Merge pull request #42 from sfcheung/devel

Fix some typos

NAMESPACE

@@ -1,7 +1,9 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(confint,std_selected)
 S3method(print,summary.std_selected)
 S3method(summary,std_selected)
+S3method(vcov,std_selected)
 export(std_selected)
 export(std_selected_boot)
 export(stdmod)

NEWS.md

@@ -5,6 +5,10 @@
  - Change the column order of the coefficient table
  in `std_selected_boot` output. Bootstrapping confidence
  intervals are placed next to parameter estimates.
+ - Add `vcov` method for `std_selected` output. If bootstrapping is used,
+ it can return the variance-covariance matrix of the bootstrap estimates.
+ - Add `confint` method for `std_selected` output. If bootstrapping is used,
+ it can return the bootstrap percentile confidence intervals if requested. 
 
 - 0.1.3
 

R/confint_stdmod.R

@@ -0,0 +1,68 @@
+#'@title confint method for \code{std_selected} class output
+#'
+#'@description Return the confidence intervals of estimates
+#' in a \code{std_selected} class output
+#'
+#'@details If bootstrapping is used to form the confidence interval,
+#' users can request the percentile confidence intervals of
+#' the bootstrap estimates.
+#'
+#'@return
+#' A matrix of the confidence intervals.
+#'
+#'@param object The output of the class \code{std_selected}.
+#'@param parm The parameters for which confidence intervals should be returned.
+#' If missing, the confidence intervals of all parameters will be
+#' returned.
+#'@param level The level of confidence. For the confidence intervals returned
+#' by [lm], default is .95. For the bootstrapping confidence
+#' intervals, default is the level used in calling
+#' [std_selected_boot]. If a level different from that in the
+#' original
+#' call is specified, `full_output` needs to be set in the call
+#' to [std_selected_boot] such that the original bootstrap output
+#' is stored.
+#'@param type The type of the confidence intervals. Default is "lm",
+#' returned by the [confint] method of [lm]. If set to "boot",
+#' the bootstrap percentile confidence intervals are
+#' returned.
+#'@param ... Arguments to be passed to \code{summary.lm}.
+#'
+#'@examples
+#' # See examples for std_selected.
+#' @export
+
+
+confint.std_selected <- function(object, parm, level = .95, type = "lm", ...) {
+ if (!(type %in% c("lm", "boot"))) {
+ stop("type must be either lm or boot.")
+ }
+ if (type == "boot") {
+ if (is.null(object$boot_est)) {
+ stop("Bootstrap estimates not available in the object.")
+ }
+ if ((level != object$conf) && is.null(object$boot_out)) {
+ stop("level is different form conf in std_selected_boot but full_output is FALSE.")
+ }
+ if (level == object$conf) {
+ out <- object$boot_ci
+ } else {
+ p <- nrow(object$boot_ci)
+ out <- t(sapply(seq_len(p), function(x) {
+ boot::boot.ci(object$boot_out, conf = level,
+ type = "perc", index = x)$percent[4:5]
+ }))
+ rownames(out) <- names(object$boot_out$t0)
+ }
+ tmp <- object
+ class(tmp) <- "lm"
+ colnames(out) <- colnames(stats::confint(tmp, level = level))
+ if (!missing(parm)) {
+ out <- out[rownames(out) %in% parm, , drop = FALSE]
+ }
+ return(out)
+ }
+ if (type == "lm") {
+ NextMethod()
+ }
+ }

R/print_summary_stdmod.R

@@ -16,20 +16,24 @@
 
 print.summary.std_selected <- function(x, ...) {
  cat("\nSelected variable(s) are centered and/or scaled")
- cat("\nVariable(s) centered:", x$centered_terms)
- cat("\nVariable(s) scaled:", x$scaled_terms)
+ cat("\n- Variable(s) centered:", x$centered_terms)
+ cat("\n- Variable(s) scaled:", x$scaled_terms)
  cat("\n")
  dat_sc <- data.frame(centered_by = x$centered_by,
  scaled_by = x$scaled_by)
  print(dat_sc)
- cat("Note:")
- cat("\nCentered by 0 = No centering; Scaled by 1: No scaling.")
+ cat("\nNote:")
+ cat("\n- Centered by 0 or NA: No centering\n- Scaled by 1 or NA: No scaling")
  if (!is.null(x$nboot)) {
- cat("\nNonparametric bootstrapping 95% confidence intervals computed.")
- cat("\nNumber of bootstrap samples is", x$nboot)
+ cat("\n- Nonparametric bootstrapping 95% confidence intervals computed.")
+ cat("\n- The number of bootstrap samples is", x$nboot)
  }
  cat("\n")
- xlm <- x
- class(xlm) <- "summary.lm"
- print(xlm, ...)
+ NextMethod()
+ if (!is.null(x$nboot)) {
+ cat("Note:")
+ cat("\n- [CI Lower, CI Upper] are bootstrap percentile confidence intervals.")
+ cat("\n- Std. Error are standard errors in the original analysis, not bootstrap SEs.")
+ cat("\n")
+ }
  }

