Gitlab migration complete. If you have any issue please read the FAQ.

Commit 05b38bf4 authored by Armin Rauschenberger's avatar Armin Rauschenberger
Browse files

automation

parent f4df6530
This package was submitted to CRAN on 2019-08-02.
Once it is accepted, delete this file and tag the release (commit a4d115213f).
Package: joinet
Version: 0.0.2
Title: Multivariate Regression through Stacked Generalisation
Title: Multivariate Elastic Net Regression
Description: Implements high-dimensional multivariate regression by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>). For positively correlated outcomes, a single multivariate regression is typically more predictive than multiple univariate regressions. Includes functions for model fitting, extracting coefficients, outcome prediction, and performance measurement.
Depends: R (>= 3.0.0)
Imports: glmnet, palasso, cornet
......
## joinet 0.0.1 (2019-07-31)
## joinet 0.0.2 (2019-08-08)
* performance comparison
## joinet 0.0.1 (2019-08-03)
* first submission
\ No newline at end of file
......@@ -56,7 +56,8 @@
#'
#' @references
#' Armin Rauschenberger, Enrico Glaab (2019)
#' "Multivariate elastic net regression through stacked generalisation"
#' "joinet: predicting correlated outcomes jointly
#' to improve clinical prognosis"
#' \emph{Manuscript in preparation}.
#'
#' @details
......@@ -80,7 +81,7 @@
#' \eqn{q} \code{\link[glmnet]{cv.glmnet}}-like objects.
#'
#' @seealso
#' \code{\link{cv.joinet}}, \code{browseVignettes("joinet")}
#' \code{\link{cv.joinet}}, vignette
#'
#' @examples
#' n <- 50; p <- 100; q <- 3
......@@ -88,6 +89,9 @@
#' Y <- replicate(n=q,expr=rnorm(n=n,mean=rowSums(X[,1:5])))
#' object <- joinet(Y=Y,X=X)
#'
#' \dontrun{
#' browseVignettes("joinet") # further examples}
#'
joinet <- function(Y,X,family="gaussian",nfolds=10,foldid=NULL,type.measure="deviance",alpha.base=1,alpha.meta=0,...){
#--- temporary ---
......@@ -421,7 +425,7 @@ print.joinet <- function(x,...){
#' Model comparison
#'
#' @description
#' Compares univariate and multivariate regression
#' Compares univariate and multivariate regression.
#'
#' @inheritParams joinet
#'
......@@ -453,7 +457,9 @@ print.joinet <- function(x,...){
#'
#' @return
#' This function returns a matrix with \eqn{q} columns,
#' including the cross-validated loss.
#' including the cross-validated loss from the univariate models
#' (\code{base}), the multivariate models (\code{meta}),
#' and the intercept-only models (\code{none}).
#'
#' @examples
#' n <- 50; p <- 100; q <- 3
......
......@@ -21,7 +21,7 @@ knitr::opts_chunk$set(
## Scope
Multivariate Elastic Net Regression (extending the [R](https://cran.r-project.org) package [glmnet](https://CRAN.R-project.org/package=glmnet)).
Multivariate elastic net regression through stacked generalisation (extending the [R](https://cran.r-project.org) package [glmnet](https://CRAN.R-project.org/package=glmnet)).
## Installation
......@@ -40,6 +40,4 @@ devtools::install_github("rauschenberger/joinet")
## Reference
Armin Rauschenberger and Enrico Glaab (2019).
"Multivariate regression through stacked generalisation".
*Manuscript in preparation.*
Armin Rauschenberger and Enrico Glaab (2019). "joinet: predicting correlated outcomes jointly to improve clinical prognosis". *Manuscript in preparation.*
......@@ -10,8 +10,8 @@ Status](https://codecov.io/github/rauschenberger/joinet/coverage.svg?branch=mast
## Scope
Multivariate Elastic Net Regression (extending the
[R](https://cran.r-project.org) package
Multivariate elastic net regression through stacked generalisation
(extending the [R](https://cran.r-project.org) package
[glmnet](https://CRAN.R-project.org/package=glmnet)).
## Installation
......@@ -33,5 +33,6 @@ devtools::install_github("rauschenberger/joinet")
## Reference
Armin Rauschenberger and Enrico Glaab (2019). “Multivariate regression
through stacked generalisation”. *Manuscript in preparation.*
Armin Rauschenberger and Enrico Glaab (2019). “joinet: predicting
correlated outcomes jointly to improve clinical prognosis”. *Manuscript
in preparation.*
Thanks, I improved the description, added a DOI, and added the value fields.
