns in a data frame } doBy/man/crimeRate.Rd0000644000176200001440000000116514551451413014132 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{crimeRate} \alias{crimeRate} \title{crimeRate} \format{ This data frame contains: \describe{ \item{state:}{State of the USA} \item{murder:}{crime of murder} \item{rape:}{} \item{robbery:}{} \item{assault:}{} \item{burglary:}{residential theft} \item{larceny:}{unlawful taking of personal property (pocket picking)} \item{autotheft:}{} } } \usage{ crimeRate } \description{ Crime rates per 100,000 inhabitants in states of the USA for different crime types in 1977. } \examples{ data(crimeRate) } \keyword{datasets} doBy/man/vselect.Rd0000644000176200001440000000065515024532741013667 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/vtools.R \name{vselect} \alias{vselect} \title{Select columns from a data frame using flexible input} \usage{ vselect(df, ...) } \arguments{ \item{df}{A data frame} \item{...}{Variable input (unquoted, character vector, or formula)} } \value{ A data frame with selected columns } \description{ Select columns from a data frame using flexible input } doBy/man/by-transform.Rd0000755000176200001440000000236514551451413014650 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/by_transformBy.R \name{by-transform} \alias{by-transform} \alias{transform_by} \alias{transformBy} \title{Function to make groupwise transformations} \usage{ transform_by(data, formula, ...) transformBy(formula, data, ...) } \arguments{ \item{data}{A data frame} \item{formula}{A formula with only a right hand side, see examples below} \item{\dots}{Further arguments of the form tag=value} } \value{ The modified value of the dataframe data. } \description{ Function to make groupwise transformations of data by applying the transform function to subsets of data. } \details{ The ... arguments are tagged vector expressions, which are evaluated in the data frame data. The tags are matched against names(data), and for those that match, the value replace the corresponding variable in data, and the others are appended to data. } \examples{ data(dietox) transformBy(~Pig, data=dietox, minW=min(Weight), maxW=max(Weight), gain=diff(range(Weight))) } \seealso{ \code{\link{orderBy}}, \code{\link{order_by}}, \code{\link{summaryBy}}, \code{\link{summary_by}}, \code{\link{splitBy}}, \code{\link{split_by}} } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{univar} doBy/man/generate_data_list.Rd0000644000176200001440000000061614607145223016036 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model_stability.r \name{generate_data_list} \alias{generate_data_list} \title{Generate data list} \usage{ generate_data_list(data., K, method = c("subgroups", "resample")) } \arguments{ \item{data.}{A data frame} \item{K}{Number of folds} \item{method}{Method for generating data} } \description{ Generate data list } doBy/man/codstom.Rd0000644000176200001440000001174714551451413013676 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{codstom} \alias{codstom} \title{Diet of Atlantic cod in the Gulf of St. Lawrence (Canada)} \format{ A data frame with 10000 observations on the following 10 variables. \describe{ \item{\code{region}}{a factor with levels \code{SGSL} \code{NGSL} representing the southern and northern Gulf of St. Lawrence, respectively} \item{\code{ship.type}}{a factor with levels \code{2} \code{3} \code{31} \code{34} \code{90} \code{99}} \item{\code{ship.id}}{a factor with levels \code{11558} \code{11712} \code{136148} \code{136885} \code{136902} \code{137325} \code{151225} \code{151935} \code{99433}} \item{\code{trip}}{a factor with levels \code{10} \code{11} \code{12} \code{179} \code{1999} \code{2} \code{2001} \code{20020808} \code{3} \code{4} \code{5} \code{6} \code{7} \code{8} \code{88} \code{9} \code{95}} \item{\code{set}}{a numeric vector} \item{\code{fish.id}}{a numeric vector} \item{\code{fish.length}}{a numeric vector, length in mm} \item{\code{prey.mass}}{a numeric vector, mass of item in stomach, in g} \item{\code{prey.type}}{a factor with levels \code{Ammodytes_sp} \code{Argis_dent} \code{Chion_opil} \code{Detritus} \code{Empty} \code{Eualus_fab} \code{Eualus_mac} \code{Gadus_mor} \code{Hyas_aran} \code{Hyas_coar} \code{Lebbeus_gro} \code{Lebbeus_pol} \code{Leptocl_mac} \code{Mallot_vil} \code{Megan_norv} \code{Ophiuroidea} \code{Other} \code{Paguridae} \code{Pandal_bor} \code{Pandal_mon} \code{Pasiph_mult} \code{Sabin_sept} \code{Sebastes_sp} \code{Them_abys} \code{Them_comp} \code{Them_lib}} } } \source{ Small subset from a larger dataset (more stomachs, more variables, more \code{prey.types}) collected by D. Chabot and M. Hanson, Fisheries & Oceans Canada \email{chabotd@dfo-mpo.gc.ca}. } \usage{ codstom } \description{ Stomach content data for Atlantic cod (\emph{Gadus morhua}) in the Gulf of St.Lawrence, Eastern Canada. Note: many prey items were of no interest for this analysis and were regrouped into the "Other" category. } \details{ Cod are collected either by contracted commerical fishing vessels (\code{ship.type} 90 or 99) or by research vessels. Commercial vessels are identified by a unique \code{ship.id}. Either one research vessel or several commercial vessels conduct a survey (\code{trip}), during which a trawl, gillnets or hooked lines are set several times. Most trips are random stratified surveys (depth-based stratification). Each trip takes place within one of the \code{region}s. The \code{trip} label is only guaranteed to be unique within a region and the \code{set} label is only guaranteed to be unique within a \code{trip}. For each fish caught, the \code{fish.length} is recorded and the fish is allocated a \code{fish.id}, but the \code{fish.id} is only guaranteed to be unique within a \code{set}. A subset of the fish caught are selected for stomach analysis (stratified random selection according to fish length; unit of stratification is the set for research surveys, the combination ship.id and stratum for surveys conducted by commercial vessels, although strata are not shown in codstom). The basic experimental unit in this data set is a cod stomach (one stomach per fish). Each stomach is uniquely identified by a combination of \code{region}, \code{ship.type}, \code{ship.id}, \code{trip}, \code{set}, and \code{fish.id}. For each prey item found in a stomach, the species and mass of the prey item are recorded, so there can be multiple observations per stomach. There may also be several prey items with the same \code{prey.type} in the one stomach (for example many \code{prey.types} have been recoded \code{Other}, which produced many instances of \code{Other} in the same stomach). If a stomach is empty, a single observation is recorded with \code{prey.type} \code{Empty} and a \code{prey.mass} of zero. } \examples{ data(codstom) str(codstom) # removes multiple occurences of same prey.type in stomachs codstom1 <- summaryBy(prey.mass ~ region + ship.type + ship.id + trip + set + fish.id + prey.type, data = codstom, FUN = sum) # keeps a single line per stomach with the total mass of stomach content codstom2 <- summaryBy(prey.mass ~ region + ship.type + ship.id + trip + set + fish.id, data = codstom, FUN = sum) # mean prey mass per stomach for each trip codstom3 <- summaryBy(prey.mass.sum ~ region + ship.type + ship.id + trip, data = codstom2, FUN = mean) \dontrun{ # wide version, one line per stomach, one column per prey type library(reshape) codstom4 <- melt(codstom, id = c(1:7, 9)) codstom5 <- cast(codstom4, region + ship.type + ship.id + trip + set + fish.id + fish.length ~ prey.type, sum) k <- length(names(codstom5)) prey_col <- 8:k out <- codstom5[,prey_col] out[is.na(out)] <- 0 codstom5[,prey_col] <- out codstom5$total.content <- rowSums(codstom5[, prey_col]) } } \keyword{datasets} doBy/man/data_personality.Rd0000644000176200001440000000143614634662024015566 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{data_personality} \alias{data_personality} \alias{personality} \title{Personality traits} \format{ This dataframe has recordings on the following 32 variables: distant, talkatv, carelss, hardwrk, anxious, agreebl, tense, kind, opposng, relaxed, disorgn, outgoin, approvn, shy, discipl, harsh, persevr, friendl, worryin, respnsi, contrar, sociabl, lazy, coopera, quiet, organiz, criticl, lax, laidbck, withdrw, givinup, easygon } \usage{ data(personality) } \description{ The \code{peronality} dataframe has 240 rows and 32 columns } \examples{ data(personality) str(personality) } \references{ Origin unclear } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{datasets} doBy/man/interaction-plot.Rd0000644000176200001440000000166515030065056015514 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_functions.r \name{interaction-plot} \alias{interaction-plot} \alias{interaction_plot} \title{Two-way interaction plot} \usage{ interaction_plot(.data, .formula, interval = "conf.int") } \arguments{ \item{.data}{A data frame} \item{.formula}{A formula of the form \code{y ~ x1 + x2}} \item{interval}{Either \code{conf.int}, \code{boxplot} or \code{none}} } \description{ Plots the mean of the response for two-way combinations of factors, thereby illustrating possible interactions. } \note{ This is a recent addition to the package and is subject to change. } \examples{ income$educf <- cut(income$educ, breaks=3) income |> interaction_plot(inc ~ race + educf) income |> interaction_plot(inc ~ race + educf, interval="conf.int") income |> interaction_plot(inc ~ race + educf, interval="boxplot") income |> interaction_plot(inc ~ race + educf, interval="none") } doBy/man/plot_lm.Rd0000644000176200001440000000112014767113317013663 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_functions.r \name{plot_lm} \alias{plot_lm} \title{Plot linear model object} \usage{ plot_lm(lm_fit, format = "2x2", global_aes = NULL) } \arguments{ \item{lm_fit}{An object of class 'lm'} \item{format}{The format of the plot (or a list of plots if format is "list")} \item{global_aes}{Currently no effect.} } \description{ Plot linear model object } \examples{ data(income) m1 <- lm(inc ~ race + educ, data=income) plot_lm(m1) plot_lm(m1, "2x2") plot_lm(m1, "1x4") plot_lm(m1, "4x1") plot_lm(m1, "list") } doBy/man/model_stability_glm.Rd0000644000176200001440000000107614612020050016226 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model_stability.r \name{model_stability_glm} \alias{model_stability_glm} \title{Model stability for glm objects} \usage{ model_stability_glm( data., model, n.searches = 10, method = c("subgroups", "resample"), ... ) } \arguments{ \item{data.}{A data frame} \item{model}{A glm object} \item{n.searches}{Number of searches} \item{method}{Method for generating data} \item{...}{Additional arguments to be passed to \code{\link{step}}} } \description{ Model stability for glm objects } doBy/man/reciprocal.Rd0000644000176200001440000000053115015753266014345 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/linear_modelling.R \name{reciprocal} \alias{reciprocal} \title{Reciprocal function} \usage{ reciprocal(x) } \arguments{ \item{x}{An R object for which 1/x makes sense} } \description{ A function returning the reciprocal of its argument } \author{ Søren Højsgaard } doBy/man/add_pred.Rd0000644000176200001440000000152514772257513013773 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/linear_modelling.R \name{add_pred} \alias{add_pred} \title{Add predicted values of different types to dataframe} \usage{ add_pred(data, model, var = "pred", type = NULL, transformation = NULL) } \arguments{ \item{data}{dataframe or tibble} \item{model}{model object} \item{var}{name of new variable in dataframe / tibble} \item{type}{type of predicted value} \item{transformation}{A possible transformation of predicted variable, e.g. reciprocal(), log() etc} } \value{ dataframe / tibble } \description{ Add predicted values of different types to dataframe } \examples{ data(cars) lm1 <- lm(dist ~ speed + I(speed^2), data=cars) lm1 |> response() |> head() cars <- cars |> add_pred(lm1) cars |> head() cars <- cars |> add_resid(lm1) cars } \author{ Søren Højsgaard } doBy/man/truncate0.Rd0000644000176200001440000000100414634661533014124 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/doby_utilities.R \name{truncate0} \alias{truncate0} \title{Truncate values in a matrix / vector to zero if they are below a certain threshold.