0.4: Lots of internal updates to documentation, testing

Author: pbreheny

.Rbuildignore

@@ -1,2 +1,5 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
+^inst/data$
+^\.lintr$
+^ex$

.Rprofile

@@ -1,3 +1,5 @@
 .Rproj.user
 hdrm.Rproj
 !R/sysdata.rda
+inst/data/*
+!inst/data/README

.gitignore

@@ -1,15 +1,14 @@
 Package: hdrm
 Title: High-Dimensional Regression Modeling
-Date: 2023-01-11
-Version: 0.3.8
+Date: 2023-02-08
+Version: 0.4.0
 Author: Patrick Breheny and Jian Huang
 Maintainer: Patrick Breheny <patrick-breheny@uiowa.edu>
 Description: Examples, simulations, and figures from the book, "High-Dimensional
  Regression Modeling", by Breheny and Huang.
-Depends: glmnet, ncvreg
+Depends: R (>= 3.5.0), glmnet, ncvreg
 Imports: grpreg, Matrix
 Suggests: ggplot2, abind, gridExtra
-LazyData: TRUE
 BugReports: http://github.com/pbreheny/hdrm/issues
 License: GPL-3
 RoxygenNote: 7.2.3

DESCRIPTION

@@ -11,7 +11,7 @@
 #'
 #' @return A list of two arrays, `MCP`, containing the MSE for MCP, and `SCAD`, containing the MSE for SCAD.
 #'
-#' @examples Ex3.1(N=10)
+#' @examples Ex3.1(N=2)
 #'
 #' @export
 
@@ -50,7 +50,7 @@ Ex3.1 <- function(N=500, n=50, p=100, p1=6, SNR=c(1, 2, 4), seed=2) {
 #' @param parlist   List of arguments to pass to `par()`
 #'
 #' @examples
-#' out <- Ex3.1(N=10)
+#' out <- Ex3.1(N=3)
 #' Fig3.5(out)
 #' @export
 

R/gam-snr.R

@@ -1,16 +1,12 @@
-#' hdrm: High dimensional regression modeling, by Breheny and Huang
-#'
-#' @docType package
+#' @keywords internal
 #'
 #' @import stats
 #' @import graphics
 #' @import grDevices
 #' @import utils
 #' @import glmnet
 #' @import ncvreg
-#' 
+#'
 #' @importFrom Matrix bandSparse t
 #' @importFrom grpreg grpreg grpsurv cv.grpreg cv.grpsurv
-#'
-#' @name hdrm
-NULL
+"_PACKAGE"

R/hdrm.R

@@ -10,7 +10,7 @@
 #' @param rho     Correlation between features (compound symmetric)
 #' @param seed    Seed for reproducibility
 #'
-#' @examples Ex4.4(N=3)
+#' @examples Ex4.4(N=2, seq=c(0.3, 1))
 #' @export
 
 Ex4.4 <- function(N=100, s=seq(0.1, 1.1, 0.2), n=100, p=500, p1=12, rho=0.7, seed=1) {

R/mnet-sim-cor.R

@@ -9,7 +9,7 @@
 #' @param p1      Number of non-null features
 #' @param seed    Seed for reproducibility
 #'
-#' @examples Ex4.3(N=10)
+#' @examples Ex4.3(N=2, seq=c(0.3, 1))
 #' @export
 
 Ex4.3 <- function(N=100, s=seq(0.1, 1.1, 0.2), n=100, p=500, p1=12, seed=1) {
@@ -50,7 +50,7 @@ Ex4.3 <- function(N=100, s=seq(0.1, 1.1, 0.2), n=100, p=500, p1=12, seed=1) {
 #' @param parlist   List of arguments to pass to `par()`
 #'
 #' @examples
-#' res <- Ex4.3(N=5, corr='cs')
+#' res <- Ex4.3(N=5)
 #' Fig4.2(res)
 #' @export
 

R/mnet-sim-ind.R

@@ -2,20 +2,22 @@
 #'
 #' Reproduces Figure 1.5 from the book.  If you specify any options, your results may look different.
 #'
+#' @param lwd,xlim,ylim,xlab,ylab   As in `plot()`
+#' @param ...                       Further arguments to `plot()`
+#'
 #' @examples
 #' Fig1.5()
-#'
 #' @export
 
-Fig1.5 <- function() {
+Fig1.5 <- function(lwd=3, xlim=c(3,-3), ylim=c(50,250), xlab=expression(lambda), ylab="Prediction error", ...) {
   Data <- readData("pollution")
   XX <- std(Data$X)
   y <- Data$y
   fit <- ridge(XX, y)
   ll <- log10(fit$lambda)
   col <- pal(2)
-  plot(ll, fit$GCV/1000, type="l", lwd=3, xlim=c(3,-3), las=1, xlab=expression(lambda), xaxt="n", ylab="Prediction error",
-       col=col[2], bty="n", ylim=c(50,250))
+  plot(ll, fit$GCV/1000, type="l", lwd=3, xlim=xlim, las=1, xlab=xlab, xaxt="n", ylab=ylab, ylim=ylim,
+       col=col[2], bty="n")
   logAxis(1, base=10)
   lines(ll, fit$RSS/1000, col=col[1], lwd=3)
   text(-3.1, 105, "GCV", xpd=TRUE)

R/ridge-gcv.R

@@ -77,7 +77,11 @@ ridge.matrix <- function (obj, y, lambda, ...) {
 
 #' Plot a ridge regression coefficient path
 #'
-#' @param x  a ridge
+#' @param x       An object of class `"ridge"`, as returned by `ridge()`
+#' @param xaxis   One of `"loglam"`, `"df"`, or `"both"`. If `"both"`, the
+#' bottom axis is lambda (log scale), and the top is df.
+#' @param xlab,ylab   As in `plot()`
+#' @param ...         Additional arguments to `matplot()`
 #'
 #' @export
 
@@ -89,7 +93,7 @@ plot.ridge <- function(x, xaxis=c('loglam', 'df', 'both'), xlab, ylab, ...) {
     ll <- log10(x$lambda)
     if (missing(xlab)) xlab <- expression(lambda)
     matplot(ll, B, lty=1, col=col, type="l", lwd=3, xaxt="n",
-            xlab=xlab, ylab="", las=1, xlim=rev(range(ll)), bty="n")
+            xlab=xlab, ylab="", las=1, xlim=rev(range(ll)), bty="n", ...)
     logAxis(1, base=10)
   } else if (xaxis=='df') {
     matplot(x$df, B, lty=1, col=col, type="l", lwd=3,

R/ridge.R

@@ -6,7 +6,7 @@
 #' @param seed   Random seed for reproducibility
 #'
 #' @examples
-#' p <- Ex9.2(N=10)
+#' p <- Ex9.2(N=5)
 #' head(sort(p))
 #'
 #' @export

R/split-tcga.R

@@ -1,4 +1,4 @@
-[![GitHub version](https://img.shields.io/static/v1?label=GitHub&message=0.3.8&color=blue&logo=github)](https://github.com/pbreheny/hdrm)
+[![GitHub version](https://img.shields.io/static/v1?label=GitHub&message=0.4.0&color=blue&logo=github)](https://github.com/pbreheny/hdrm)
 
 # hdrm: High dimensional regression modeling
 

README.md

@@ -0,0 +1,3 @@
+if (requireNamespace("tinytest", quietly=TRUE)) {
+  tinytest::test_package("hdrm", pattern="^[^_].*\\.[rR]$")
+}