\ No newline at end of file
# Notes
- Early update because of manuscript submission (examples, vignettes).
- The maintainer email will change to armin.rauschenberger@uni.lu.
\ No newline at end of file
......@@ -5,11 +5,11 @@
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Stacked Elastic Net • joinet</title>
<title>Multivariate Elastic Net Regression • joinet</title>
<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js" integrity="sha256-FgpCb/KJQlLNfOu91ta32o/NMZxltwRo8QtmkMRdAu8=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.3.7/spacelab/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha256-U5ZEeKfGNOja007MMD3YBI0A3OSZOQbeG6z2f2Y0hu8=" crossorigin="anonymous"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css" integrity="sha256-eZrrJcwDc/3uDhsdt61sL2oOBY362qM3lon1gyExkL0=" crossorigin="anonymous">
<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.4/clipboard.min.js" integrity="sha256-FiZwavyI2V6+EXO1U+xzLG3IKldpiTFf3153ea9zikQ=" crossorigin="anonymous"></script><!-- sticky kit --><script src="https://cdnjs.cloudflare.com/ajax/libs/sticky-kit/1.1.3/sticky-kit.min.js" integrity="sha256-c4Rlo1ZozqTPE2RLuvbusY3+SU1pQaJC0TjuhygMipw=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
<script src="../pkgdown.js"></script><meta property="og:title" content="Stacked Elastic Net">
<script src="../pkgdown.js"></script><meta property="og:title" content="Multivariate Elastic Net Regression">
<meta property="og:description" content="">
<meta name="twitter:card" content="summary">
<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
......@@ -74,7 +74,7 @@
</header><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1>Stacked Elastic Net</h1>
<h1>Multivariate Elastic Net Regression</h1>
<small class="dont-index">Source: <a href="https://github.com/rauschenberger/joinet/blob/master/vignettes/article.Rmd"><code>vignettes/article.Rmd</code></a></small>
......@@ -88,7 +88,7 @@
<div id="reference" class="section level2">
<h2 class="hasAnchor">
<a href="#reference" class="anchor"></a>Reference</h2>
<p>Armin Rauschenberger and Enrico Glaab (2019). “Multivariate regression through stacked generalisation”. <em>Manuscript in preparation.</em></p>
<p>Armin Rauschenberger and Enrico Glaab (2019). “joinet: predicting correlated outcomes jointly to improve clinical prognosis”. <em>Manuscript in preparation.</em></p>
</div>
</div>
......
......@@ -113,8 +113,8 @@
<p class="section-desc"></p>
<ul>
<li><a href="article.html">Stacked Elastic Net</a></li>
<li><a href="vignette.html">Multivariate Elastic Net</a></li>
<li><a href="article.html">Multivariate Elastic Net Regression</a></li>
<li><a href="vignette.html">Multivariate Elastic Net Regression</a></li>
</ul>
</div>
</div>
......
......@@ -5,11 +5,11 @@
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Multivariate Elastic Net • joinet</title>
<title>Multivariate Elastic Net Regression • joinet</title>
<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js" integrity="sha256-FgpCb/KJQlLNfOu91ta32o/NMZxltwRo8QtmkMRdAu8=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.3.7/spacelab/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha256-U5ZEeKfGNOja007MMD3YBI0A3OSZOQbeG6z2f2Y0hu8=" crossorigin="anonymous"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css" integrity="sha256-eZrrJcwDc/3uDhsdt61sL2oOBY362qM3lon1gyExkL0=" crossorigin="anonymous">
<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.4/clipboard.min.js" integrity="sha256-FiZwavyI2V6+EXO1U+xzLG3IKldpiTFf3153ea9zikQ=" crossorigin="anonymous"></script><!-- sticky kit --><script src="https://cdnjs.cloudflare.com/ajax/libs/sticky-kit/1.1.3/sticky-kit.min.js" integrity="sha256-c4Rlo1ZozqTPE2RLuvbusY3+SU1pQaJC0TjuhygMipw=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
<script src="../pkgdown.js"></script><meta property="og:title" content="Multivariate Elastic Net">
<script src="../pkgdown.js"></script><meta property="og:title" content="Multivariate Elastic Net Regression">
<meta property="og:description" content="">
<meta name="twitter:card" content="summary">
<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
......@@ -74,7 +74,7 @@
</header><div class="row">