} \usage{ truncate0(x, tol = 0.6, sparse = TRUE) } \arguments{ \item{x}{matrix / vector} \item{tol}{threshold} \item{sparse}{logical; if TRUE and \code{x} is a matrix, return a sparse matrix} } \description{ Truncate values in a matrix / vector to zero if they are below a certain threshold. } doBy/man/mb_summary.Rd0000644000176200001440000000134014551451413014365 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/microbenchmark.R \name{mb_summary} \alias{mb_summary} \alias{summary_mb} \title{Fast summary of microbenchmark object} \usage{ mb_summary(object, unit, add.unit = TRUE, ...) summary_mb(object, unit, add.unit = TRUE, ...) } \arguments{ \item{object}{A microbenchmark object} \item{unit}{The time unit to be used} \item{add.unit}{Should time unit be added as column to resulting dataframe.} \item{...}{Additional arguments; currently not used.} } \description{ Fast summary of microbenchmark object. The default summary method from the microbenchmark package is fairly slow in producing a summary (due to a call to a function from the multcomp package.) } doBy/man/parseGroupFormula.Rd0000755000176200001440000000145215015013546015673 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/doby_utilities.R \name{parseGroupFormula} \alias{parseGroupFormula} \title{Extract components from a formula with "conditioning bar"} \usage{ parseGroupFormula(form) } \arguments{ \item{form}{A formula of the form \code{y ~ x1 + ... + xn | g1 + ... + gm}} } \value{ If the formula is \code{y ~ x1 + x2 | g1 + g2} the result is \item{model}{\code{y ~ x1 + x2}} \item{groups}{\code{ g1 + g2}} \item{groupFormula}{\code{~ g1 + g2}} } \description{ Extract components from a formula with the form \code{y ~ x1 + ... + xn | g1 + ... + gm} } \examples{ gf1 <- parseGroupFormula(y ~ x1 + x2 | g1 + g2) gf1 gf2 <- parseGroupFormula( ~ x1 + x2 | g1 + g2) gf2 } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{utilities} doBy/man/taylor.Rd0000644000176200001440000000157614634661533013547 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/doby_utilities.R \name{taylor} \alias{taylor} \title{Taylor expansion (one dimension)} \usage{ taylor(fn, x0, ord = 1) } \arguments{ \item{fn}{A function of one variable and that variable must be named 'x'.} \item{x0}{The point in which to to the Taylor expansion.} \item{ord}{The order of the Taylor expansion.} } \value{ function. } \description{ Returns Taylor polynomial approximating a function fn(x) } \examples{ fn <- function(x) log(x) ord <- 2 x0 <- 2 xv <- seq(.2, 5, .1) plot(xv, fn(xv), type="l") lines(xv, taylor(fn, x0=x0, ord=ord)(xv), lty=2) abline(v=x0) fn <- function(x)sin(x) ord <- 4 x0 <- 0 xv <- seq(-2*pi, 2*pi, 0.1) plot(xv, fn(xv), type="l") lines(xv, taylor(fn, x0=x0, ord=ord)(xv), lty=2) abline(v=x0) } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{utilities} doBy/man/math_teachers.Rd0000644000176200001440000000130715011564745015031 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{math_teachers} \alias{math_teachers} \title{Height of math teachers} \format{ An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 30 rows and 2 columns. } \usage{ math_teachers } \description{ Height of a sample of math teachers in Danish high schools collected at a continued education day at Mariager Fjord Gymnasium in 2019. Format: height: Height in centimeters sex: male or female } \examples{ aggregate(height ~ sex, data=math_teachers, FUN=mean) aggregate(height ~ sex, data=math_teachers, FUN=function(x) {c(mean=mean(x), sd=sd(x))}) } \keyword{datasets} doBy/man/beets.Rd0000644000176200001440000000353314551451413013322 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{beets} \alias{beets} \title{beets data} \format{ The format is: chr "beets" } \usage{ beets } \description{ Yield and sugar percentage in sugar beets from a split plot experiment. Data is obtained from a split plot experiment. There are 3 blocks and in each of these the harvest time defines the "whole plot" and the sowing time defines the "split plot". Each plot was 25 square meters and the yield is recorded in kg. See 'details' for the experimental layout. } \details{ \preformatted{ Experimental plan Sowing times 1 4. april 2 12. april 3 21. april 4 29. april 5 18. may Harvest times 1 2. october 2 21. october Plot allocation: Block 1 Block 2 Block 3 +-----------|-----------|-----------+ Plot | 1 1 1 1 1 | 2 2 2 2 2 | 1 1 1 1 1 | Harvest time 1-15 | 3 4 5 2 1 | 3 2 4 5 1 | 5 2 3 4 1 | Sowing time |-----------|-----------|-----------| Plot | 2 2 2 2 2 | 1 1 1 1 1 | 2 2 2 2 2 | Harvest time 16-30 | 2 1 5 4 3 | 4 1 3 2 5 | 1 4 3 2 5 | Sowing time +-----------|-----------|-----------+ } } \examples{ data(beets) beets$bh <- with(beets, interaction(block, harvest)) summary(aov(yield ~ block + sow + harvest + Error(bh), beets)) summary(aov(sugpct ~ block + sow + harvest + Error(bh), beets)) } \references{ Ulrich Halekoh, Søren Højsgaard (2014)., A Kenward-Roger Approximation and Parametric Bootstrap Methods for Tests in Linear Mixed Models - The R Package pbkrtest., Journal of Statistical Software, 58(10), 1-30., \url{https://www.jstatsoft.org/v59/i09/} } \keyword{datasets} doBy/man/firstlastobs.Rd0000755000176200001440000000223415030274224014733 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data_firstobs_lastobs.R \name{firstlastobs} \alias{firstlastobs} \alias{lastobs} \alias{firstobs} \alias{firstobs.default} \alias{lastobs.default} \alias{firstobs.formula} \alias{lastobs.formula} \title{Locate the index of the first/last unique value} \usage{ lastobs(x, ...) firstobs(x, ...) \method{lastobs}{default}(x, ...) \method{firstobs}{default}(x, ...) \method{lastobs}{formula}(formula, data = parent.frame(), ...) \method{firstobs}{formula}(formula, data = parent.frame(), ...) } \arguments{ \item{x}{A vector} \item{\dots}{Currently not used} \item{formula}{A formula (only the first term is used, see 'details').} \item{data}{A data frame} } \value{ A vector. } \description{ Locate the index of the first/last unique value in i) a vector or of a variable in a data frame. } \details{ If writing ~a + b + c as formula, then only a is considered. } \examples{ x <- c(rep(1, 5), rep(2, 3), rep(3, 7), rep(1, 4)) firstobs(x) lastobs(x) data(dietox) firstobs(~Pig, data=dietox) lastobs(~Pig, data=dietox) } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{utilities} doBy/man/add_resid.Rd0000644000176200001440000000130714756652024014143 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/linear_modelling.R \name{add_resid} \alias{add_resid} \title{Add residuals of different types to dataframe} \usage{ add_resid(data, model, var = "resid", type) } \arguments{ \item{data}{dataframe or tibble} \item{model}{model object} \item{var}{name of new variable in dataframe / tibble} \item{type}{type of residual value} } \value{ dataframe / tibble } \description{ Add residuals of different types to dataframe } \examples{ data(cars) lm1 <- lm(dist ~ speed + I(speed^2), data=cars) lm1 |> response() |> head() cars <- cars |> add_pred(lm1) cars |> head() cars <- cars |> add_resid(lm1) cars } \author{ Søren Højsgaard } doBy/man/nir_milk.Rd0000644000176200001440000000152214551451413014020 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{nir_milk} \alias{nir_milk} \title{nir_milk} \format{ A list with two components x Datafrane with infra red light amount at different wavelengths (column names are the wavelengths; just remove the leading X). y Datafrane with response variables fat, protein, lactose and dm (drymatter) } \usage{ nir_milk } \description{ Near infra red light (NIR) measurements are made at 152 wavelengths on 17 milk samples. While milk runs through a glass tube, infra red light is sent through the tube and the amount of light passing though the tube is measured at different wavelengths. Each milk sample was additionally analysed for fat, lactose, protein and dry matter. } \examples{ data(nir_milk) } \seealso{ \link{NIRmilk} } \keyword{datasets} doBy/man/dot-rhsf2list.Rd0000644000176200001440000000047514551451413014726 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/doby_utilities.R \name{.rhsf2list} \alias{.rhsf2list} \title{Convert right hand sided formula to a list} \usage{ .rhsf2list(f) } \arguments{ \item{f}{A right hand sided formula} } \description{ Convert right hand sided formula to a list } doBy/man/by-subset.Rd0000755000176200001440000000255214551451413014140 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/by_subsetBy.R \name{by-subset} \alias{by-subset} \alias{subset_by} \alias{subsetBy} \title{Finds subsets of a dataframe which is split by variables in a formula.} \usage{ subset_by(data, formula, subset, select, drop = FALSE, join = TRUE, ...) subsetBy( formula, subset, data = parent.frame(), select, drop = FALSE, join = TRUE, ... ) } \arguments{ \item{data}{A data frame.} \item{formula}{A right hand sided formula or a character vector of variables to split by.} \item{subset}{logical expression indicating elements or rows to keep: missing values are taken as false.} \item{select}{expression, indicating columns to select from a data frame.} \item{drop}{passed on to \code{[} indexing operator.} \item{join}{If FALSE the result is a list of data frames (as defined by 'formula'); if TRUE one data frame is returned.} \item{\dots}{further arguments to be passed to or from other methods.} } \value{ A data frame. } \description{ A data frame is split by a formula into groups. Then subsets are found within each group, and the result is collected into a data frame. } \examples{ data(dietox) subsetBy(~Evit, Weight < mean(Weight), data=dietox) } \seealso{ \code{\link{splitBy}}, \code{\link{split_by}} } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{utilities} doBy/man/recover_pca_data.Rd0000644000176200001440000000167214551451413015503 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/recover_pca_data.R \name{recover_pca_data} \alias{recover_pca_data} \title{Recover data from principal component analysis} \usage{ recover_pca_data(object, comp = 1) } \arguments{ \item{object}{An object of class \code{prcomp}.} \item{comp}{The number of components to be used. Must be smaller than the number of variables.} } \value{ A dataframe } \description{ Recover data from principal component analysis based on the first (typically few) components. } \examples{ crime <- doBy::crimeRate rownames(crime) <- crime$state crime$state <- NULL o <- order(apply(scale(crime), 1, sum)) dat <- crime[o,] head(dat) tail(dat) matplot(scale(dat), type="l") pc1 <- prcomp(dat, scale. = TRUE) summary(pc1) rec2 <- recover_pca_data(pc1, 2) pairs(rec2) par(mfrow=c(1,2)) matplot(scale(dat), type="l") matplot(scale(rec2), type="l") j <- merge(dat, rec2, by=0) pairs(j[,-1]) } doBy/man/rbind_list.Rd0000644000176200001440000000136015030273443014343 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data_rbind_list.R \name{rbind_list} \alias{rbind_list} \title{Bind list of data frames and add list names as a column} \usage{ rbind_list(lst, name = "name") } \arguments{ \item{lst}{A named list of data frames or tibbles.} \item{name}{A character scalar: name of the column to hold the list names (default "name").