version 0.2-1

DESCRIPTION

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+Package: relaxnet
+Type: Package
+Title: Relaxation of glmnet models (as in relaxed lasso, Meinshausen
+        2007)
+Version: 0.2-1
+Date: 2013-05-13
+Author: Stephan Ritter, Alan Hubbard
+Maintainer: Stephan Ritter <sritter@berkeley.edu>
+Depends: glmnet
+Description: Extends the glmnet package with "relaxation", done by
+        running glmnet once on the entire predictor matrix, then again
+        on each different subset of variables from along the
+        regularization path. Penalty may be lasso (alpha = 1) or
+        elastic net (0 < alpha < 1). For this version, family may be
+        "gaussian" or "binomial" only. Takes advantage of fast fortran
+        code from the glmnet package.
+License: GPL (>= 2)
+URL: http://cran.r-project.org/package=relaxnet
+Packaged: 2013-05-13 21:40:05 UTC; sritter
+NeedsCompilation: no
+Repository: CRAN
+Date/Publication: 2013-05-13 23:52:22

MD5

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+a2c733d9ae7a1c2eaa0e7cf73a75aac4 *DESCRIPTION
+7afafe46a8546a6f224388bb49f5d341 *NAMESPACE
+41866d4c1eaaca6c0b5a9e00ed354666 *R/cv.alpha.relaxnet.R
+ecfce34e5e1be0d6718b4d266b65885f *R/cv.relaxnet.R
+23d16565b304d04a74137e58b3cbe7a0 *R/predict.cv.alpha.relaxnet.R
+bfb5e07567c1a5c5747a4c75de823b4c *R/predict.cv.relaxnet.R
+398b60ed5e0ac6382f9b12dcc84910cb *R/predict.relaxnet.R
+bf7e8e9a2cdbd9e83d3c4a26b1be7879 *R/relaxnet.R
+4efc47e59cf8a1c03bade4913fb40555 *R/summary.relaxnet.R
+bab5e49d10753fa8c957e2298ac7d5c1 *R/zzz.R
+8185686dc82a5973a10b2799e33ced8d *man/cv.relaxnet.Rd
+53194f7662da0108e326dad35f6d21a1 *man/predict.cv.relaxnet.Rd
+7da35c54ba9cd9c3eed0f4c9bd5225d9 *man/predict.relaxnet.Rd
+470500c73bfd1037fd576df5cd430354 *man/print.relaxnet.Rd
+42389059fc67a5834a10967b9fb0660a *man/relaxnet-package.Rd
+bf6bc30c4b073b514201095f9a12e7e0 *man/relaxnet.Rd
+ac3047db141412ed844e061eb841882a *man/summary.relaxnet.Rd

NAMESPACE

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+export(relaxnet, cv.relaxnet, cv.alpha.relaxnet)
+S3method(summary, relaxnet)
+S3method(print, relaxnet)
+S3method(print, summary.relaxnet)
+S3method(predict, relaxnet)
+S3method(predict, cv.relaxnet)
+S3method(predict, cv.alpha.relaxnet)

R/cv.alpha.relaxnet.R

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+################################################################################
+
+## cross-validation (on both lambda and alpha) for relaxnet models
+## adapted from the cv.glmnet function from package glmnet
+
+cv.alpha.relaxnet <- function(x, y, family = c("gaussian", "binomial"),
+                        nlambda = 100,
+                        alpha = c(.1, .3, .5, .7, .9),
+
+                        relax = TRUE,
+                        relax.nlambda = 100,
+                        relax.max.vars = min(nrow(x), ncol(x)) * 0.8,
+
+                        lambda = NULL,
+                        relax.lambda.index = NULL,
+                        relax.lambda.list = NULL,
+
+                        ##type.measure=c("mse","deviance","class","auc","mae"),
+                        ## just set it in code for now
+
+                        nfolds = 10,
+                        foldid,
+
+                        ...) {
+
+  start.time <- Sys.time()
+
+  family = match.arg(family)
+
+  ## do something with this if including type.measure as an arg
+
+  ## if(family == "gaussian") type.measure <- "mse"
+  ## if(family == "binomial") type.measure <- "deviance"
+
+  N=nrow(x)
+
+  if(missing(foldid)) {
+
+    ## check nfolds here
+
+    foldid <- sample(rep(seq(nfolds), length=N))
+
+  }## else {
+
+    ## check foldid
+
+##  }
+
+  if(relax && ncol(x) == 1) {
+
+    warning("x has only one column, setting relax to FALSE")
+    relax <- FALSE
+  }
+
+  cv.relaxnet.results <-
+
+    lapply(alpha,
+           function(alpha.val) {
+
+             cv.relaxnet(x, y, family,
+                         nlambda,
+                         alpha.val,
+                         relax,
+                         relax.nlambda,
+                         relax.max.vars,
+                         lambda,
+                         relax.lambda.index,
+                         relax.lambda.list,
+                         foldid = foldid,
+                         ...)
+           })
+
+  alpha.min.index <- which.min(sapply(cv.relaxnet.results, function(cv.result) cv.result$min.cvm))
+
+  end.time <- Sys.time()
+
+  obj <- list(call = match.call(),
+              relax = relax,
+              alpha = alpha,
+              cv.relaxnet.results = cv.relaxnet.results,
+              which.alpha.min = alpha[alpha.min.index],
+              total.time = as.double(difftime(end.time, start.time,
+                                              units = "secs")))
+
+  class(obj) <- "cv.alpha.relaxnet"
+
+  obj
+}