automation

05b38bf4 · Armin Rauschenberger · f4df6530 · f4df6530 · 05b38bf4 · 05b38bf4
Commit 05b38bf4 authored Aug 08, 2019 by Armin Rauschenberger
--- a/CRAN-RELEASE
+++ b/CRAN-RELEASE
-This package was submitted to CRAN on 2019-08-02.
-Once it is accepted, delete this file and tag the release (commit a4d115213f).
--- a/DESCRIPTION
+++ b/DESCRIPTION
 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

--- a/NEWS.md
+++ b/NEWS.md
-## 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
--- a/R/functions.R
+++ b/R/functions.R
@@ -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

--- a/README.Rmd
+++ b/README.Rmd
@@ -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.*
--- a/README.md
+++ b/README.md
@@ -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.*
--- a/cran-comments.md
+++ b/cran-comments.md

-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
--- a/docs/articles/article.html
+++ b/docs/articles/article.html
@@ -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>


--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -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>

--- a/docs/articles/vignette.html
+++ b/docs/articles/vignette.html
@@ -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>


--- a/docs/index.html
+++ b/docs/index.html
@@ -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>


--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -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>

--- a/docs/reference/cv.joinet.html
+++ b/docs/reference/cv.joinet.html
@@ -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>

--- a/docs/reference/joinet.html
+++ b/docs/reference/joinet.html
@@ -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>

--- a/man/cv.joinet.Rd
+++ b/man/cv.joinet.Rd
@@ -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

--- a/man/joinet.Rd
+++ b/man/joinet.Rd
@@ -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
 }
--- a/vignettes/article.Rmd
+++ b/vignettes/article.Rmd
 ---
-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.*
--- a/vignettes/vignette.Rmd
+++ b/vignettes/vignette.Rmd
 ---
-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)
 ```
 -->
--- a/vignettes/vignette.html
+++ b/vignettes/vignette.html
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