} } \value{ A data frame or tibble, depending on the class of the input. } \description{ Binds a named list of data frames (or tibbles) into a single data frame. Adds the list name as a new column (first column). } \examples{ lst <- list(a = data.frame(x = 1:2), b = data.frame(x = 3:4)) rbind_list(lst) lst <- split(iris, iris$Species) rbind_list(lst) } doBy/man/by-summary.Rd0000755000176200001440000000537715030316250014330 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/by_summaryBy.R \name{by-summary} \alias{by-summary} \alias{summary_by} \alias{summaryBy} \title{Groupwise summary statistics} \usage{ summary_by( data, formula, id = NULL, FUN = mean, keep.names = FALSE, p2d = FALSE, order = TRUE, full.dimension = FALSE, var.names = NULL, fun.names = NULL, ... ) summaryBy( formula, data = parent.frame(), id = NULL, FUN = mean, keep.names = FALSE, p2d = FALSE, order = TRUE, full.dimension = FALSE, var.names = NULL, fun.names = NULL, ... ) } \arguments{ \item{data}{A data frame.} \item{formula}{A formula specifying response and grouping variables.} \item{id}{A formula indicating variables to retain (not grouped by).} \item{FUN}{A function or list of functions to apply to the response variables.} \item{keep.names}{Logical; keep original variable names if only one function is applied.} \item{p2d}{Replace parentheses in output names with dots?} \item{order}{Logical; should result be ordered by grouping variables?} \item{full.dimension}{Logical; if TRUE, repeat rows so output matches input size.} \item{var.names}{Optional custom names for response variables.} \item{fun.names}{Optional custom names for functions applied.} \item{...}{Additional arguments passed to functions in \code{FUN}.} } \value{ A data frame of grouped summary statistics. } \description{ Computes summary statistics by groups, similar to the \code{summary} procedure in SAS. A more flexible alternative to base R's \code{\link[stats]{aggregate}}. } \details{ Extra arguments in \code{...} are passed to all functions in \code{FUN}. If needed, wrap functions to handle these consistently (e.g., for \code{na.rm = TRUE}). } \examples{ data(CO2) # Simple groupwise mean summaryBy(uptake ~ Type + Treatment, data = CO2, FUN = mean) summaryBy(cbind(uptake, conc) ~ Type + Treatment, data = CO2, FUN = mean) # Compare with aggregate(cbind(uptake, conc) ~ Type + Treatment, data = CO2, FUN = mean) ## Using '.' on the right hand side of a formula means to stratify by ## all variables not used elsewhere: summaryBy(uptake ~ ., data = CO2, FUN = mean) # Multiple functions using a custom summary function myfun <- function(x, ...) c(m = mean(x, na.rm = TRUE), v = var(x, na.rm = TRUE), n = length(x)) summaryBy(uptake ~ Type + Treatment, data = CO2, FUN = myfun) # Summary on transformed variables # works: summaryBy(cbind(lu=log(uptake), conc) ~ Type, data = CO2, FUN = mean) # fails: #summaryBy(cbind(log(uptake), conc) ~ Type, data = CO2, FUN = mean) } \seealso{ \code{\link[stats]{aggregate}}, \code{\link{orderBy}}, \code{\link{transformBy}}, \code{\link{splitBy}} } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{grouping} \keyword{summary} doBy/man/recodeVar.Rd0000755000176200001440000000462514634661533014150 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/doby_utilities.R \name{recodeVar} \alias{recodeVar} \alias{recode_var} \title{Recode values of a vector} \usage{ recodeVar(x, src, tgt, default = NULL, keep.na = TRUE) recode_var(x, src, tgt, default = NULL, keep.na = TRUE) } \arguments{ \item{x}{A vector; the variable to be recoded.} \item{src}{The source values: a subset of the present values of x} \item{tgt}{The target values: the corresponding new values of x} \item{default}{Default target value for those values of x not listed in \code{src}. When default=NULL, values of x which are not given in \code{src} will be kept in the output.} \item{keep.na}{If TRUE then NA's in x will be retained in the output} } \value{ A vector } \description{ Recodes a vector with values, say 1,2 to a variable with values, say 'a', 'b' } \section{Warning }{ Care should be taken if x is a factor. A safe approach may be to convert x to a character vector using as.character. } \examples{ x <- c("dec", "jan", "feb", "mar", "apr", "may") src1 <- list(c("dec", "jan", "feb"), c("mar", "apr", "may")) tgt1 <- list("winter", "spring") recodeVar(x, src=src1, tgt=tgt1) #[1] "winter" "winter" "winter" "spring" "spring" "spring" x <- c(rep(1:3, 3)) #[1] 1 2 3 1 2 3 1 2 3 ## Simple usage: recodeVar(x, src=c(1, 2), tgt=c("A", "B")) #[1] "A" "B" NA "A" "B" NA "A" "B" NA ## Here we need to use lists recodeVar(x, src=list(c(1, 2)), tgt=list("A")) #[1] "A" "A" NA "A" "A" NA "A" "A" NA recodeVar(x, src=list(c(1, 2)), tgt=list("A"), default="L") #[1] "A" "A" "L" "A" "A" "L" "A" "A" "L" recodeVar(x, src=list(c(1, 2), 3), tgt=list("A", "B"), default="L") #[1] "A" "A" "B" "A" "A" "B" "A" "A" "B" ## Dealing with NA's in x x<-c(NA,rep(1:3, 3),NA) #[1] NA 1 2 3 1 2 3 1 2 3 NA recodeVar(x, src=list(c(1, 2)), tgt=list("A")) #[1] NA "A" "A" NA "A" "A" NA "A" "A" NA NA recodeVar(x, src=list(c(1, 2)), tgt=list("A"), default="L") #[1] NA "A" "A" "L" "A" "A" "L" "A" "A" "L" NA recodeVar(x, src=list(c(1, 2)), tgt=list("A"), default="L", keep.na=FALSE) #[1] "L" "A" "A" "L" "A" "A" "L" "A" "A" "L" "L" x <- c("no", "yes", "not registered", "no", "yes", "no answer") recodeVar(x, src = c("no", "yes"), tgt = c("0", "1"), default = NA) } \seealso{ \code{\link[base]{cut}}, \code{\link[base]{factor}}, \code{\link[doBy]{recodeVar}} } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{utilities} doBy/man/time_since_event.Rd0000644000176200001440000000302315030071003015514 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sequence_utilities.R \name{time_since_event} \alias{time_since_event} \alias{timeSinceEvent} \title{Calculate "time since event" in a vector} \usage{ time_since_event(yvar, tvar = seq_along(yvar)) timeSinceEvent(...) } \arguments{ \item{yvar}{A numeric or logical vector indicating events.} \item{tvar}{An optional numeric vector specifying time values. Defaults to the index.} \item{...}{Arguments pased on to time_since_event} } \value{ A data frame with columns 'yvar', 'tvar', 'abs.tse' (absolute time since event), 'sign.tse' (signed time since event), and other helper columns. } \description{ Calculates the time since the nearest event in a sequence, optionally using a custom time scale. } \details{ Events are coded as 1 (or TRUE). Non-events are anything else. The result includes absolute and signed distances to events. } \examples{ ## Example 1: Basic usage with default time index y <- c(0, 0, 1, 0, 0, 1, 0) tse <- time_since_event(y) print(tse) ## Example 2: Custom (non-integer) time variable y <- c(0, 0, 1, 0, 0, 0, 1, 0) t <- seq(0.5, 3.5, length.out = length(y)) tse <- time_since_event(y, t) print(tse) ## Example 3: Plotting the signed time since event plot(sign.tse ~ tvar, data = tse, type = "b", main = "Signed time since event", xlab = "Time", ylab = "Signed time since event") grid() abline(h = 0, col = "red", lty = 2) } \seealso{ \code{\link{subSeq}}, \code{\link{rle}} } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } doBy/man/income.Rd0000644000176200001440000000151114756241373013475 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{income} \alias{income} \title{income data} \format{ This data frame contains: \describe{ \item{inc:}{Income: Yearly income (thousands of dollars).} \item{educ:}{Education: Number of years of education (12=high school graduate, 16=college graduate).} \item{race:}{Racial-Ethnic group: "b" (black), "h" (hispanic) and "w" (white).} } } \usage{ income } \description{ Data on income, years of educations and ethnicity for a samle of adult Americans aged over 25. The year of sampling is not avalable in the source. } \details{ Variable names are as in the reference. } \references{ Agresti, A. (2024) Statistical Methods for the Social Sciences, Global Edition (6th edition). ISBN-13: 9781292449197. Table 13.1 } \keyword{datasets} doBy/man/data_mathmark.Rd0000644000176200001440000000120014634662024015006 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{data_mathmark} \alias{data_mathmark} \alias{mathmark} \alias{math} \title{Mathematics marks for students} \format{ This data frame contains the following columns: mechanics, vectors, algebra, analysis, statistics. } \usage{ data(mathmark) } \description{ The \code{mathmark} data frame has 88 rows and 5 columns. } \examples{ data(mathmark) } \references{ David Edwards, An Introduction to Graphical Modelling, Second Edition, Springer Verlag, 2000 } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{datasets} doBy/man/which.maxn.Rd0000755000176200001440000000153014634661533014272 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/doby_utilities.R \name{which.maxn} \alias{which.maxn} \alias{which.minn} \title{Where are the n largest or n smallest elements in a numeric vector ?} \usage{ which.maxn(x, n = 1) } \arguments{ \item{x}{numeric vector} \item{n}{integer >= 1} } \value{ A vector of length at most n with the indices of the n largest / smaller elements. NAs are discarded and that can cause the vector to be smaller than n. } \description{ Determines the locations, i.e., indices of the n largest or n smallest elements of a numeric vector. } \examples{ x <- c(1:4, 0:5, 11, NA, NA) ii <- which.minn(x, 5) x <- c(1, rep(NA,10), 2) ii <- which.minn(x, 5) } \seealso{ \code{\link{which.max}}, \code{\link{which.min}} } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{utilities} doBy/man/tidy-esticon.Rd0000644000176200001440000000113115030273443014621 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/linest_tidy.R \name{tidy-esticon} \alias{tidy-esticon} \alias{tidy.esticon_class} \title{Tidy an esticon object} \usage{ \method{tidy}{esticon_class}(x, conf.int = FALSE, conf.level = 0.95, ...) } \arguments{ \item{x}{A 'esticon_class' object (produced by \code{esticon} methods).} \item{conf.int}{Should confidence intervals be added.} \item{conf.level}{Desired confidence level.} \item{\dots}{Additional arguments; currently not used.} } \description{ Tidy summarizes information about the components of the object. } doBy/man/linest.Rd0000755000176200001440000000373314735725514013535 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/linest_compute.R \name{linest} \alias{linest} \alias{linest.lm} \alias{linest.glm} \alias{linest.geeglm} \alias{linest.lmerMod} \alias{linest.merMod} \alias{linest.default} \alias{confint.linest_class} \alias{coef.linest_class} \alias{summary.linest_class} \title{Compute linear estimates} \usage{ linest(object, L = NULL, level = 0.95, ...) \method{confint}{linest_class}(object, parm, level = 0.95, ...) \method{coef}{linest_class}(object, ...) \method{summary}{linest_class}(object, ...) } \arguments{ \item{object}{Model object} \item{L}{Either \code{NULL} or a matrix with p columns where p is the number of parameters in the systematic effects in the model. If \code{NULL} then \code{L} is taken to be the p times p identity matrix} \item{level}{The level of the (asymptotic) confidence interval.} \item{\dots}{Additional arguments; currently not used.} \item{parm}{Specification of the parameters estimates for which confidence intervals are to be calculated.} \item{confint}{Should confidence interval appear in output.