<div class="col-md-9 contents">
<div class="page-header toc-ignore">
<h1>Multivariate Elastic Net</h1>
<h1>Multivariate Elastic Net Regression</h1>
<small class="dont-index">Source: <a href="https://github.com/rauschenberger/joinet/blob/master/vignettes/vignette.Rmd"><code>vignettes/vignette.Rmd</code></a></small>
......@@ -162,28 +162,24 @@
<div id="reference" class="section level2">
<h2 class="hasAnchor">
<a href="#reference" class="anchor"></a>Reference</h2>
<p>Armin Rauschenberger and Enrico Glaab (2019). “Multivariate regression through stacked generalisation”. <em>Manuscript in preparation.</em></p>
<!--
<p>Armin Rauschenberger and Enrico Glaab (2019). “joinet: predicting correlated outcomes jointly to improve clinical prognosis”. <em>Manuscript in preparation.</em> <!--
```r
#install.packages("plsgenomics")
data(Ecoli,package="plsgenomics")
X <- Ecoli$CONNECdata
Y <- Ecoli$GEdata
loss <- joinet:::cv.joinet(Y=Y,X=X)
```
loss <- cv.joinet(Y=Y,X=X)
```r
#install.packages("BiocManager")
#BiocManager::install("mixOmics")
data(liver.toxicity,package="mixOmics")
X <- liver.toxicity$gene
Y <- liver.toxicity$clinic
Y$Cholesterol.mg.dL. <- -Y$Cholesterol.mg.dL.
loss <- joinet:::cv.joinet(Y=Y,X=X)
loss <- cv.joinet(Y=Y,X=X)
```
-->
--></p>
</div>
</div>
......
......@@ -5,11 +5,11 @@
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Multivariate Regression through Stacked Generalisation • joinet</title>
<title>Multivariate Elastic Net Regression • joinet</title>
<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js" integrity="sha256-FgpCb/KJQlLNfOu91ta32o/NMZxltwRo8QtmkMRdAu8=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.3.7/spacelab/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha256-U5ZEeKfGNOja007MMD3YBI0A3OSZOQbeG6z2f2Y0hu8=" crossorigin="anonymous"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css" integrity="sha256-eZrrJcwDc/3uDhsdt61sL2oOBY362qM3lon1gyExkL0=" crossorigin="anonymous">
<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.4/clipboard.min.js" integrity="sha256-FiZwavyI2V6+EXO1U+xzLG3IKldpiTFf3153ea9zikQ=" crossorigin="anonymous"></script><!-- sticky kit --><script src="https://cdnjs.cloudflare.com/ajax/libs/sticky-kit/1.1.3/sticky-kit.min.js" integrity="sha256-c4Rlo1ZozqTPE2RLuvbusY3+SU1pQaJC0TjuhygMipw=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet">
<script src="pkgdown.js"></script><meta property="og:title" content="Multivariate Regression through Stacked Generalisation">
<script src="pkgdown.js"></script><meta property="og:title" content="Multivariate Elastic Net Regression">
<meta property="og:description" content="Implements high-dimensional multivariate regression by stacked generalisation (Wolpert 1992 &lt;doi:10.1016/S0893-6080(05)80023-1&gt;). For positively correlated outcomes, a single multivariate regression is typically more predictive than multiple univariate regressions. Includes functions for model fitting, extracting coefficients, outcome prediction, and performance measurement.">
<meta name="twitter:card" content="summary">
<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
......@@ -82,7 +82,7 @@
<div id="scope" class="section level2">
<h2 class="hasAnchor">
<a href="#scope" class="anchor"></a>Scope</h2>
<p>Multivariate Elastic Net Regression (extending the <a href="https://cran.r-project.org">R</a> package <a href="https://CRAN.R-project.org/package=glmnet">glmnet</a>).</p>
<p>Multivariate elastic net regression through stacked generalisation (extending the <a href="https://cran.r-project.org">R</a> package <a href="https://CRAN.R-project.org/package=glmnet">glmnet</a>).</p>
</div>
<div id="installation" class="section level2">
<h2 class="hasAnchor">
......@@ -96,7 +96,7 @@
<div id="reference" class="section level2">
<h2 class="hasAnchor">
<a href="#reference" class="anchor"></a>Reference</h2>
<p>Armin Rauschenberger and Enrico Glaab (2019). “Multivariate regression through stacked generalisation”. <em>Manuscript in preparation.</em></p>
<p>Armin Rauschenberger and Enrico Glaab (2019). “joinet: predicting correlated outcomes jointly to improve clinical prognosis”. <em>Manuscript in preparation.</em></p>
</div>
</div>
......