R/std_selected.R

@@ -156,7 +156,7 @@ std_selected <- function(lm_out,
  lm_out_mod$centered_terms <- center_terms
  lm_out_mod$scaled_by <- var_b
  lm_out_mod$centered_by <- var_a
-
+ lm_out_mod$std_selected_call <- match.call()
  lm_out_mod
 
  }

R/std_selected_boot.R

@@ -12,7 +12,7 @@
 #' random number. Users are recommended to set the seed, e.g., using [set.seed()]
 #' before calling it, to ensure reproducibility.
 #'
-# The updated \code{lm} output, with the class \code{stdmod} added. It will be
+# The updated \code{lm} output, with the class \code{std_selected} added. It will be
 # treated as a usual \code{lm} object by most functions. It has these additional elements:
 #
 # - \code{scaled}: The terms scaled.
@@ -30,6 +30,9 @@
 #' @param nboot The number of bootstrap samples. Default is 100.
 #' @param boot_args A named list of arguments to be passed to [boot::boot()]. Default
 #' is `NULL`.
+#' @param save_boot_est If `TRUE`, the default, the bootstrap estimates will be saved
+#' in the element
+#' `boot_est` of the output.
 #' @param full_output Whether the full output from [boot::boot()] is return. Default is 
 #' `FALSE`.
 #'
@@ -61,6 +64,7 @@ std_selected_boot <- function(lm_out,
  conf = .95,
  nboot = 100,
  boot_args = NULL,
+ save_boot_est = TRUE,
  full_output = FALSE) {
  if (missing(lm_out)) {
  stop("The arguments lm_out cannot be empty.")
@@ -108,6 +112,11 @@ std_selected_boot <- function(lm_out,
 
  std_selected_out$boot_ci <- cis
  std_selected_out$nboot <- nboot
+ std_selected_out$conf <- conf
+ tmp <- boot_out$t
+ colnames(tmp) <- names(boot_out$t0)
+ std_selected_out$boot_est <- tmp
+ std_selected_out$std_selected_boot_call <- match.call()
  if (full_output) {
  std_selected_out$boot_out <- boot_out
  }

R/vcov_stdmod.R

@@ -0,0 +1,38 @@
+#'@title vcov method for \code{std_selected} class output
+#'
+#'@description Compute the variance-covariance matrix
+#' of estimates in a \code{std_selected} class output
+#'
+#'@details If bootstrapping is used to form the confidence interval,
+#' users can request the variance-covariance matrix of the bootstrap estimates.
+#'
+#'@return
+#' A matrix of the variances and covariances.
+#'
+#'@param object The output of the class \code{std_selected}.
+#'@param type The type of variance-covariance matrix. Default is "lm",
+#' returned by the [vcov] method of [lm]. If set to "boot",
+#' the variance-covariance matrix of the bootstrap estimates
+#' is returned.
+#'@param ... Arguments to be passed to \code{summary.lm}.
+#'
+#'@examples
+#' # See examples for std_selected.
+#' @export
+
+
+vcov.std_selected <- function(object, type = "lm", ...) {
+ if (!(type %in% c("lm", "boot"))) {
+ stop("type must be either lm or boot.")
+ }
+ if (type == "boot") {
+ if (is.null(object$boot_est)) {
+ stop("Bootstrap estimates not available in the object.")
+ }
+ out <- stats::cov(object$boot_est)
+ return(out)
+ }
+ if (type == "lm") {
+ NextMethod()
+ }
+ }

README.md

@@ -22,7 +22,7 @@ remotes::install_github("sfcheung/stdmod")
 
