vignette

CRAN-RELEASE

-This package was submitted to CRAN on 2020-10-21.
-Once it is accepted, delete this file and tag the release (commit 8eb7bf5).

R/functions.R

@@ -89,15 +89,12 @@
 #' n <- 50; p <- 100; q <- 3
 #' 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)
-#' }}
-#' 
+#' object <- joinet(Y=Y,X=X)}}
 #' \dontrun{
 #' n <- 50; p <- 100; q <- 3
 #' 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)
-#' }
+#' object <- joinet(Y=Y,X=X)}
 #' 
 #' \dontrun{
 #' browseVignettes("joinet") # further examples}
@@ -284,14 +281,21 @@ joinet <- function(Y,X,family="gaussian",nfolds=10,foldid=NULL,type.measure="dev
 #' with \eqn{n} rows (samples) and \eqn{q} columns (variables).
 #' 
 #' @examples
+#' \dontshow{
 #' if(!grepl('SunOS',Sys.info()['sysname'])){
 #' n <- 50; p <- 100; q <- 3
 #' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
 #' Y <- replicate(n=q,expr=rnorm(n=n,mean=rowSums(X[,1:5])))
 #' Y[,1] <- 1*(Y[,1]>median(Y[,1]))
 #' object <- joinet(Y=Y,X=X,family=c("binomial","gaussian","gaussian"))
-#' predict(object,newx=X)
-#' }
+#' predict(object,newx=X)}}
+#' \dontrun{
+#' n <- 50; p <- 100; q <- 3
+#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
+#' Y <- replicate(n=q,expr=rnorm(n=n,mean=rowSums(X[,1:5])))
+#' Y[,1] <- 1*(Y[,1]>median(Y[,1]))
+#' object <- joinet(Y=Y,X=X,family=c("binomial","gaussian","gaussian"))
+#' predict(object,newx=X)}
 #' 
 predict.joinet <- function(object,newx,type="response",...){
   if(length(list(...))!=0){warning("Ignoring argument.",call.=FALSE)}
@@ -357,13 +361,19 @@ predict.joinet <- function(object,newx,type="response",...){
 #' in a matrix with \eqn{p} rows (inputs) and \eqn{q} columns.
 #' 
 #' @examples
+#' \dontshow{
 #' if(!grepl('SunOS',Sys.info()['sysname'])){
 #' n <- 50; p <- 100; q <- 3
 #' 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)
-#' coef <- coef(object)
-#' }
+#' coef <- coef(object)}}
+#' \dontrun{
+#' n <- 50; p <- 100; q <- 3
+#' 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)
+#' coef <- coef(object)}
 #' 
 coef.joinet <- function(object,...){
   if(length(list(...))!=0){warning("Ignoring argument.",call.=FALSE)}
@@ -426,13 +436,19 @@ coef.joinet <- function(object,...){
 #' in the row on the outcomes in the column.
 #' 
 #' @examples
+#' \dontshow{
 #' if(!grepl('SunOS',Sys.info()['sysname'])){
 #' n <- 50; p <- 100; q <- 3
 #' 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)
-#' weights(object)
-#' }
+#' weights(object)}}
+#' \dontrun{
+#' n <- 50; p <- 100; q <- 3
+#' 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)
+#' weights(object)}
 #' 
 weights.joinet <- function(object,...){
   if(length(list(...))!=0){warning("Ignoring argument.",call.=FALSE)}
@@ -505,12 +521,17 @@ print.joinet <- function(x,...){
 #' and the intercept-only models (\code{none}).
 #' 
 #' @examples
+#' \dontshow{
 #' if(!grepl('SunOS',Sys.info()['sysname'])){
 #' n <- 50; p <- 100; q <- 3
 #' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
 #' Y <- replicate(n=q,expr=rnorm(n=n,mean=rowSums(X[,1:5])))
-#' cv.joinet(Y=Y,X=X)
-#' }
+#' cv.joinet(Y=Y,X=X)}}
+#' \dontrun{
+#' n <- 50; p <- 100; q <- 3
+#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
+#' Y <- replicate(n=q,expr=rnorm(n=n,mean=rowSums(X[,1:5])))
+#' cv.joinet(Y=Y,X=X)}
 #' 
 #' \dontrun{
 #' # correlated features

docs/pkgdown.yml

@@ -4,5 +4,5 @@ pkgdown_sha: ~
 articles:
   article: article.html
   joinet: joinet.html
-last_built: 2020-11-03T08:27Z
+last_built: 2020-11-03T08:45Z
 