......@@ -109,9 +109,16 @@
<small>Source: <a href='https://github.com/rauschenberger/joinet/blob/master/NEWS.md'><code>NEWS.md</code></a></small>
</div>
<div id="joinet-001-2019-07-31" class="section level2">
<div id="joinet-002-2019-08-08" class="section level2">
<h2 class="hasAnchor">
<a href="#joinet-001-2019-07-31" class="anchor"></a>joinet 0.0.1 (2019-07-31)</h2>
<a href="#joinet-002-2019-08-08" class="anchor"></a>joinet 0.0.2 (2019-08-08)</h2>
<ul>
<li>performance comparison</li>
</ul>
</div>
<div id="joinet-001-2019-08-03" class="section level2">
<h2 class="hasAnchor">
<a href="#joinet-001-2019-08-03" class="anchor"></a>joinet 0.0.1 (2019-08-03)</h2>
<ul>
<li>first submission</li>
</ul>
......@@ -122,7 +129,8 @@
<div id="tocnav">
<h2>Contents</h2>
<ul class="nav nav-pills nav-stacked">
<li><a href="#joinet-001-2019-07-31">0.0.1</a></li>
<li><a href="#joinet-002-2019-08-08">0.0.2</a></li>
<li><a href="#joinet-001-2019-08-03">0.0.1</a></li>
</ul>
</div>
</div>
......
......@@ -32,7 +32,7 @@
<meta property="og:title" content="Model comparison — cv.joinet" />
<meta property="og:description" content="Compares univariate and multivariate regression" />
<meta property="og:description" content="Compares univariate and multivariate regression." />
<meta name="twitter:card" content="summary" />
......@@ -115,7 +115,7 @@
<div class="ref-description">
<p>Compares univariate and multivariate regression</p>
<p>Compares univariate and multivariate regression.</p>
</div>
......@@ -200,7 +200,9 @@ and <code><a href='https://www.rdocumentation.org/packages/glmnet/topics/cv.glmn
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>This function returns a matrix with \(q\) columns,
including the cross-validated loss.</p>
including the cross-validated loss from the univariate models
(<code>base</code>), the multivariate models (<code>meta</code>),
and the intercept-only models (<code>none</code>).</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
......
......@@ -201,19 +201,23 @@ ridge renders dense models (<code>alpha</code>\(=0\))</p>
<h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>
<p>Armin Rauschenberger, Enrico Glaab (2019)
"Multivariate elastic net regression through stacked generalisation"
"joinet: predicting correlated outcomes jointly
to improve clinical prognosis"
<em>Manuscript in preparation</em>.</p>
<h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
<div class='dont-index'><p><code><a href='cv.joinet.html'>cv.joinet</a></code>, <code><a href='https://www.rdocumentation.org/packages/utils/topics/browseVignettes'>browseVignettes("joinet")</a></code></p></div>
<div class='dont-index'><p><code><a href='cv.joinet.html'>cv.joinet</a></code>, vignette</p></div>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='no'>n</span> <span class='kw'>&lt;-</span> <span class='fl'>50</span>; <span class='no'>p</span> <span class='kw'>&lt;-</span> <span class='fl'>100</span>; <span class='no'>q</span> <span class='kw'>&lt;-</span> <span class='fl'>3</span>
<span class='no'>X</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/matrix'>matrix</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/Normal'>rnorm</a></span>(<span class='no'>n</span>*<span class='no'>p</span>),<span class='kw'>nrow</span><span class='kw'>=</span><span class='no'>n</span>,<span class='kw'>ncol</span><span class='kw'>=</span><span class='no'>p</span>)
<span class='no'>Y</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/lapply'>replicate</a></span>(<span class='kw'>n</span><span class='kw'>=</span><span class='no'>q</span>,<span class='kw'>expr</span><span class='kw'>=</span><span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/Normal'>rnorm</a></span>(<span class='kw'>n</span><span class='kw'>=</span><span class='no'>n</span>,<span class='kw'>mean</span><span class='kw'>=</span><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/colSums'>rowSums</a></span>(<span class='no'>X</span>[,<span class='fl'>1</span>:<span class='fl'>5</span>])))
<span class='no'>object</span> <span class='kw'>&lt;-</span> <span class='fu'>joinet</span>(<span class='kw'>Y</span><span class='kw'>=</span><span class='no'>Y</span>,<span class='kw'>X</span><span class='kw'>=</span><span class='no'>X</span>)</div></pre>
<span class='no'>object</span> <span class='kw'>&lt;-</span> <span class='fu'>joinet</span>(<span class='kw'>Y</span><span class='kw'>=</span><span class='no'>Y</span>,<span class='kw'>X</span><span class='kw'>=</span><span class='no'>X</span>)</div><span class='co'># NOT RUN {</span>
<span class='fu'><a href='https://www.rdocumentation.org/packages/utils/topics/browseVignettes'>browseVignettes</a></span>(<span class='st'>"joinet"</span>) <span class='co'># further examples</span>
<span class='co'># }</span><div class='input'>
</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
......