 # Implementation
 
-The main function, `stdmod_selected()`, accepts an `lm()`
+The main function, `std_selected()`, accepts an `lm()`
 output, standardizes variables by users, and update the
 results. If interaction terms are presented, they will be
 formed after the standardization. If bootstrapping

tests/testthat/test_stdmod_selected_boot.R

@@ -5,6 +5,8 @@ library(stdmod)
 
 set.seed(8970808)
 
+nboot <- 1000
+
 context("Check standardizing selected variables with std_selected with bootstrapping")
 
 dat <- test_x_1_w_1_v_1_cat1_n_500
@@ -27,13 +29,13 @@ lm_cxsw <- lm(dv ~ iv*mod + v1 + cat1, dplyr::mutate(dat, iv = scale(iv, scale
  mod = scale(mod, scale = sd(dat$mod), center = FALSE)[, 1]))
 
 
-stdmod_wy <- std_selected_boot(lm_raw, to_scale = ~ mod + dv, to_center = ~ mod + dv, nboot = 2000)
-stdmod_xwy <- std_selected_boot(lm_raw, to_scale = ~ mod + iv + dv, to_center = ~ iv + mod + dv, nboot = 2000)
+stdmod_wy <- std_selected_boot(lm_raw, to_scale = ~ mod + dv, to_center = ~ mod + dv, nboot = nboot)
+stdmod_xwy <- std_selected_boot(lm_raw, to_scale = ~ mod + iv + dv, to_center = ~ iv + mod + dv, nboot = nboot)
 
 stdmod2_wy <- stdmod_boot(lm_raw, x = iv, y = dv, w =mod,
- x_rescale = FALSE, y_rescale = TRUE, w_rescale = TRUE, nboot = 2000)
+ x_rescale = FALSE, y_rescale = TRUE, w_rescale = TRUE, nboot = nboot)
 stdmod2_xwy <- stdmod_boot(lm_raw, x = iv, y = dv, w =mod,
- x_rescale = TRUE, y_rescale = TRUE, w_rescale = TRUE, nboot = 2000)
+ x_rescale = TRUE, y_rescale = TRUE, w_rescale = TRUE, nboot = nboot)
 
 stdmod3_wy <- std_selected(lm_raw, to_scale = ~ mod + dv, to_center = ~ dv + mod)
 stdmod3_xwy <- std_selected(lm_raw, to_scale = ~ dv + iv + mod, to_center = ~ mod + iv + dv)

tests/testthat/test_stdmod_selected_confint.R

@@ -0,0 +1,50 @@
+library(testthat)
+library(stdmod)
+
+context("Check setting confint in summary if bootstrapping is done")
+
+dat <- test_x_1_w_1_v_1_cat1_n_500
+
+lm_raw <- lm(dv ~ iv*mod + v1 + cat1, dat)
+
+set.seed(649831074)
+stdmod_wy <- std_selected_boot(lm_raw, to_scale = ~ mod + dv, to_center = ~ mod + dv,
+ nboot = 100,
+ full_output = TRUE)
+confint_def <- confint(stdmod_wy)
+confint_boot <- confint(stdmod_wy, type = "boot")
+confint_lm <- confint(stdmod_wy, type = "lm")
+confint_boot_parm <- confint(stdmod_wy, parm = "iv:mod", type = "boot")
+confint_boot_conf90 <- confint(stdmod_wy, parm = "iv:mod", level = .90, type = "boot")
+
+stdmod_wy_lm <- std_selected(lm_raw, to_scale = ~ mod + dv, to_center = ~ mod + dv)
+confint_lm_no_boot <- confint(stdmod_wy_lm)
+
+confint_boot_check <- summary(stdmod_wy)$coefficients[, c("CI Lower", "CI Upper")]
+confint_boot_conf90_check <- boot::boot.ci(stdmod_wy$boot_out, conf = .90,
+ type = "perc", index = 7)$percent[4:5]
+
+test_that("confint_def == confint by lm", {
+ expect_equivalent(
+ confint_def, confint_lm_no_boot
+ )
+ })
+
+test_that("confint_boot == confint from summary", {
+ expect_equivalent(
+ confint_boot, confint_boot_check
+ )
+ })
+
+test_that("confint_boot != confint from lm", {
+ expect_false(
+ identical(confint_boot, confint_lm_no_boot)
+ )
+ })
+
+test_that("confint_boot with user confidence level", {
+ expect_true(
+ all(confint_boot_conf90 == confint_boot_conf90_check)
+ )
+ })
+