Commit 7622bf3c authored by Armin Rauschenberger's avatar Armin Rauschenberger

automation

parent 06811263
......@@ -74,6 +74,12 @@
#' argument \code{nzero} in functions
#' \code{\link{coef}} and \code{\link{predict}}.
#'
#' @return
#' Object of class \code{\link[starnet]{starnet}}.
#' The slots \code{base} and \code{meta}
#' contain \code{\link[glmnet]{cv.glmnet}}-like objects,
#' for the base and meta learners, respectively.
#'
#' @examples
#' set.seed(1)
#' n <- 30; p <- 50
......@@ -297,6 +303,16 @@ starnet <- function(y,X,family="gaussian",nalpha=21,alpha=NULL,nfolds=10,foldid=
#' @param ...
#' further arguments (not applicable)
#'
#' @return
#' Matrix of predicted values, with samples in the rows,
#' and models in the columns. Included models are
#' \code{alpha} (fixed elastic net),
#' \code{ridge} (i.e. \code{alpha0}),
#' \code{lasso} (i.e. \code{alpha1}),
#' \code{tune} (tuned elastic net),
#' \code{stack} (stacked elastic net),
#' and \code{none} (intercept-only model).
#'
#' @examples
#' set.seed(1)
#' n <- 30; p <- 50
......@@ -360,6 +376,11 @@ predict.starnet <- function(object,newx,type="response",nzero=NULL,...){
#'
#' @inheritParams predict.starnet
#'
#' @return
#' List of scalar \code{alpha} and vector \code{beta},
#' containing the pooled intercept and the pooled slopes,
#' respectively.
#'
#' @examples
#' set.seed(1)
#' n <- 30; p <- 50
......@@ -419,6 +440,9 @@ coef.starnet <- function(object,nzero=NULL,...){
#' @param ...
#' further arguments (not applicable)
#'
#' @return
#' Vector containing intercept and slopes from the meta learner.
#'
#' @examples
#' set.seed(1)
#' n <- 30; p <- 50
......@@ -450,6 +474,9 @@ weights.starnet <- function(object,...){
#' @param ...
#' further arguments (not applicable)
#'
#' @return
#' Prints "stacked gaussian/binomial/poisson elastic net".
#'
#' @examples
#' set.seed(1)
#' n <- 30; p <- 20
......@@ -490,6 +517,19 @@ print.starnet <- function(x,...){
#' @param ...
#' further arguments (not applicable)
#'
#' @return
#' List containing the cross-validated loss
#' (or out-of sample loss if \code{nfolds.ext} equals two,
#' and \code{foldid.ext} contains zeros and ones).
#' The slot \code{meta} contains the loss from the stacked elastic net
#' (\code{stack}), the tuned elastic net (\code{tune}), ridge, lasso,
#' and the intercept-only model (\code{none}).
#' The slot \code{base} contains the loss from the base learners.
#' And the slot \code{extra} contains the loss from the restricted
#' stacked elastic net (\code{stack}), lasso, and lasso-like elastic net
#' (\code{enet}),
#' with the maximum number of non-zero coefficients shown in the column name.
#'
#' @examples
#' set.seed(1)
#' n <- 50; p <- 20
......@@ -497,7 +537,7 @@ print.starnet <- function(x,...){
#' y <- rnorm(n=n,mean=rowSums(X[,1:20]))
#' \dontshow{
#' loss <- cv.starnet(y=y,X=X,nfolds.ext=2,nfolds.int=3)}
#' \dontrun{
#' \donttest{
#' loss <- cv.starnet(y=y,X=X)}
#'
cv.starnet <- function(y,X,family="gaussian",nalpha=21,alpha=NULL,nfolds.ext=10,nfolds.int=10,foldid.ext=NULL,foldid.int=NULL,type.measure="deviance",alpha.meta=1,nzero=NULL,intercept=NULL,upper.limit=NULL,unit.sum=NULL,...){
......@@ -597,6 +637,9 @@ cv.starnet <- function(y,X,family="gaussian",nalpha=21,alpha=NULL,nfolds.ext=10,
#' @param family
#' character \code{"gaussian"}, \code{"binomial"} or \code{"poisson"}
#'
#' @return
#' List of vector \code{y} and matrix \code{X}.