} } \value{ A dataframe with results from computing the contrasts. } \description{ Compute linear estimates, i.e. \code{L \%*\% beta} for a range of models. One example of linear estimates is population means (also known as LSMEANS). } \examples{ ## Make balanced dataset dat.bal <- expand.grid(list(AA=factor(1:2), BB=factor(1:3), CC=factor(1:3))) dat.bal$y <- rnorm(nrow(dat.bal)) ## Make unbalanced dataset # 'BB' is nested within 'CC' so BB=1 is only found when CC=1 # and BB=2,3 are found in each CC=2,3,4 dat.nst <- dat.bal dat.nst$CC <-factor(c(1,1,2,2,2,2,1,1,3,3,3,3,1,1,4,4,4,4)) mod.bal <- lm(y ~ AA + BB * CC, data=dat.bal) mod.nst <- lm(y ~ AA + BB : CC, data=dat.nst) L <- LE_matrix(mod.nst, effect=c("BB", "CC")) linest( mod.nst, L ) } \seealso{ \code{\link{LSmeans}}, \code{\link{LE_matrix}} } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{utilities} doBy/man/by-order.Rd0000755000176200001440000000224215030273442013737 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/by_orderBy.R \name{by-order} \alias{by-order} \alias{order_by} \alias{orderBy} \title{Ordering (sorting) rows of a data frame} \usage{ order_by(data, formula) orderBy(formula, data) } \arguments{ \item{data}{A dataframe} \item{formula}{The right hand side of a formula} } \value{ The ordered data frame } \description{ Ordering (sorting) rows of a data frame by the certain variables in the data frame. This function is essentially a wrapper for the \code{order()} function - the important difference being that variables to order by can be given by a model formula. } \details{ The sign of the terms in the formula determines whether sorting should be ascending or decreasing; see examples below } \examples{ orderBy(~ conc + Treatment, CO2) ## Sort decreasingly by conc orderBy(~ - conc + Treatment, CO2) ## Same as: order_by(CO2, c("conc", "Treatment")) order_by(CO2, c("-conc", "Treatment")) } \seealso{ \code{\link{transformBy}}, \code{\link{transform_by}}, \code{\link{splitBy}}, \code{\link{split_by}} } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} and Kevin Wright } \keyword{utilities} doBy/man/by_scale.Rd0000644000176200001440000000224015025234421013766 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/by_scaleBy.R \name{scaleBy} \alias{scaleBy} \alias{scale_by} \title{Group-wise scaling of data} \usage{ scaleBy(formula, data = parent.frame(), center = TRUE, scale = TRUE) scale_by(data, formula, center = TRUE, scale = TRUE) } \arguments{ \item{formula}{Grouping structure: a formula, character vector, or variables as \code{as.quoted}.} \item{data}{A data frame or matrix.} \item{center}{Logical; if TRUE, center the variables.} \item{scale}{Logical; if TRUE, scale the variables.} } \value{ A list of data frames or matrices (same class as input), one per group. } \description{ Splits a data frame or matrix by grouping variables and scales numeric variables within each group. } \examples{ scale_by(iris, ~Species) scale_by(iris, ~1) ## Combine result into one data frame: a <- scale_by(iris, ~Species) d <- do.call(rbind, a) ## Old interface scaleBy(~Species, data = iris, center = TRUE, scale = FALSE) scaleBy(~1, data = iris) } \seealso{ \code{\link{summaryBy}}, \code{\link{transformBy}}, \code{\link{orderBy}} } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{utilities} doBy/man/vparse.Rd0000644000176200001440000000072515024537513013522 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/vtools.R \name{vparse} \alias{vparse} \title{Variable utilities: parse, select, check, map, rename} \usage{ vparse(...) } \arguments{ \item{...}{Variable input (unquoted, character vector, or formula)} } \description{ These functions provide flexible tools for parsing and managing variable names from formulas, unquoted names, or character vectors. Demonstrated using \code{CO2} dataset. } doBy/man/shoes.Rd0000644000176200001440000000135714756002446013350 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{shoes} \alias{shoes} \title{shoes} \format{ This data frame contains: \describe{ \item{A:}{Wear, material A} \item{B:}{Wear, material B} \item{boy:}{Id of boy} \item{footA:}{The foot with material A} } } \usage{ shoes } \description{ Wear of soles of shoes of materials A and B for one foot each for of ten boys. } \details{ The shoes data are measurements of the amount wear of the soles of shoes worn by 10 boys. The soles were made to two different synthetic materials, a standard material A and a cheaper material B. } \references{ Box, Hunter, Hunter (2005) Statistics for Experimenters, 2nd edition Wiley, p. 81. } \keyword{datasets} doBy/man/data_cad.Rd0000644000176200001440000000603414634661533013747 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{data_cad} \alias{data_cad} \alias{cad1} \alias{cad2} \title{Coronary artery disease data} \format{ A data frame with 236 observations on the following 14 variables. \describe{ \item{\code{Sex}}{Sex; a factor with levels \code{Female} \code{Male}} \item{\code{AngPec}}{Angina pectoris (chest pain attacks); a factor with levels \code{Atypical} \code{None} \code{Typical}} \item{\code{AMI}}{Acute myocardic infarct; a factor with levels \code{Definite} \code{NotCertain}} \item{\code{QWave}}{A reading from an electrocardiogram; a factor with levels \code{No} \code{Yes}; Yes means pathological and is a sign of previous myocardial infarction. } \item{\code{QWavecode}}{a factor with levels \code{Nonusable} \code{Usable}. An assesment of whether QWave is reliable.} \item{\code{STcode}}{a factor with levels \code{Nonusable} \code{Usable}. An assesment of whether STchange is reliable.} \item{\code{STchange}}{A reading from an electrocardiogram; a factor with levels \code{No} \code{Yes}. An STchange indicates a blockage of the coronary artery.} \item{\code{SuffHeartF}}{Sufficient heart frequency; a factor with levels \code{No}, \code{Yes}} \item{\code{Hypertrophi}}{a factor with levels \code{No}, \code{Yes}. Hypertrophy refers to an increased size of the heart muscle due to exercise. } \item{\code{Hyperchol}}{a factor with levels \code{No} \code{Yes}. Hypercholesterolemia, also called high cholesterol, is the presence of high levels of cholesterol in the blood.} \item{\code{Smoker}}{Is the patient a smoker; a factor with levels \code{No}, \code{Yes}.} \item{\code{Inherit}}{Hereditary predispositions for CAD; a factor with levels \code{No}, \code{Yes}.} \item{\code{Heartfail}}{Previous heart failures; a factor with levels \code{No} \code{Yes}} \item{\code{CAD}}{Coronary Artery Disease; a factor with levels \code{No} \code{Yes}}. CAD refers to a reduction of blood flow to the heart muscle (commonly known as a heart attack). The diagnosis made from biopsies. } } \usage{ data(cad1) } \description{ A cross classified table with observational data from a Danish heart clinic. The response variable is CAD (coronary artery disease, some times called heart attack). } \details{ Notice that data are collected at a heart clinic, so data do not represent the population, but are conditional on patients having ended up at the clinic. \itemize{ \item cad1: Complete dataset, 236 cases. \item cad2: Incomplete dataset, 67 cases. Information on (some of) the variables 'Hyperchol', 'Smoker' and 'Inherit' is missing. } } \examples{ data(cad1) ## maybe str(cad1) ; plot(cad1) ... } \references{ Hansen, J. F. (1980). The clinical diagnosis of ischaemic heart disease due to coronary artery disease. Danish Medical Bulletin Højsgaard, Søren and Thiesson, Bo (1995). BIFROST - Block recursive models Induced From Relevant knowledge, Observations and Statistical Techniques. Computational Statistics and Data Analysis, vol. 19, p. 155-175 } \keyword{datasets} doBy/man/vmap.Rd0000644000176200001440000000056415024547433013170 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/vtools.R \name{vmap} \alias{vmap} \title{Apply a function to each parsed variable name} \usage{ vmap(.vars, .f) } \arguments{ \item{.vars}{Variables to parse} \item{.f}{Function to apply to each name} } \value{ A list of results } \description{ Apply a function to each parsed variable name } doBy/man/data_budworm.Rd0000644000176200001440000000363214634662024014674 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{data_budworm} \alias{data_budworm} \alias{budworm} \title{Budworm data} \format{ This data frame contains 12 rows and 4 columns: \describe{ \item{sex:}{sex of the budworm.} \item{dose:}{dose of the insecticide trans-cypermethrin (in micro grams)}. \item{ndead:}{budworms killed in a trial.} \item{ntotal:}{total number of budworms exposed per trial.} } } \source{ Collett, D. (1991) Modelling Binary Data, Chapman & Hall, London, Example 3.7 } \usage{ budworm } \description{ Experiment on the toxicity to the tobacco budworm Heliothis virescens of doses of the pyrethroid trans-cypermethrin to which the moths were beginning to show resistance. Batches of 20 moths of each sex were exposed for three days to the pyrethroid and the number in each batch that were dead or knocked down was recorded. Data is reported in Collett (1991, p. 75). } \examples{ data(budworm) ## function to caclulate the empirical logits empirical.logit<- function(nevent,ntotal) { y <- log((nevent + 0.5) / (ntotal - nevent + 0.5)) y } # plot the empirical logits against log-dose log.dose <- log(budworm$dose) emp.logit <- empirical.logit(budworm$ndead, budworm$ntotal) plot(log.dose, emp.logit, type='n', xlab='log-dose',ylab='emprirical logit') title('budworm: emprirical logits of probability to die ') male <- budworm$sex=='male' female <- budworm$sex=='female' lines(log.dose[male], emp.logit[male], type='b', lty=1, col=1) lines(log.dose[female], emp.logit[female], type='b', lty=2, col=2) legend(0.5, 2, legend=c('male', 'female'), lty=c(1,2), col=c(1,2)) \dontrun{ * SAS example; data budworm; infile 'budworm.txt' firstobs=2; input sex dose ndead ntotal; run; } } \references{ Venables, W.N; Ripley, B.D.(1999) Modern Applied Statistics with S-Plus, Heidelberg, Springer, 3rd edition, chapter 7.2 } \concept{data} \keyword{datasets} doBy/man/v.Rd0000644000176200001440000000062215024601317012455 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/vtools.R \name{v} \alias{v} \title{Shorthand for vparse()} \usage{ v(...) } \arguments{ \item{...}{Variable input in any accepted \code{vparse()} form} } \value{ A character vector of variable names } \description{ A short and convenient alias for \code{vparse()}. Accepts unquoted names, character vectors, or a formula. } doBy/man/data_breastcancer.Rd0000644000176200001440000000270014634662104015643 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{data_breastcancer} \alias{data_breastcancer} \alias{breastcancer} \title{Gene expression signatures for p53 mutation status in 250 breast cancer samples} \format{ A data frame with 250 observations on 1001 variables. The first 1000 columns are numerical variables; the last column (named \code{code}) is a factor with levels \code{case} and \code{control}. } \source{ Chris Holmes, \email{c.holmes@stats.ox.ac.uk} } \usage{ breastcancer } \description{ Perturbations of the p53 pathway are associated with more aggressive and therapeutically refractory tumours. We preprocessed the data using Robust Multichip Analysis (RMA). Dataset has been truncated to the 1000 most informative genes (as selected by Wilcoxon test statistics) to simplify computation. The genes have been standardized to have zero mean and unit variance (i.e. z-scored). } \details{ The factor \code{code} defines whether there was a mutation in the p53 sequence (code=case) or not (code=control). } \examples{ data(breastcancer) bc <- breastcancer pairs(bc[,1:5], col=bc$code) train <- sample(1:nrow(bc), 50) table(bc$code[train]) \dontrun{ library(MASS) z <- lda(code ~ ., data=bc, prior = c(1, 1) / 2, subset = train) pc <- predict(z, bc[-train, ])$class pc bc[-train, "code"] table(pc, bc[-train, "code"]) } } \references{ Miller et al (2005, PubMed ID:16141321) } \keyword{datasets} doBy/man/by-lapply.Rd0000755000176200001440000000244714551451413014137 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/by_lapplyBy.R \name{by-lapply} \alias{by-lapply} \alias{lapply_by} \alias{lapplyBy} \alias{sapply_by} \alias{sapplyBy} \title{Formula based version of lapply and sapply} \usage{ lapply_by(data, formula, FUN, ...) lapplyBy(formula, data = parent.frame(), FUN, ...) sapply_by(data, formula, FUN, ..., simplify = TRUE, USE.NAMES = TRUE) sapplyBy( formula, data = parent.frame(), FUN, ..., simplify = TRUE, USE.NAMES = TRUE ) } \arguments{ \item{data}{A dataframe.} \item{formula}{A formula describing how data should be split.} \item{FUN}{A function to be applied to each element in the split list, see 'Examples' below.} \item{\dots}{optional arguments to FUN.} \item{simplify}{Same as for \code{sapply}} \item{USE.NAMES}{Same as for \code{sapply}} } \value{ A list. } \description{ This function is a wrapper for calling lapply on the list resulting from first calling splitBy. } \examples{ fun <- function(x) range(x$uptake) lapplyBy(~Treatment + Type, data=CO2, FUN=fun) sapplyBy(~Treatment + Type, data=CO2, FUN=fun) # Same as lapply(splitBy(~Treatment + Type, data=CO2), FUN=fun) } \seealso{ \code{\link{splitBy}}, \code{\link{split_by}} } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{utilities} doBy/man/child_growth.Rd0000644000176200001440000000251414770522654014704 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{child_growth} \alias{child_growth} \title{Berkeley Growth Study data} \format{ An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 2883 rows and 4 columns. } \usage{ child_growth } \description{ dataframe with heights of 39 boys and 54 girls from age 1 to 18 and the ages at which they were collected. Format: gender: Gender of child age: Age at time of data recordning subject: Idenfication for each child height: Height of child } \details{ Notice that the ages are not equally spaced. Data are taken from the fda package (growth) but put in long format here. } \references{ \if{html}{\out{