docs/reference/coef.joinet.html

@@ -155,13 +155,19 @@ and the slot <code>beta</code> contains the slopes
 in a matrix with \(p\) rows (inputs) and \(q\) columns.</p>
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span>(!<span class='fu'><a href='https://rdrr.io/r/base/grep.html'>grepl</a></span>(<span class='st'>'SunOS'</span>,<span class='fu'><a href='https://rdrr.io/r/base/Sys.info.html'>Sys.info</a></span>()[<span class='st'>'sysname'</span>])){
+    <pre class="examples"><div class='input'><span class='co'># \dontshow{</span>
+<span class='kw'>if</span>(!<span class='fu'><a href='https://rdrr.io/r/base/grep.html'>grepl</a></span>(<span class='st'>'SunOS'</span>,<span class='fu'><a href='https://rdrr.io/r/base/Sys.info.html'>Sys.info</a></span>()[<span class='st'>'sysname'</span>])){
 <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://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/stats/Normal.html'>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://rdrr.io/r/base/lapply.html'>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://rdrr.io/r/stats/Normal.html'>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://rdrr.io/r/base/colSums.html'>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'><a href='joinet.html'>joinet</a></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>)
-<span class='no'>coef</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/coef.html'>coef</a></span>(<span class='no'>object</span>)
-}</div></pre>
+<span class='no'>coef</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/coef.html'>coef</a></span>(<span class='no'>object</span>)}<span class='co'># }</span>
+<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
+<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://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/stats/Normal.html'>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://rdrr.io/r/base/lapply.html'>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://rdrr.io/r/stats/Normal.html'>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://rdrr.io/r/base/colSums.html'>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'><a href='joinet.html'>joinet</a></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>)
+<span class='no'>coef</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/coef.html'>coef</a></span>(<span class='no'>object</span>)}</div></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
     <nav id="toc" data-toggle="toc" class="sticky-top">

docs/reference/predict.joinet.html

@@ -159,14 +159,14 @@ The slots <code>base</code> and <code>meta</code> each contain a matrix
 with \(n\) rows (samples) and \(q\) columns (variables).</p>
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span>(!<span class='fu'><a href='https://rdrr.io/r/base/grep.html'>grepl</a></span>(<span class='st'>'SunOS'</span>,<span class='fu'><a href='https://rdrr.io/r/base/Sys.info.html'>Sys.info</a></span>()[<span class='st'>'sysname'</span>])){
+    <pre class="examples"><div class='input'><span class='co'># \dontshow{</span>
+<span class='kw'>if</span>(!<span class='fu'><a href='https://rdrr.io/r/base/grep.html'>grepl</a></span>(<span class='st'>'SunOS'</span>,<span class='fu'><a href='https://rdrr.io/r/base/Sys.info.html'>Sys.info</a></span>()[<span class='st'>'sysname'</span>])){
 <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://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/stats/Normal.html'>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://rdrr.io/r/base/lapply.html'>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://rdrr.io/r/stats/Normal.html'>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://rdrr.io/r/base/colSums.html'>rowSums</a></span>(<span class='no'>X</span>[,<span class='fl'>1</span>:<span class='fl'>5</span>])))
 <span class='no'>Y</span>[,<span class='fl'>1</span>] <span class='kw'>&lt;-</span> <span class='fl'>1</span>*(<span class='no'>Y</span>[,<span class='fl'>1</span>]<span class='kw'>&gt;</span><span class='fu'><a href='https://rdrr.io/r/stats/median.html'>median</a></span>(<span class='no'>Y</span>[,<span class='fl'>1</span>]))
 <span class='no'>object</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='joinet.html'>joinet</a></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>,<span class='kw'>family</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"binomial"</span>,<span class='st'>"gaussian"</span>,<span class='st'>"gaussian"</span>))
-<span class='fu'><a href='https://rdrr.io/r/stats/predict.html'>predict</a></span>(<span class='no'>object</span>,<span class='kw'>newx</span><span class='kw'>=</span><span class='no'>X</span>)
-}</div><div class='output co'>#&gt; $base
+<span class='fu'><a href='https://rdrr.io/r/stats/predict.html'>predict</a></span>(<span class='no'>object</span>,<span class='kw'>newx</span><span class='kw'>=</span><span class='no'>X</span>)}<span class='co'># }</span></div><div class='output co'>#&gt; $base
 #&gt;             [,1]         [,2]        [,3]
 #&gt;  [1,] 0.41837245 -1.928585385 -2.49734037
 #&gt;  [2,] 0.73430703 -1.232615541 -1.92258279
@@ -271,8 +271,13 @@ with \(n\) rows (samples) and \(q\) columns (variables).</p>
 #&gt; [48,] 0.07567690 -3.2214496 -3.11672913
 #&gt; [49,] 0.58299820 -0.1185430 -0.03693255
 #&gt; [50,] 0.42367190 -0.9519434 -1.64962005
-#&gt; </div><div class='input'>
-</div></pre>
+#&gt; </div><div class='input'><span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
+<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://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/stats/Normal.html'>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://rdrr.io/r/base/lapply.html'>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://rdrr.io/r/stats/Normal.html'>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://rdrr.io/r/base/colSums.html'>rowSums</a></span>(<span class='no'>X</span>[,<span class='fl'>1</span>:<span class='fl'>5</span>])))
+<span class='no'>Y</span>[,<span class='fl'>1</span>] <span class='kw'>&lt;-</span> <span class='fl'>1</span>*(<span class='no'>Y</span>[,<span class='fl'>1</span>]<span class='kw'>&gt;</span><span class='fu'><a href='https://rdrr.io/r/stats/median.html'>median</a></span>(<span class='no'>Y</span>[,<span class='fl'>1</span>]))
+<span class='no'>object</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='joinet.html'>joinet</a></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>,<span class='kw'>family</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"binomial"</span>,<span class='st'>"gaussian"</span>,<span class='st'>"gaussian"</span>))
+<span class='fu'><a href='https://rdrr.io/r/stats/predict.html'>predict</a></span>(<span class='no'>object</span>,<span class='kw'>newx</span><span class='kw'>=</span><span class='no'>X</span>)}</div></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
     <nav id="toc" data-toggle="toc" class="sticky-top">

