Version 3.6-0

Author: pbreheny

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: ncvreg
 Title: Regularization Paths for SCAD and MCP Penalized Regression Models
 Version: 3.6-0
-Date: 2016-06-02
+Date: 2016-06-13
 Author: Patrick Breheny [aut,cre], Sangin Lee [ctb]
 Maintainer: Patrick Breheny <[email protected]>
 Depends:

NEWS

@@ -1,3 +1,12 @@
+3.6-0
+	* Improved efficiency for cox models (avoids recalculating linear
+          predictors)
+	* Exported std() function for standardizing a design matrix
+	* Reorganized testing suite
+	* Fixed bug in predict.cv.ncvsurv
+	* Added 'quick start' vignette
+	* 'survival' package now used for setupLambda in Cox models
+
 3.5-2
 	* Fixed bug for ncvsurv with integer penalty factors
 	* Fixed rare numerical accuracy bug in cv fold assignments

inst/tests/coerce.R

@@ -0,0 +1,5 @@
+data(heart)
+X <- heart[,1:9]
+y <- factor(heart$chd, labels=c("No", "Yes"))
+fit <- ncvreg(X, y, family="binomial")
+std(X)

inst/tests/cv.ncvreg.R

@@ -7,7 +7,6 @@ equal <- function(x, y) {all.equal(x, y, tol=0.001, check.attributes=FALSE)}
 X <- matrix(rnorm(500), 50, 10)
 y <- rnorm(50)
 cvfit <- cv.ncvreg(X, y)
-plot(cvfit, type='all')
 print(summary(cvfit))
 
 # Predict
@@ -30,7 +29,6 @@ cvfit <- cv.ncvreg(as.data.frame(X), y)
 X <- matrix(rnorm(25*4), 25, 4)
 y <- rnorm(25)
 cvfit <- cv.ncvreg(X, y, nfolds=25)
-plot(cvfit, type='all')
 print(summary(cvfit))
 
 #### Logistic regression ####
@@ -39,7 +37,6 @@ print(summary(cvfit))
 X <- matrix(rnorm(500), 50, 10)
 y <- rbinom(50, 1, 0.5)
 cvfit <- cv.ncvreg(X, y, family='binomial')
-plot(cvfit, type='all')
 print(summary(cvfit))
 
 # Predict
@@ -55,14 +52,12 @@ p <- predict(cvfit, X, type='nvars')
 X <- matrix(rnorm(30*2), 30, 2)
 y <- rbinom(30, 1, 0.5)
 cvfit <- cv.ncvreg(X, y, nfolds=30, family='binomial')
-plot(cvfit, type='all')
 print(summary(cvfit))
 
 #### Poisson regression ####
 
 # Works
 cvfit <- cv.ncvreg(X, y, family='poisson')
-plot(cvfit, type='all')
 print(summary(cvfit))
 
 # Predict
@@ -75,5 +70,4 @@ p <- predict(cvfit, X, type='nvars')
 
 # LOOCV
 cvfit <- cv.ncvreg(X, y, nfolds=30, family='poisson')
-plot(cvfit, type='all')
 print(summary(cvfit))

inst/tests/parallel.R

@@ -1,17 +1,19 @@
-require(parallel)
-require(survival)
-cl <- makeCluster(4)
+if (Sys.getenv("_R_CHECK_LIMIT_CORES_")=="") {
+  require(parallel)
+  require(survival)
+  cl <- makeCluster(4)
 
-# Linear
-X <- matrix(rnorm(500), 50, 10)
-y <- rnorm(50)
-cvfit <- cv.ncvreg(X, y, cluster=cl)
+  # Linear
+  X <- matrix(rnorm(500), 50, 10)
+  y <- rnorm(50)
+  cvfit <- cv.ncvreg(X, y, cluster=cl)
 
-# Logistic
-y <- rbinom(50, 1, 0.5)
-cvfit <- cv.ncvreg(X, y, cluster=cl, family='binomial')
+  # Logistic
+  y <- rbinom(50, 1, 0.5)
+  cvfit <- cv.ncvreg(X, y, cluster=cl, family='binomial')
 
-# Cox
-y <- Surv(rexp(50), sample(rep(0:1, c(10,40))))
-X <- matrix(rnorm(50*10), 50, 10)
-cvfit <- cv.ncvsurv(X, y, cluster=cl)
+  # Cox
+  y <- Surv(rexp(50), sample(rep(0:1, c(10,40))))
+  X <- matrix(rnorm(50*10), 50, 10)
+  cvfit <- cv.ncvsurv(X, y, cluster=cl)
+}

man/ncvreg-package.Rd

@@ -3,34 +3,25 @@
 \docType{package}
 \title{Regularization paths for SCAD- and MCP-penalized regression
   models}
-\description{Efficient algorithms for fitting regularization paths for
+\description{
+  Efficient algorithms for fitting regularization paths for
   a variety of regression models (linear, logistic, Poisson, survival)
-  penalized by MCP or SCAD, with optional additional L2 penalty.}
+  penalized by MCP or SCAD, with optional additional L2 penalty.
+}
 \details{
-  \tabular{ll}{
-    Package: \tab ncvreg\cr
-    Type: \tab Package\cr
-    Version: \tab 3.5-2\cr
-    Date: \tab 2016-04-08\cr
-    License: \tab GPL-2\cr}
   Accepts a design matrix \code{X} and vector of responses \code{y},
   produces the regularization path over a grid of values for the tuning
   parameter \code{lambda}.  Also provides methods for plotting,
   cross-validation-based inference, and for determining locally convex
-  regions of the coefficients paths.}
+  regions of the coefficients paths.
+
+  See the "Quick start guide" for a brief overview of how the package
+  works.
+}
 \references{Breheny, P. and Huang, J. (2011) Coordinate descent
   algorithms for nonconvex penalized regression, with applications to
   biological feature selection. Ann. Appl. Statist., 5: 232-253.}
 \author{Patrick Breheny <patrick-breheny@uiowa.edu>}
 \examples{
-data(prostate)
-X <- as.matrix(prostate[,1:8])
-y <- prostate$lpsa
-
-fit <- ncvreg(X,y)
-plot(fit)
-
-cvfit <- cv.ncvreg(X,y)
-plot(cvfit)
-summary(cvfit)
+vignette("quick-start", package="ncvreg")
 }

man/plot.fir.Rd

@@ -33,7 +33,13 @@ fit <- ncvreg(X, y)
 f <- fir(fit)
 cbind(EF=f$EF, S=f$S, FIR=f$FIR)[1:10,]
 
-## Comparison with perm.ncvreg
+# Some plotting options
+plot(f)
+plot(f, type="EF")
+plot(f, log=TRUE)
+
+
+# Comparison with perm.ncvreg
 par(mfrow=c(2,2))
 plot(f)
 plot(f, type="EF")