}}\preformatted{Ramsay, James O., Hooker, Giles, and Graves, Spencer (2009), _Functional data analysis with R and Matlab_, Springer, New York. Ramsay, James O., and Silverman, Bernard W. (2005), _Functional Data Analysis, 2nd ed._, Springer, New York. Ramsay, James O., and Silverman, Bernard W. (2002), _Applied Functional Data Analysis_, Springer, New York. Tuddenham, R. D., and Snyder, M. M. (1954) "Physical growth of California boys and girls from birth to age 18", _University of California Publications in Child Development_, 1, 183-364. }\if{html}{\out{
}} } \keyword{datasets} doBy/man/data-prostate.Rd0000644000176200001440000000327214551451413014770 0ustar liggesusers\name{prostate} \docType{data} \alias{prostate} \title{Prostate Tumor Gene Expression Dataset} \description{ This is the Prostate Tumor Gene Expression dataset used in Chung and Keles (2010). } \usage{ data(prostate) } \format{ A list with two components: \describe{ \item{x}{ Gene expression data. A matrix with 102 rows and 6033 columns.} \item{y}{ Class index. A vector with 102 elements.} } } \details{ The prostate dataset consists of 52 prostate tumor and 50 normal samples. Normal and tumor classes are coded in 0 and 1, respectively, in \code{y} vector. Matrix \code{x} is gene expression data and arrays were normalized, log transformed, and standardized to zero mean and unit variance across genes as described in Dettling (2004) and Dettling and Beuhlmann (2002). See Chung and Keles (2010) for more details. } \source{ Singh D, Febbo P, Ross K, Jackson D, Manola J, Ladd C, Tamayo P, Renshaw A, DAmico A, Richie J, Lander E, Loda M, Kantoff P, Golub T, and Sellers W (2002), "Gene expression correlates of clinical prostate cancer behavior", \emph{Cancer Cell}, Vol. 1, pp. 203--209. } \references{ Chung D and Keles S (2010), "Sparse partial least squares classification for high dimensional data", \emph{Statistical Applications in Genetics and Molecular Biology}, Vol. 9, Article 17. Dettling M (2004), "BagBoosting for tumor classification with gene expression data", \emph{Bioinformatics}, Vol. 20, pp. 3583--3593. Dettling M and Beuhlmann P (2002), "Supervised clustering of genes", \emph{Genome Biology}, Vol. 3, pp. research0069.1--0069.15. } \examples{ data(prostate) prostate$x[1:5,1:5] prostate$y } \keyword{datasets} doBy/man/scale_df.Rd0000644000176200001440000000126515030273443013756 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data_scale_df.R \name{scale_df} \alias{scale_df} \title{Scale numeric variables in a data frame} \usage{ scale_df(x, center = TRUE, scale = TRUE) } \arguments{ \item{x}{A data frame or matrix.} \item{center}{Logical; if TRUE, center the variables.} \item{scale}{Logical; if TRUE, scale the variables.} } \value{ An object of the same class as \code{x}. } \description{ Applies \code{base::scale()} to numeric, integer, or logical columns in a data frame. Non-numeric columns are left unchanged. } \details{ If \code{x} is not a data frame, \code{base::scale()} is applied directly. } \examples{ scale_df(iris) } doBy/man/cropyield.Rd0000644000176200001440000000161014551451413014204 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{cropyield} \alias{cropyield} \title{Yield from Danish agricultural production of grain and root crop.} \format{ A dataframe with 97 rows and 7 columns. \describe{ \item{\code{year}}{From 1901 to 1997.} \item{\code{precip}}{Milimeter precipitation.} \item{\code{yield}}{Million feed units (see details).} \item{\code{area}}{Area in 1000 ha for grains and root crop.} \item{\code{fertil}}{1000 tons fertilizer.} \item{\code{avgtmp1}}{Average temperature April-June (3 months).} \item{\code{avgtmp2}}{Average temperature July-Octobre (4 months). } } } \usage{ cropyield } \description{ Yield from Danish agricultural production of grain and root crop. } \details{ A feed unit is the amount of energy in a kg of barley. } \references{ Danmarks statistik (Statistics Denmark). } \keyword{datasets} doBy/man/haldCement.Rd0000644000176200001440000000215614551451413014264 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{haldCement} \alias{haldCement} \title{Heat development in cement under hardening.} \format{ A data frame with 13 observations on the following 5 variables. \describe{ \item{\code{x1}}{Percentage (weight) of \verb{[3Ca0][Al2O3]}} \item{\code{x2}}{Percentage (weight) of \verb{[3Cao][SiO2]}} \item{\code{x3}}{Percentage (weight) of \verb{[4Ca0][Al2O3][Fe03]}} \item{\code{x4}}{Percentage (weight) of \verb{[2Cao][SiO2]}} \item{\code{y}}{Heat development measured in calories per gram cement after 180 days} } } \usage{ haldCement } \description{ Heat development in cement under hardening related to the chemical composition. } \examples{ data(haldCement) if( interactive() ){ pairs( haldCement ) } m <- lm(y ~ x1 + x2 + x3 + x4, data=haldCement) summary(m) # Notice: The model explains practically all variation in data; # yet none of the explanatory variables appear to be statistically # significant. } \references{ Anders Hald (1949); Statistiske Metoder; Akademisk Forlag (in Danish), page 509. } \keyword{datasets} doBy/man/pow.Rd0000644000176200001440000000051515023576711013026 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/linear_modelling.R \name{pow} \alias{pow} \title{Power function} \usage{ pow(x, p) } \arguments{ \item{x}{An object for which x^p makes sense} \item{p}{A power} } \description{ A function returning x raised to the power p. } \author{ Søren Højsgaard } doBy/man/NIRmilk.Rd0000644000176200001440000000157614551451413013532 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{NIRmilk} \alias{NIRmilk} \title{NIRmilk} \format{ This data frame contains 17 rows and 158 columns. The first column is the sample number. The columns \code{Xklm} contains the transmittance (fraction of electromagnetic power) transmittance through the sample at wavelength \code{klm}. The response variables are fat, protein, lactose and dm (dry matter). } \usage{ NIRmilk } \description{ Near infra red light (NIR) measurements are made at 152 wavelengths on 17 milk samples. While milk runs through a glass tube, infra red light is sent through the tube and the amount of light passing though the tube is measured at different wavelengths. Each milk sample was additionally analysed for fat, lactose, protein and dry matter. } \examples{ data(NIRmilk) } \keyword{datasets} doBy/man/linest-get.Rd0000755000176200001440000000226314551451413014275 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/linest_get.R \name{linest-get} \alias{linest-get} \alias{get_xlevels} \alias{get_xlevels.default} \alias{get_xlevels.mer} \alias{get_xlevels.merMod} \alias{get_contrasts} \alias{get_contrasts.default} \alias{get_contrasts.merMod} \alias{set_xlevels} \alias{get_vartypes} \alias{set_covariate_val} \alias{get_X} \alias{get_X.default} \alias{get_X.merMod} \title{Auxillary functions for computing lsmeans, contrasts etc} \usage{ get_xlevels(obj) \method{get_xlevels}{default}(obj) \method{get_xlevels}{mer}(obj) \method{get_xlevels}{merMod}(obj) get_contrasts(obj) \method{get_contrasts}{default}(obj) \method{get_contrasts}{merMod}(obj) set_xlevels(xlev, at) get_vartypes(obj) set_covariate_val(xlev, covariateVal) get_X(obj, newdata, at = NULL) \method{get_X}{default}(obj, newdata, at = NULL) \method{get_X}{merMod}(obj, newdata, at = NULL) } \arguments{ \item{obj}{An R object} \item{xlev}{FIXME: to be described} \item{at}{FIXME: to be described} \item{covariateVal}{FIXME: to be described} \item{newdata}{FIXME: to be described} } \description{ Auxillary functions for computing lsmeans, contrasts etc. } doBy/man/add_int.Rd0000644000176200001440000000061114762143004013612 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/linear_modelling.R \name{add_int} \alias{add_int} \title{Add interaction columns to data frame} \usage{ add_int(.data, .formula) } \arguments{ \item{.data}{dataframe} \item{.formula}{right hand sided formula} } \value{ dataframe } \description{ Add interaction columns to data frame } \author{ Søren Højsgaard } doBy/man/renameCol.Rd0000755000176200001440000000166314634661533014142 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/doby_utilities.R \name{renameCol} \alias{renameCol} \title{Rename columns in a matrix or a dataframe.} \usage{ renameCol(indata, src, tgt) } \arguments{ \item{indata}{A dataframe or a matrix} \item{src}{Source: Vector of names of columns in \code{indata} to be renamed. Can also be a vector of column numbers.} \item{tgt}{Target: Vector with corresponding new names in the output.} } \value{ A dataframe if \code{indata} is a dataframe; a matrix in \code{indata} is a matrix. } \description{ Rename columns in a matrix or a dataframe. } \examples{ renameCol(CO2, 1:2, c("kk", "ll")) renameCol(CO2, c("Plant", "Type"), c("kk", "ll")) # These fail - as they should: # renameCol(CO2, c("Plant", "Type", "conc"), c("kk", "ll")) # renameCol(CO2, c("Plant", "Type", "Plant"), c("kk", "ll")) } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{utitlities} doBy/man/split_byrow_bycol.Rd0000644000176200001440000000173515030273443015765 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data_split_byrow_bycol.R \name{split_byrow_bycol} \alias{split_byrow_bycol} \alias{split_bycol} \alias{split_byrow} \title{Split matrix or dataframe into list} \usage{ split_bycol(x, idx = NULL, as.list = FALSE) split_byrow(x, idx = NULL) } \arguments{ \item{x}{Matrix or dataframe.} \item{idx}{Index to split by. If NULL, split by columns or rows.} \item{as.list}{If TRUE, return list of dataframes. If FALSE, return list of matrices.} } \description{ Split matrix or dataframe into list by columns or by rows } \examples{ x <- mtcars[1:3, 1:6] x |> split_bycol() x |> split_bycol(as.list=TRUE) x |> split_bycol(as.list=FALSE) x |> split_bycol(idx=c(1,1,1,2,2,3,3,3)) ## x |> split_bycol(idx=c(1,1,7,2,2,3,3,3)) ## Gives error x <- mtcars[1:6, 1:6] x |> split_byrow() x |> split_byrow(idx=c(1,1,2,2)) m <- as.matrix(x) u <- x |> split_byrow(idx=c(1,1,2,2)) y <- m |> split_byrow(idx=c(1,1,2,2)) } doBy/man/sub_seq.Rd0000644000176200001440000000125515030071003013642 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sequence_utilities.R \name{sub_seq} \alias{sub_seq} \alias{subSeq} \title{Find sub-sequences of identical elements in a vector.} \usage{ sub_seq(x, item = NULL) subSeq(x, item = NULL) } \arguments{ \item{x}{An atomic vector or a factor.} \item{item}{Optionally a specific value to look for in \code{x}.} } \value{ A dataframe. } \description{ Find sub-sequences of identical elements in a vector. } \examples{ x <- c(1, 1, 1, 0, 0, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 3) sub_seq(x) sub_seq(x, item=1) } \seealso{ \code{\link{rle}} } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{utilities} doBy/man/fev.Rd0000644000176200001440000000150114551451413012771 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{fev} \alias{fev} \title{Forced expiratory volume in children} \format{ A data frame with 654 observations on the following 5 variables. \describe{ \item{\code{Age}}{Age in years.