......@@ -56,10 +56,12 @@ and \code{\link[glmnet]{cv.glmnet}}}
}
\value{
This function returns a matrix with \eqn{q} columns,
including the cross-validated loss.
including the cross-validated loss from the univariate models
(\code{base}), the multivariate models (\code{meta}),
and the intercept-only models (\code{none}).
}
\description{
Compares univariate and multivariate regression
Compares univariate and multivariate regression.
}
\examples{
n <- 50; p <- 100; q <- 3
......
......@@ -70,12 +70,16 @@ X <- matrix(rnorm(n*p),nrow=n,ncol=p)
Y <- replicate(n=q,expr=rnorm(n=n,mean=rowSums(X[,1:5])))
object <- joinet(Y=Y,X=X)
\dontrun{
browseVignettes("joinet") # further examples}
}
\references{
Armin Rauschenberger, Enrico Glaab (2019)
"Multivariate elastic net regression through stacked generalisation"
"joinet: predicting correlated outcomes jointly
to improve clinical prognosis"
\emph{Manuscript in preparation}.
}
\seealso{
\code{\link{cv.joinet}}, \code{browseVignettes("joinet")}
\code{\link{cv.joinet}}, vignette
}
---
title: Stacked Elastic Net
title: Multivariate Elastic Net Regression
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{article}
......@@ -11,6 +11,4 @@ The `joinet` manuscript is in preparation. Click [here](https://CRAN.R-project.o
## Reference
Armin Rauschenberger and Enrico Glaab (2019).
"Multivariate regression through stacked generalisation".
*Manuscript in preparation.*
Armin Rauschenberger and Enrico Glaab (2019). "joinet: predicting correlated outcomes jointly to improve clinical prognosis". *Manuscript in preparation.*
---
title: Multivariate Elastic Net
title: Multivariate Elastic Net Regression
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{vignette}
......@@ -123,26 +123,21 @@ cv.joinet(Y=Y,X=X,family=family)
## Reference
Armin Rauschenberger and Enrico Glaab (2019).
"Multivariate regression through stacked generalisation".
*Manuscript in preparation.*
Armin Rauschenberger and Enrico Glaab (2019). "joinet: predicting correlated outcomes jointly to improve clinical prognosis". *Manuscript in preparation.*
<!--
```{r,eval=FALSE}
#install.packages("plsgenomics")
data(Ecoli,package="plsgenomics")
X <- Ecoli$CONNECdata
Y <- Ecoli$GEdata
loss <- joinet:::cv.joinet(Y=Y,X=X)
```
loss <- cv.joinet(Y=Y,X=X)
```{r,eval=FALSE}
#install.packages("BiocManager")
#BiocManager::install("mixOmics")
data(liver.toxicity,package="mixOmics")
X <- liver.toxicity$gene
Y <- liver.toxicity$clinic
Y$Cholesterol.mg.dL. <- -Y$Cholesterol.mg.dL.