#'
#' @examples
#' NA
#'
......@@ -694,7 +737,7 @@ cv.starnet <- function(y,X,family="gaussian",nalpha=21,alpha=NULL,nfolds.ext=10,
#'
#' @param grouped
#' logical (for \code{"cox"} only)
#'
#'
#' @examples
#' NA
#'
......@@ -814,6 +857,9 @@ cv.starnet <- function(y,X,family="gaussian",nalpha=21,alpha=NULL,nfolds.ext=10,
#' @param w
#' (ignored here)
#'
#' @return
#' area under the ROC curve
#'
#' @examples
#' NA
#'
......@@ -835,6 +881,9 @@ glmnet.auc <- get("auc",envir=asNamespace("glmnet"))
#' maximum number of non-zero coefficients\strong{:}
#' positive integer
#'
#' @return
#' Object of class \code{\link[glmnet]{cv.glmnet}}.
#'
#' @examples
#' NA
#'
......
......@@ -10,23 +10,27 @@
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......@@ -53,7 +57,7 @@
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......@@ -122,6 +126,12 @@ Content not found. Please use links in the navbar.
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<nav id="toc" data-toggle="toc" class="sticky-top">
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......@@ -132,7 +142,7 @@ Content not found. Please use links in the navbar.
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<div class="pkgdown">
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......@@ -6,19 +6,19 @@
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......@@ -79,7 +79,7 @@
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<h1>Stacked Elastic Net</h1>
<h1 data-toc-skip>Stacked Elastic Net</h1>
<small class="dont-index">Source: <a href="https://github.com/rauschenberger/starnet/blob/master/vignettes/article.Rmd"><code>vignettes/article.Rmd</code></a></small>
......@@ -97,16 +97,11 @@
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......@@ -117,7 +112,7 @@
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......@@ -10,23 +10,27 @@
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......@@ -122,11 +126,14 @@
<h3>All vignettes</h3>
<p class="section-desc"></p>
<ul>
<li><a href="article.html">Stacked Elastic Net</a></li>
<li><a href="script.html">Stacked Elastic Net</a></li>
<li><a href="starnet.html">Stacked Elastic Net</a></li>
</ul>
<dl>
<dt><a href="article.html">Stacked Elastic Net</a></dt>
<dd></dt>
<dt><a href="script.html">Stacked Elastic Net</a></dt>
<dd></dt>
<dt><a href="starnet.html">Stacked Elastic Net</a></dt>
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......@@ -138,7 +145,7 @@
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<h1>Stacked Elastic Net</h1>
<h1 data-toc-skip>Stacked Elastic Net</h1>
<small class="dont-index">Source: <a href="https://github.com/rauschenberger/starnet/blob/master/vignettes/starnet.Rmd"><code>vignettes/starnet.Rmd</code></a></small>
......@@ -93,24 +93,19 @@
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<a href="#installation" class="anchor"></a>Installation</h2>
<p>Install the current release from <a href="https://CRAN.R-project.org/package=starnet">CRAN</a>:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="kw"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span>(<span class="st">"starnet"</span>)</a></code></pre></div>
<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span>(<span class="st">"starnet"</span>)</pre></body></html></div>
<p>or the latest development version from <a href="https://github.com/rauschenberger/starnet">GitHub</a>:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1"><span class="co">#install.packages("devtools")</span></a>
<a class="sourceLine" id="cb2-2" title="2">devtools<span class="op">::</span><span class="kw"><a href="https://devtools.r-lib.org//reference/remote-reexports.html">install_github</a></span>(<span class="st">"rauschenberger/starnet"</span>)</a></code></pre></div>
<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="co">#install.packages("devtools")</span>
<span class="kw pkg">devtools</span><span class="kw ns">::</span><span class="fu"><a href="https://devtools.r-lib.org//reference/remote-reexports.html">install_github</a></span>(<span class="st">"rauschenberger/starnet"</span>)</pre></body></html></div>
<p>A Rauschenberger, E Glaab, and MA van de Wiel (2020). “Predictive and interpretable models via the stacked elastic net”. <em>Bioinformatics.</em> In press.</p>
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<script src="pkgdown.js"></script><meta property="og:title" content="Stacked Elastic Net">
<meta property="og:description" content="Implements stacked elastic net regression (Rauschenberger 2020). The elastic net generalises ridge and lasso regularisation (Zou 2005, &lt;doi:10.1111/j.1467-9868.2005.00503.x&gt;). Instead of fixing or tuning the mixing parameter alpha, we combine multiple alpha by stacked generalisation (Wolpert 1992 &lt;doi:10.1016/S0893-6080(05)80023-1&gt;).">
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</head>
<body>
<body data-spy="scroll" data-target="#toc">
<div class="container template-home">
<header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
<div class="container">
......