} \item{\code{FEV}}{Forced expiratory volume in liters per second.} \item{\code{Ht}}{Height in centimeters.} \item{\code{Gender}}{Gender.} \item{\code{Smoke}}{Smoking status.} } } \usage{ fev } \description{ Dataset to examine if respiratory function in children was influenced by smoking. } \examples{ data(fev) summary(fev) } \references{ I. Tager and S. Weiss and B. Rosner and F. Speizer (1979). Effect of Parental Cigarette Smoking on the Pulmonary Function of Children. American Journal of Epidemiology. 110:15-26 } \keyword{datasets} doBy/man/descStat.Rd0000755000176200001440000000124714634661533014005 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/doby_utilities.R \name{descStat} \alias{descStat} \title{Computing simple descriptive statistics of a numeric vector.} \usage{ descStat(x, na.rm = TRUE) } \arguments{ \item{x}{A numeric vector} \item{na.rm}{Should missing values be removed} } \value{ A vector with named elements. } \description{ Computing simple descriptive statistics of a numeric vector - not unlike what proc means of SAS does } \examples{ x <- c(1, 2, 3, 4, NA, NaN) descStat(x) } \seealso{ \code{\link{summaryBy}}, \code{\link{summary_by}} } \author{ Gregor Gorjanc; \email{gregor.gorjanc@bf.uni-lj.si} } \keyword{utilities} doBy/man/pick_elements.Rd0000644000176200001440000000272215011613250015030 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pick.R \name{pick_elements} \alias{pick_elements} \alias{pick1} \alias{pick2} \title{Extract (pick) elements without using brackets} \usage{ pick1(x, which) pick2(x, which) } \arguments{ \item{x}{A list, data frame, or vector.} \item{which}{The index or name of the element(s) to extract.} } \value{ \itemize{ \item \code{pick1()} returns a subset of \code{x}. \item \code{pick2()} returns a single element from \code{x}. } } \description{ Extract (pick) elements without using brackets so that elements can be picked out as part of a pipe workflow. } \details{ These two helper functions extract elements from lists, data frames, or vectors. They are simple wrappers for the standard bracket operators in R: \itemize{ \item \code{pick1()} uses single brackets (\code{[}) and returns a \strong{subset}. \item \code{pick2()} uses double brackets (\code{[[}) and returns the \strong{element itself}. } These are safer and more flexible than \code{$}, especially when used with the base R pipe (\verb{|>}) or in functional programming. } \examples{ lst <- list(a = 1:3, b = 4:6) # Without pipe pick1(lst, "a") # List with one element pick2(lst, "a") # Just the vector 1:3 # With base R pipe lst |> pick1("a") lst |> pick2("a") df <- data.frame(x = 1:5, y = letters[1:5]) df |> pick1("y") # Returns a data frame with column 'y' df |> pick2("y") # Returns column 'y' as a character vector } doBy/man/crickets.Rd0000644000176200001440000000213715030274224014022 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{crickets} \alias{crickets} \title{crickets data} \format{ This data frame contains: \describe{ \item{species:}{Species, (exis, nius), see details} \item{temp:}{temperature} \item{pps:}{pulse per second} } } \usage{ crickets } \description{ Mating songs of male tree crickets. } \details{ Walker (1962) studied the mating songs of male tree crickets. Each wingstroke by a cricket produces a pulse of song, and females may use the number of pulses per second to identify males of the correct species. Walker (1962) wanted to know whether the chirps of the crickets Oecanthus exclamationis (abbreviated exis) and Oecanthus niveus (abbreviated nius) had different pulse rates. See the biostathandbook for details. (The abbreviations are made from the the first two and last two letters of the species.) Walker measured the pulse rate of the crickets (variable \code{pps}) at a variety of temperatures (\code{temp}): } \examples{ data(crickets) coplot(pps ~ temp | species, data=crickets) } \keyword{datasets} doBy/man/section_fun.Rd0000644000176200001440000000610415030023277014525 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/functional_section_fun.R \name{section_fun} \alias{section_fun} \alias{set_default} \alias{section_fun_sub} \alias{section_fun_env} \alias{get_section} \alias{get_fun} \title{Section a function and set default values in function} \usage{ set_default(fun, nms, vls = NULL) section_fun(fun, nms, vls = NULL, method = "args") section_fun_sub(fun, nms, vls = NULL, envir = parent.frame()) section_fun_env(fun, nms, vls = NULL) get_section(object) get_fun(object) } \arguments{ \item{fun}{Function to be sectioned} \item{nms}{Either a named list of the form name=value where each name is the name of an argument of the function (in which case \code{vls} is ignored) or a character vector of names of arguments.} \item{vls}{A vector or list of values of the arguments} \item{method}{One of the following: 1) "args" (default); based on substituting fixed values into the function argument list as default values). For backward compatibility can also be "def". 2) "body" for substituting fixed values into the function body. For backward compatibility can also be "sub". 3) "env": (for environment); using an auxillary argument for storing sectioned values.} \item{envir}{Environment} \item{object}{An object from section_fun (a scaffold object).} } \value{ A new function: The input function \code{fun} but with certain arguments fixed at specific values. } \description{ Section a functions domain by fixing certain arguments of a function call. } \details{ Let E be a subset of the cartesian product X x Y where X and Y are some sets. Consider a function f(x,y) defined on E. Then for any x in X, the section of E defined by x (denoted Ex) is the set of $y$s in Y such that (x, y) is in E. Correspondingly, the section of f(x,y) defined by x is the function $f_x$ defined on Ex given by $f_x(y)=f(x,y)$. \code{section_fun} is a wrapper for calling \code{set_default} (default method), \code{section_fun_env} or \code{section_fun_sub}. Notice that creating a sectioned function with \code{section_fun_sub} can be time consuming. } \examples{ f <- function(x, y){x + y} f_ <- section_fun(f, list(y = 10), method="args") ## "def"" is default f_ <- section_fun(f, nms="y", vls=10, method="args") ## SAME AS ABOVE f_ f_(x=1) f_ <- section_fun(f, list(y = 10), method="body") ## f_ <- section_fun(f, nms="y", vls=10, method="body") ## SAME AS ABOVE f_ f_(x=1) f_ <- section_fun(f, list(y = 10), method="env") f_ <- section_fun(f, nms="y", vls=10, method="env") ## SAME AS ABOVE f_ f_(x=1) get_section(f_) get_fun(f_) ## With more complicated values: g <- function(A, B) { A + B } g_ <- section_fun(g, list(A = matrix(1:4, nrow=2))) g_ <- section_fun(g, "A", list(matrix(1:4, nrow=2))) g_(diag(1, 2)) g_ <- section_fun(g, list(A = matrix(1:4, nrow=2))) ## Using built in function set.seed(123) rnorm5 <- section_fun(rnorm, list(n=5)) rnorm5(0, 1) set.seed(123) rnorm(5) } \seealso{ \code{\link[=bquote_fun_list]{bquote_fun_list()}} } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} based on code adapted from the curry package. } doBy/man/by-split.Rd0000644000176200001440000000354715024542034013764 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/by_splitBy.R \name{by-split} \alias{by-split} \alias{split_by} \alias{splitBy} \alias{head.splitByData} \alias{tail.splitByData} \alias{split_by.legacy} \alias{splitBy.legacy} \title{Split a data frame into groups defined by variable(s)} \usage{ split_by(data., ..., omit = TRUE) splitBy(formula, data, omit = TRUE) \method{head}{splitByData}(x, n = 6L, ...) \method{tail}{splitByData}(x, n = 6L, ...) split_by.legacy(data, formula, drop = TRUE) splitBy.legacy(formula, data = parent.frame(), drop = TRUE) } \arguments{ \item{data.}{A data frame (or tibble) to split} \item{...}{Variables defining the groups} \item{omit}{If TRUE (default), group-defining variables are omitted in each split group} \item{formula}{A right hand sided formula (for the old interface)} \item{data}{A data frame (for the old interface)} \item{x}{A splitByData object.} \item{n}{An integer vector.} \item{drop}{Obsolete} } \value{ An object of class \"splitByData\" (a named list with group attributes) } \description{ Split a dataframe according to the levels of variables in the dataframe. Uses vparse() to interpret flexible input. } \examples{ split_by(CO2, ~Treatment+Type) split_by(CO2, Treatment, Type) split_by(CO2, c("Treatment", "Type")) split_by(CO2, "Treatment", "Type") x <- split_by(CO2, "Treatment", "Type") head(x, 3) tail(x, 3) ## Via wrapper: foo2 <- function(x) { x <- rlang::enquo(x) split_by(CO2, !!x) } foo2(~Treatment) ## The "Old" interface splitBy(~Treatment + Type, CO2) splitBy(~Treatment + Type, data=CO2) splitBy(c("Treatment", "Type"), data=CO2) } \seealso{ \code{\link{orderBy}}, \code{\link{order_by}}, \code{\link{summaryBy}}, \code{\link{summary_by}}, \code{\link{transformBy}}, \code{\link{transform_by}} } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{utilities} doBy/man/data-wine.Rd0000644000176200001440000000257014760044654014100 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{data-wine} \alias{data-wine} \alias{wine} \title{Chemical composition of wine} \format{ A data frame with 178 observations on the following 14 variables. \describe{ \item{\code{Cult}}{a factor with levels \code{v1} \code{v2} \code{v3}: 3 different graph varieties} \item{\code{Alch}}{Alcohol} \item{\code{Mlca}}{Malic acid} \item{\code{Ash}}{Ash} \item{\code{Aloa}}{Alcalinity of ash} \item{\code{Mgns}}{Magnesium} \item{\code{Ttlp}}{Total phenols} \item{\code{Flvn}}{Flavanoids} \item{\code{Nnfp}}{Nonflavanoid phenols} \item{\code{Prnt}}{Proanthocyanins} \item{\code{Clri}}{Color intensity} \item{\code{Hue}}{Hue} \item{\code{Oodw}}{OD280/OD315 of diluted wines} \item{\code{Prln}}{Proline} } } \source{ Frank, A. & Asuncion, A. (2010). UCI Machine Learning Repository \url{https://archive.ics.uci.edu/ml/}. Irvine, CA: University of California, School of Information and Computer Science. } \usage{ data(wine) } \description{ Using chemical analysis determine the origin of wines } \details{ Data comes from the UCI Machine Learning Repository. The grape variety \code{Cult} is the class identifier. } \examples{ data(wine) ## maybe str(wine) ; plot(wine) ... } \references{ See references at \url{https://archive.ics.uci.edu/ml/datasets/Wine/} } \concept{dataset} \keyword{datasets} doBy/man/is_estimable.Rd0000644000176200001440000000217014746511676014672 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/linest_utilities.R \name{is_estimable} \alias{is_estimable} \title{Determines if contrasts are estimable.} \usage{ is_estimable(K, null.basis) } \arguments{ \item{K}{A matrix.} \item{null.basis}{A basis for a null space (can be found with \code{null_basis()}).} } \value{ A logical vector. } \description{ Determines if contrasts are estimable, that is, if the contrasts can be written as a linear function of the data. } \details{ Consider the setting \eqn{E(Y)=Xb}. A linear function of \eqn{b}, say \eqn{l'b} is estimable if and only if there exists an \eqn{r} such that \eqn{r'X=l'} or equivalently \eqn{l=X'r}. Hence \eqn{l} must be in the column space of \eqn{X'}, i.e. in the orthogonal complement of the null space of \eqn{X}. Hence, with a basis \eqn{B} for the null space, \code{is_estimable()} checks if each row \eqn{l} of the matrix \eqn{K} is perpendicular to each column basis vector in \eqn{B}. } \references{ \url{http://web.mit.edu/18.06/www/Essays/newpaper_ver3.pdf} } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{utilities} doBy/man/vcheck.Rd0000644000176200001440000000056515024532741013465 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/vtools.R \name{vcheck} \alias{vcheck} \title{Check if variables exist in a data frame} \usage{ vcheck(df, ...) } \arguments{ \item{df}{A data frame} \item{...