loss <- joinet:::cv.joinet(Y=Y,X=X)
loss <- cv.joinet(Y=Y,X=X)
```
-->
<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8" />
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Multivariate Elastic Net</title>
<style type="text/css">code{white-space: pre;}</style>
<style type="text/css" data-origin="pandoc">
a.sourceLine { display: inline-block; line-height: 1.25; }
a.sourceLine { pointer-events: none; color: inherit; text-decoration: inherit; }
a.sourceLine:empty { height: 1.2em; }
.sourceCode { overflow: visible; }
code.sourceCode { white-space: pre; position: relative; }
div.sourceCode { margin: 1em 0; }
pre.sourceCode { margin: 0; }
@media screen {
div.sourceCode { overflow: auto; }
}
@media print {
code.sourceCode { white-space: pre-wrap; }
a.sourceLine { text-indent: -1em; padding-left: 1em; }
}
pre.numberSource a.sourceLine
{ position: relative; left: -4em; }
pre.numberSource a.sourceLine::before
{ content: attr(title);
position: relative; left: -1em; text-align: right; vertical-align: baseline;
border: none; pointer-events: all; display: inline-block;
-webkit-touch-callout: none; -webkit-user-select: none;
-khtml-user-select: none; -moz-user-select: none;
-ms-user-select: none; user-select: none;
padding: 0 4px; width: 4em;
color: #aaaaaa;
}
pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; }
div.sourceCode
{ }
@media screen {
a.sourceLine::before { text-decoration: underline; }
}
code span.al { color: #ff0000; font-weight: bold; } /* Alert */
code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */
code span.at { color: #7d9029; } /* Attribute */
code span.bn { color: #40a070; } /* BaseN */
code span.bu { } /* BuiltIn */
code span.cf { color: #007020; font-weight: bold; } /* ControlFlow */
code span.ch { color: #4070a0; } /* Char */
code span.cn { color: #880000; } /* Constant */
code span.co { color: #60a0b0; font-style: italic; } /* Comment */
code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */
code span.do { color: #ba2121; font-style: italic; } /* Documentation */
code span.dt { color: #902000; } /* DataType */
code span.dv { color: #40a070; } /* DecVal */
code span.er { color: #ff0000; font-weight: bold; } /* Error */
code span.ex { } /* Extension */
code span.fl { color: #40a070; } /* Float */
code span.fu { color: #06287e; } /* Function */
code span.im { } /* Import */
code span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */
code span.kw { color: #007020; font-weight: bold; } /* Keyword */
code span.op { color: #666666; } /* Operator */
code span.ot { color: #007020; } /* Other */
code span.pp { color: #bc7a00; } /* Preprocessor */
code span.sc { color: #4070a0; } /* SpecialChar */
code span.ss { color: #bb6688; } /* SpecialString */
code span.st { color: #4070a0; } /* String */
code span.va { color: #19177c; } /* Variable */
code span.vs { color: #4070a0; } /* VerbatimString */
code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */
</style>
<script>
// apply pandoc div.sourceCode style to pre.sourceCode instead
(function() {
var sheets = document.styleSheets;
for (var i = 0; i < sheets.length; i++) {
if (sheets[i].ownerNode.dataset["origin"] !== "pandoc") continue;
try { var rules = sheets[i].cssRules; } catch (e) { continue; }
for (var j = 0; j < rules.length; j++) {
var rule = rules[j];
// check if there is a div.sourceCode rule
if (rule.type !== rule.STYLE_RULE || rule.selectorText !== "div.sourceCode") continue;
var style = rule.style.cssText;
// check if color or background-color is set
if (rule.style.color === '' && rule.style.backgroundColor === '') continue;
// replace div.sourceCode by a pre.sourceCode rule
sheets[i].deleteRule(j);
sheets[i].insertRule('pre.sourceCode{' + style + '}', j);
}
}
})();
</script>
<style type="text/css">body {
background-color: #fff;
margin: 1em auto;
max-width: 700px;
overflow: visible;
padding-left: 2em;
padding-right: 2em;
font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
font-size: 14px;
line-height: 1.35;
}
#header {
text-align: center;
}
#TOC {
clear: both;
margin: 0 0 10px 10px;
padding: 4px;
width: 400px;
border: 1px solid #CCCCCC;
border-radius: 5px;
background-color: #f6f6f6;
font-size: 13px;
line-height: 1.3;
}
#TOC .toctitle {
font-weight: bold;
font-size: 15px;
margin-left: 5px;
}
#TOC ul {
padding-left: 40px;
margin-left: -1.5em;
margin-top: 5px;
margin-bottom: 5px;
}
#TOC ul ul {
margin-left: -2em;
}
#TOC li {
line-height: 16px;
}
table {
margin: 1em auto;
border-width: 1px;
border-color: #DDDDDD;
border-style: outset;
border-collapse: collapse;
}
table th {
border-width: 2px;
padding: 5px;
border-style: inset;
}
table td {
border-width: 1px;
border-style: inset;
line-height: 18px;
padding: 5px 5px;
}
table, table th, table td {
border-left-style: none;
border-right-style: none;
}
table thead, table tr.even {
background-color: #f7f7f7;
}
p {
margin: 0.5em 0;
}
blockquote {
background-color: #f6f6f6;
padding: 0.25em 0.75em;
}
hr {
border-style: solid;
border: none;
border-top: 1px solid #777;
margin: 28px 0;
}