}{Variables to check} } \value{ TRUE if all variables exist, otherwise error } \description{ Check if variables exist in a data frame } doBy/man/by-lmby.Rd0000644000176200001440000000253415030273442013570 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/by_lmBy.R \name{by-lmby} \alias{by-lmby} \alias{lm_by} \alias{lmBy} \alias{coef.lmBy} \alias{coef.summary_lmBy} \alias{summary.lmBy} \alias{fitted.lmBy} \alias{residuals.lmBy} \alias{getBy} \title{List of lm objects with a common model} \usage{ lm_by(data., formula., id = NULL, ...) lmBy(formula., data., id = NULL, ...) } \arguments{ \item{data.}{A dataframe} \item{formula.}{A linear model formula object of the form \code{y ~ x1 + ... + xn | g1 + ... + gm}. In the formula object, \code{y} represents the response, \verb{x1, ..., xn} the covariates, and the grouping factors specifying the partitioning of the data according to which different lm fits should be performed.} \item{id}{A formula describing variables from data which are to be available also in the output.} \item{\dots}{Additional arguments passed on to \code{lm()}.} } \value{ A list of lm fits. } \description{ The data is split into strata according to the levels of the grouping factors and individual lm fits are obtained for each stratum. } \examples{ lm_lst <- lmBy(1 / uptake ~ log(conc) | Treatment, data=CO2) coef(lm_lst) fitted(lm_lst) residuals(lm_lst) summary(lm_lst) coef(summary(lm_lst)) coef(summary(lm_lst), simplify=TRUE) } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{models} doBy/man/by-sample.Rd0000755000176200001440000000240614551451413014112 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/by_sampleBy.R \name{by-sample} \alias{by-sample} \alias{sample_by} \alias{sampleBy} \title{Sampling from a data frame} \usage{ sample_by(data, formula, frac = 0.1, replace = FALSE, systematic = FALSE) sampleBy( formula, frac = 0.1, replace = FALSE, data = parent.frame(), systematic = FALSE ) } \arguments{ \item{data}{A data frame.} \item{formula}{A formula defining the grouping of the data frame.} \item{frac}{The part of data to be sampled.} \item{replace}{Is the sampling with replacement.} \item{systematic}{Should sampling be systematic.} } \value{ A dataframe. } \description{ A data frame is split according to some variables in a formula, and a sample of a certain fraction of each is drawn. } \details{ If systematic=FALSE (default) then frac gives the fraction of data sampled. If systematic=TRUE and frac=.2 then every 1/.2 i.e. every 5th observation is taken out. } \examples{ data(dietox) sampleBy(formula = ~ Evit + Cu, frac=.1, data = dietox) } \seealso{ \code{\link{orderBy}}, \code{\link{order_by}}, \code{\link{splitBy}}, \code{\link{split_by}}, \code{\link{summaryBy}}, \code{\link{summary_by}}, \code{\link{transformBy}}, \code{\link{transform_by}} } \keyword{utilities} doBy/man/head_matrix.Rd0000644000176200001440000000064315015752671014512 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/pick.R \name{head_matrix} \alias{head_matrix} \alias{head2} \alias{tail2} \title{head and tail for matrices} \usage{ head2(x, n = 6, m = n) tail2(x, n = 6, m = n) } \arguments{ \item{x}{matrix} \item{n, m}{number of rows and columns} } \description{ head and tail for matrices } \examples{ M <- matrix(1:20, nrow=4) head2(M) head2(M, 2) } doBy/man/carcass.Rd0000644000176200001440000000377214551451413013644 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/DATA_doby.R \docType{data} \name{carcass} \alias{carcass} \alias{carcassall} \title{Lean meat contents of 344 pig carcasses} \format{ carcassall: A data frame with 344 observations on the following 17 variables. \describe{ \item{\code{weight}}{Weight of carcass} \item{\code{lengthc}}{Length of carcass from back toe to head (when the carcass hangs in the back legs)} \item{\code{lengthf}}{Length of carcass from back toe to front leg (that is, to the shoulder)} \item{\code{lengthp}}{Length of carcass from back toe to the pelvic bone} \item{\code{Fat02, Fat03, Fat11, Fat12, Fat13, Fat14, Fat16}}{Thickness of fat layer at different locations on the back of the carcass (FatXX refers to thickness at (or rather next to) rib no. XX. Notice that 02 is closest to the head} \item{\code{Meat11, Meat12, Meat13}}{Thickness of meat layer at different locations on the back of the carcass, see description above} \item{\code{LeanMeat}}{Lean meat percentage determined by dissection} \item{\code{slhouse}}{Slaughter house; a factor with levels \code{slh1} and \code{slh2}.} \item{\code{sex}}{Sex of the pig; a factor with levels \code{castrate} and \code{female}.} \item{\code{size}}{Size of the carcass; a factor with levels \code{normal} and \code{large}. Here, \code{normal} refers to carcass weight under 80 kg; \code{large} refers to carcass weights between 80 and 110 kg.} } } \source{ Busk, H., Olsen, E. V., Brøndum, J. (1999) Determination of lean meat in pig carcasses with the Autofom classification system, Meat Science, 52, 307-314 } \usage{ carcass } \description{ Measurement of lean meat percentage of 344 pig carcasses together with auxiliary information collected at three Danish slaughter houses } \details{ : Notice that there were slaughtered large pigs only at one slaughter house. } \note{ carcass: Contains only the variables Fat11, Fat12, Fat13, Meat11, Meat12, Meat13, LeanMeat } \examples{ data(carcass) head(carcass) } \keyword{datasets} doBy/man/ls-means.Rd0000755000176200001440000001004114746514105013736 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/linest_LSmeans.R \name{ls-means} \alias{ls-means} \alias{LSmeans} \alias{LSmeans.default} \alias{LSmeans.lmerMod} \alias{popMeans} \alias{popMeans.default} \alias{popMeans.lmerMod} \title{Compute LS-means (aka population means or marginal means)} \usage{ LSmeans(object, effect = NULL, at = NULL, level = 0.95, ...) \method{LSmeans}{default}(object, effect = NULL, at = NULL, level = 0.95, ...) \method{LSmeans}{lmerMod}(object, effect = NULL, at = NULL, level = 0.95, adjust.df = TRUE, ...) popMeans(object, effect = NULL, at = NULL, level = 0.95, ...) \method{popMeans}{default}(object, effect = NULL, at = NULL, level = 0.95, ...) \method{popMeans}{lmerMod}(object, effect = NULL, at = NULL, level = 0.95, adjust.df = TRUE, ...) } \arguments{ \item{object}{Model object} \item{effect}{A vector of variables. For each configuration of these the estimate will be calculated.} \item{at}{A list of values of covariates (including levels of some factors) to be used in the calculations} \item{level}{The level of the (asymptotic) confidence interval.} \item{\dots}{Additional arguments; currently not used.} \item{adjust.df}{Should denominator degrees of freedom be adjusted?} } \value{ A dataframe with results from computing the contrasts. } \description{ LS-means (least squares means, also known as population means and as marginal means) for a range of model types. } \details{ There are restrictions on the formulas allowed in the model object. For example having \code{y ~ log(x)} will cause an error. Instead one must define the variable \code{logx = log(x)} and do \code{y ~ logx}. } \note{ \code{LSmeans} and \code{popMeans} are synonymous. } \section{Warning}{ Notice that \code{LSmeans} and \code{LE_matrix} fails if the model formula contains an offset (as one would have in connection with e.g. Poisson regression. } \examples{ ## Two way anova: data(warpbreaks) m0 <- lm(breaks ~ wool + tension, data=warpbreaks) m1 <- lm(breaks ~ wool * tension, data=warpbreaks) LSmeans(m0) LSmeans(m1) ## same as: K <- LE_matrix(m0);K linest(m0, K) K <- LE_matrix(m1);K linest(m1, K) LE_matrix(m0, effect="wool") LSmeans(m0, effect="wool") LE_matrix(m1, effect="wool") LSmeans(m1, effect="wool") LE_matrix(m0, effect=c("wool", "tension")) LSmeans(m0, effect=c("wool", "tension")) LE_matrix(m1, effect=c("wool", "tension")) LSmeans(m1, effect=c("wool", "tension")) ## Regression; two parallel regression lines: data(Puromycin) m0 <- lm(rate ~ state + log(conc), data=Puromycin) ## Can not use LSmeans / LE_matrix here because of ## the log-transformation. Instead we must do: Puromycin$lconc <- log( Puromycin$conc ) m1 <- lm(rate ~ state + lconc, data=Puromycin) LE_matrix(m1) LSmeans(m1) LE_matrix(m1, effect="state") LSmeans(m1, effect="state") LE_matrix(m1, effect="state", at=list(lconc=3)) LSmeans(m1, effect="state", at=list(lconc=3)) ## Non estimable contrasts ## ## Make balanced dataset dat.bal <- expand.grid(list(AA=factor(1:2), BB=factor(1:3), CC=factor(1:3))) dat.bal$y <- rnorm(nrow(dat.bal)) ## ## Make unbalanced dataset # 'BB' is nested within 'CC' so BB=1 is only found when CC=1 # and BB=2,3 are found in each CC=2,3,4 dat.nst <- dat.bal dat.nst$CC <-factor(c(1, 1, 2, 2, 2, 2, 1, 1, 3, 3, 3, 3, 1, 1, 4, 4, 4, 4)) mod.bal <- lm(y ~ AA + BB * CC, data=dat.bal) mod.nst <- lm(y ~ AA + BB : CC, data=dat.nst) LSmeans(mod.bal, effect=c("BB", "CC")) LSmeans(mod.nst, effect=c("BB", "CC")) LSmeans(mod.nst, at=list(BB=1, CC=1)) LSmeans(mod.nst, at=list(BB=1, CC=2)) ## Above: NA's are correct; not an estimable function if( require( lme4 )){ warp.mm <- lmer(breaks ~ -1 + tension + (1|wool), data=warpbreaks) LSmeans(warp.mm, effect="tension") class(warp.mm) fixef(warp.mm) coef(summary(warp.mm)) vcov(warp.mm) if (require(pbkrtest)) vcovAdj(warp.mm) } LSmeans(warp.mm, effect="tension") } \seealso{ \code{\link{LE_matrix}}, \code{\link{linest}} } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{utilities} doBy/man/bquote_fun_list.Rd0000644000176200001440000000147215030273443015420 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/functional_bquote_list.R \name{bquote_fun_list} \alias{bquote_fun_list} \title{Backquote a list of functions} \usage{ bquote_fun_list(fun_list) } \arguments{ \item{fun_list}{List of functions} } \description{ Backquote a list of functions } \examples{ ## Evaluate a list of functions f1 <- function(x){x + 1} f2 <- function(x){x + 8} f1_ <- set_default(f1, list(x=10)) f2_ <- set_default(f2, list(x=10)) f1_(); f2_() fn_list <- list(f1_, f2_) fn_list_ <- bquote_fun_list(fn_list) eval(fn_list[[1]]) ## No sapply(fn_list, eval) ## No eval(fn_list_[[1]]) ## Yes sapply(fn_list_, eval) ## Yes } \seealso{ \code{\link[base:bquote]{base::bquote()}}, \code{\link[=set_default]{set_default()}}, \code{\link[=section_fun]{section_fun()}} } doBy/man/tidy-linest.Rd0000644000176200001440000000112115030273443014452 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/linest_tidy.R \name{tidy-linest} \alias{tidy-linest} \alias{tidy.linest_class} \title{Tidy a linest object} \usage{ \method{tidy}{linest_class}(x, conf.int = FALSE, conf.level = 0.95, ...) } \arguments{ \item{x}{A 'linest_class' object (produced by \code{linest} methods).} \item{conf.int}{Should confidence intervals be added.} \item{conf.level}{Desired confidence level.} \item{\dots}{Additional arguments; currently not used.} } \description{ Tidy summarizes information about the components of the object. } doBy/man/esticon.Rd0000755000176200001440000001112114551451413013657 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/esticon.R \name{esticon} \alias{esticon} \alias{coef.esticon_class} \alias{summary.esticon_class} \alias{confint.esticon_class} \alias{vcov.esticon_class} \title{Contrasts for lm, glm, lme, and geeglm objects} \usage{ esticon(obj, L, beta0, conf.int = TRUE, level = 0.95, joint.test = FALSE, ...) \method{coef}{esticon_class}(object, ...) \method{summary}{esticon_class}(object, ...) \method{confint}{esticon_class}(object, parm, level = 0.95, ...) \method{vcov}{esticon_class}(object, ...) } \arguments{ \item{obj}{Regression object (of type lm, glm, lme, geeglm).} \item{L}{Matrix (or vector) specifying linear functions of the regression parameters (one linear function per row). The number of columns must match the number of fitted regression parameters in the model. See 'details' below.} \item{beta0}{A vector of numbers} \item{conf.int}{TRUE} \item{level}{The confidence level} \item{joint.test}{Logical value. If TRUE a 'joint' Wald test for the hypothesis L beta = beta0 is made. Default is that the 'row-wise' tests are made, i.e. (L beta)i=beta0i. If joint.test is TRUE, then no confidence interval etc. is calculated.} \item{\dots}{Additional arguments; currently not used.} \item{object}{An \code{esticon_class} object.} \item{parm}{a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.} } \value{ Returns a matrix with one row per linear function. Columns contain estimated coefficients, standard errors, t values, degrees of freedom, two-sided p-values, and the lower and upper endpoints of the 1-alpha confidence intervals. } \description{ Computes linear functions (i.e. weighted sums) of the estimated regression parameters. Can also test the hypothesis, that such a function is equal to a specific value. } \details{ Let the estimated parameters of the model be \deqn{\beta_1, \beta_2, \dots, \beta_p} A linear function of the estimates is of the form \deqn{l=\lambda_1 \beta_1+\lambda_2 \beta_2+ \dots+\lambda_p \beta_p} where \eqn{\lambda_1, \lambda_2, \dots,\lambda_p} is specified by the user. The esticon function calculates l, its standard error and by default also a 95 pct confidence interval. It is sometimes of interest to test the hypothesis \eqn{H_0: l=\beta_0} for some value \eqn{\beta_0} given by the user. A test is provided for the hypothesis \eqn{H_0: l=0} but other values of \eqn{\beta_0} can be specified. In general, one can specify r such linear functions at one time by specifying L to be an \eqn{r\times p} matrix where each row consists of p numbers \eqn{\lambda_1,\lambda_2,\dots, \lambda_p}. Default is then that \eqn{\beta_0} is a p vector of 0s but other values can be given. It is possible to test simultaneously that all specified linear functions are equal to the corresponding values in \eqn{\beta_0}. For computing contrasts among levels of a single factor, 'contrast.lm' may be more convenient. } \examples{ data(iris) lm1 <- lm(Sepal.Length ~ Sepal.Width + Species + Sepal.Width : Species, data=iris) ## Note that the setosa parameters are set to zero coef(lm1) ## Estimate the intercept for versicolor lambda1 <- c(1, 0, 1, 0, 0, 0) esticon(lm1, L=lambda1) ## Estimate the difference between versicolor and virgica intercept ## and test if the difference is 1 lambda2 <- c(0, 1, -1, 0, 0, 0) esticon(lm1, L=lambda2, beta0=1) ## Do both estimates at one time esticon(lm1, L=rbind(lambda1, lambda2), beta0=c(0, 1)) ## Make a combined test for that the difference between versicolor and virgica intercept ## and difference between versicolor and virginica slope is zero: lambda3 <- c(0, 0, 0, 0, 1, -1) esticon(lm1, L=rbind(lambda2, lambda3), joint.test=TRUE) # Example using esticon on coxph objects (thanks to Alessandro A. Leidi). # Using dataset 'veteran' in the survival package # from the Veterans' Administration Lung Cancer study if (require(survival)){ data(veteran) sapply(veteran, class) levels(veteran$celltype) attach(veteran) veteran.s <- Surv(time, status) coxmod <- coxph(veteran.s ~ age + celltype + trt, method='breslow') summary(coxmod) # compare a subject 50 years old with celltype 1 # to a subject 70 years old with celltype 2 # both subjects on the same treatment AvB <- c(-20, -1, 0, 0, 0) # compare a subject 40 years old with celltype 2 on treat=0 # to a subject 35 years old with celltype 3 on treat=1 CvB <- c(5, 1, -1, 0, -1) est <- esticon(coxmod, L=rbind(AvB, CvB)) est ##exp(est[, c(2, 7, 8)]) } } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{utilities} doBy/man/formula_ops.Rd0000644000176200001440000000446514634265101014552 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/formula_ops.R \name{formula_ops} \alias{formula_ops} \alias{formula_add_str} \alias{formula_add} \alias{formula_poly} \alias{formula_nth} \alias{formula_to_interaction_matrix} \alias{formula_chr_to_form} \alias{to_str} \alias{terms_labels} \alias{simplify_rhs} \alias{simplify_rhs.formula} \alias{simplify_rhs.character} \alias{as_rhs_frm} \alias{as_lhs_frm} \alias{as_rhs_chr} \alias{as_lhs_chr} \alias{unique_formula} \title{Formula operations and coercion.} \usage{ formula_add_str(frm1, terms, op = "+") formula_add(frm1, frm2) formula_poly(chr1, n, noint = FALSE, y = NULL) formula_nth(frm1, n) formula_to_interaction_matrix(frm1) formula_chr_to_form(rhs, lhs = character(0)) to_str(chr1, collapse = "+") terms_labels(frm1) simplify_rhs(object) \method{simplify_rhs}{formula}(object) \method{simplify_rhs}{character}(object) as_rhs_frm(object) as_lhs_frm(object) as_rhs_chr(object, string = FALSE) as_lhs_chr(object, string = FALSE) unique_formula(list_of_formulas) } \arguments{ \item{frm1, frm2}{Formulas to be coerced to character vectors.} \item{terms}{Character string.} \item{op}{Either "+" (default) or "-".} \item{chr1}{Character vector to be coerced to formulas.} \item{n}{Positive integer.} \item{noint}{Boolean.} \item{y}{Response} \item{rhs, lhs}{right-hand-side and left-hand-side for formula (as characters)} \item{collapse}{Character to use as separator.} \item{object}{Character vector or formula.} \item{string}{Boolean.} \item{list_of_formulas}{list of formulas} } \description{ Formula operations and coercion as a supplement to \code{update.formula()} } \examples{ formula_poly("z", 2) formula_poly("z", 2, noint=TRUE) as_rhs_chr(c("a", "b", "z")) as_rhs_chr(c("a*b", "z")) as_rhs_chr(y~a+b+z) as_rhs_chr(y~a+b+z, string=TRUE) as_rhs_chr(y~a+b+z) as_rhs_chr(y~a*b+z) as_rhs_chr(y~a*b+z, string=TRUE) as_lhs_chr(y~a*b+z) as_lhs_chr(log(y)~a*b+z) ## Not what one might expect as_lhs_chr(cbind(y, u)~a*b+z) ## Not what one might expect formula_chr_to_form(c("a*b", "z")) formula_chr_to_form(c("a*b", "z"), "y") formula_chr_to_form(c("a*b", "z"), "log(y)") formula_add(y~a*b+z, ~-1) formula_add(y~a*b+z, ~a:b) formula_add_str(y~x1 + x2, "x3") formula_add_str(y~x1 + x2, "x1") formula_add_str(y~x1 + x2, "x1", op="-") } doBy/man/linest-matrix.Rd0000755000176200001440000000475414551451413015031 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/linest_matrix.R \name{linest-matrix} \alias{linest-matrix} \alias{LE_matrix} \alias{LE_matrix.default} \alias{aggregate_linest_list} \alias{get_linest_list} \title{Linear estimates matrix} \usage{ LE_matrix(object, effect = NULL, at = NULL) \method{LE_matrix}{default}(object, effect = NULL, at = NULL) aggregate_linest_list(linest_list) get_linest_list(object, effect = NULL, at = NULL) } \arguments{ \item{object}{Model object} \item{effect}{A vector of variables. For each configuration of these the estimate will be calculated.} \item{at}{Either NULL, a list or a dataframe. 1) If a list, then the list must consist of covariates (including levels of some factors) to be used in the calculations. 2) If a dataframe, the dataframe is split rowwise and the function is invoked on each row.} \item{linest_list}{Linear estimate list (as generated by \code{get_linest_list}).} } \description{ Generate matrix specifying linear estimate. } \details{ Check this } \examples{ ## Two way anova: data(warpbreaks) ## An additive model m0 <- lm(breaks ~ wool + tension, data=warpbreaks) ## Estimate mean for each wool type, for tension="M": K <- LE_matrix(m0, at=list(wool=c("A", "B"), tension="M")) K ## Vanilla computation: K \%*\% coef(m0) ## Alternative; also providing standard errors etc: linest(m0, K) esticon(m0, K) ## Estimate mean for each wool type when averaging over tension; # two ways of doing this K <- LE_matrix(m0, at=list(wool=c("A", "B"))) K K <- LE_matrix(m0, effect="wool") K linest(m0, K) ## The linear estimate is sometimes called to "least squares mean" # (LSmeans) or popupulation means. # Same as LSmeans(m0, effect="wool") ## Without mentioning 'effect' or 'at' an average across all #predictors are calculated: K <- LE_matrix(m0) K linest(m0, K) ## Because the design is balanced (9 observations per combination #of wool and tension) this is the same as computing the average. If #the design is not balanced, the two quantities are in general not #the same. mean(warpbreaks$breaks) ## Same as LSmeans(m0) ## An interaction model m1 <- lm(breaks ~ wool * tension, data=warpbreaks) K <- LE_matrix(m1, at=list(wool=c("A", "B"), tension="M")) K linest(m1, K) K <- LE_matrix(m1, at=list(wool=c("A", "B"))) K linest(m1, K) K <- LE_matrix(m1, effect="wool") K linest(m1, K) LSmeans(m1, effect="wool") K <- LE_matrix(m1) K linest(m1, K) LSmeans(m1) } \seealso{ \code{\link{LSmeans}}, \code{\link{linest}} } \keyword{utilities} doBy/man/response_plot.Rd0000644000176200001440000000232515030071174015105 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_functions.r \name{response_plot} \alias{response_plot} \title{Plot the response variable against the predictor variables.} \usage{ response_plot( data., formula., geoms = NULL, global_aes = NULL, plot = TRUE, nrow = NULL, ncol = NULL ) } \arguments{ \item{data.}{A data frame containing the variables in the formula.} \item{formula.}{A formula of the form y ~ x1 + x2 + ... + xn, where y is the response variable and x1, x2, ..., xn are the predictor variables. A dot as right hand side is allowed.} \item{geoms}{A list of ggplot2 geoms to be added to the plot.} \item{global_aes}{A list of global aesthetics to be added to the plot.} \item{plot}{A logical value indicating whether the plot should be displayed.} \item{nrow, ncol}{Number of rows / columns in plot.} } \value{ A list of ggplot2 plots. } \description{ Plot the response variable against the predictor variables. } \examples{ library(ggplot2) response_plot(iris, Sepal.Width ~ ., geoms=geom_point()) response_plot(iris, Sepal.Width ~ ., geoms=geom_point(), global_aes=list(color="Species")) personality |> response_plot(easygon~., geoms=geom_point(), global_aes=NULL) } doBy/man/set_list_set_matrix.Rd0000644000176200001440000000133614623475547016322 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/model_stability.r \name{set_list_set_matrix} \alias{set_list_set_matrix} \alias{set_list2matrix} \alias{matrix2set_list} \title{Matrix representatation of list of vectors and vice versa} \usage{ set_list2matrix(set_list, aggregate = FALSE) matrix2set_list(set_matrix) } \arguments{ \item{set_list}{list of vectors} \item{aggregate}{should the vectors be aggregated} \item{set_matrix}{matrix representatation} } \description{ Matrix representatation of list of vectors and vice versa } \examples{ l <- list(c(1,2,3), c(3,2,4), c(3,2,4)) m1 <- set_list2matrix(l) m1 matrix2set_list(m1) m2 <- set_list2matrix(l, aggregate=TRUE) m2 matrix2set_list(m2) } doBy/DESCRIPTION0000755000176200001440000000260515030323372012661 0ustar liggesusersPackage: doBy Version: 4.7.0 Title: Groupwise Statistics, LSmeans, Linear Estimates, Utilities Authors@R: c( person(given = "Ulrich", family = "Halekoh", email = "uhalekoh@health.sdu.dk", role = c("aut", "cph")), person(given = "Søren", family = "Højsgaard", email = "sorenh@math.aau.dk", role = c("aut", "cre", "cph")) ) Description: Utility package containing: Main categories: Working with grouped data: 'do' something to data when stratified 'by' some variables. General linear estimates. Data handling utilities. Functional programming, in particular restrict functions to a smaller domain. Miscellaneous functions for data handling. Model stability in connection with model selection. Miscellaneous other tools. Encoding: UTF-8 VignetteBuilder: knitr LazyData: true LazyDataCompression: xz URL: https://github.com/hojsgaard/doBy License: GPL (>= 2) Depends: R (>= 4.2.0), methods Imports: boot, broom, cowplot, Deriv, dplyr, ggplot2, MASS, Matrix, modelr, microbenchmark, rlang, tibble, tidyr, Suggests: geepack, knitr, lme4, markdown, multcomp, pbkrtest (>= 0.5.2), survival, testthat (>= 2.1.0) RoxygenNote: 7.3.2 NeedsCompilation: no Packaged: 2025-06-29 20:24:22 UTC; sorenh Author: Ulrich Halekoh [aut, cph], Søren Højsgaard [aut, cre, cph] Maintainer: Søren Højsgaard Repository: CRAN Date/Publication: 2025-06-29 20:50:02 UTC