docs/reference/weights.joinet.html

@@ -154,18 +154,22 @@ which are the effects of the outcomes
 in the row on the outcomes in the column.</p>
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
-    <pre class="examples"><div class='input'><span class='kw'>if</span>(!<span class='fu'><a href='https://rdrr.io/r/base/grep.html'>grepl</a></span>(<span class='st'>'SunOS'</span>,<span class='fu'><a href='https://rdrr.io/r/base/Sys.info.html'>Sys.info</a></span>()[<span class='st'>'sysname'</span>])){
+    <pre class="examples"><div class='input'><span class='co'># \dontshow{</span>
+<span class='kw'>if</span>(!<span class='fu'><a href='https://rdrr.io/r/base/grep.html'>grepl</a></span>(<span class='st'>'SunOS'</span>,<span class='fu'><a href='https://rdrr.io/r/base/Sys.info.html'>Sys.info</a></span>()[<span class='st'>'sysname'</span>])){
 <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://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/stats/Normal.html'>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://rdrr.io/r/base/lapply.html'>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://rdrr.io/r/stats/Normal.html'>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://rdrr.io/r/base/colSums.html'>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'><a href='joinet.html'>joinet</a></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>)
-<span class='fu'><a href='https://rdrr.io/r/stats/weights.html'>weights</a></span>(<span class='no'>object</span>)
-}</div><div class='output co'>#&gt;                      y1          y2         y3
+<span class='fu'><a href='https://rdrr.io/r/stats/weights.html'>weights</a></span>(<span class='no'>object</span>)}<span class='co'># }</span></div><div class='output co'>#&gt;                      y1          y2         y3
 #&gt; (Intercept) -0.04720442 -0.15165929 0.26901703
 #&gt; V1           0.00000000  0.01158793 0.65726908
 #&gt; V2           0.55230103  0.71134918 0.45932382
-#&gt; V3           0.60228936  0.49505561 0.01764908</div><div class='input'>
-</div></pre>
+#&gt; V3           0.60228936  0.49505561 0.01764908</div><div class='input'><span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
+<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://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/stats/Normal.html'>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://rdrr.io/r/base/lapply.html'>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://rdrr.io/r/stats/Normal.html'>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://rdrr.io/r/base/colSums.html'>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'><a href='joinet.html'>joinet</a></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>)
+<span class='fu'><a href='https://rdrr.io/r/stats/weights.html'>weights</a></span>(<span class='no'>object</span>)}</div></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
     <nav id="toc" data-toggle="toc" class="sticky-top">

man/coef.joinet.Rd

@@ -24,12 +24,18 @@ Extracts pooled coefficients.
 the coefficients from the base learners.)
 }
 \examples{
+\dontshow{
 if(!grepl('SunOS',Sys.info()['sysname'])){
 n <- 50; p <- 100; q <- 3
 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)
-coef <- coef(object)
-}
+coef <- coef(object)}}
+\dontrun{
+n <- 50; p <- 100; q <- 3
+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)
+coef <- coef(object)}
 
 }

man/cv.joinet.Rd

@@ -87,12 +87,17 @@ and the intercept-only models (\code{none}).
 Compares univariate and multivariate regression.
 }
 \examples{
+\dontshow{
 if(!grepl('SunOS',Sys.info()['sysname'])){
 n <- 50; p <- 100; q <- 3
 X <- matrix(rnorm(n*p),nrow=n,ncol=p)
 Y <- replicate(n=q,expr=rnorm(n=n,mean=rowSums(X[,1:5])))
-cv.joinet(Y=Y,X=X)
-}
+cv.joinet(Y=Y,X=X)}}
+\dontrun{
+n <- 50; p <- 100; q <- 3
+X <- matrix(rnorm(n*p),nrow=n,ncol=p)
+Y <- replicate(n=q,expr=rnorm(n=n,mean=rowSums(X[,1:5])))
+cv.joinet(Y=Y,X=X)}
 
 \dontrun{
 # correlated features

man/joinet.Rd

@@ -78,15 +78,12 @@ if(!grepl('SunOS',Sys.info()['sysname'])){
 n <- 50; p <- 100; q <- 3
 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)
-}}
-
+object <- joinet(Y=Y,X=X)}}
 \dontrun{
 n <- 50; p <- 100; q <- 3
 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)
-}
+object <- joinet(Y=Y,X=X)}
 
 \dontrun{
 browseVignettes("joinet") # further examples}

man/predict.joinet.Rd

@@ -26,13 +26,20 @@ with \eqn{n} rows (samples) and \eqn{q} columns (variables).
 Predicts outcome from features with stacked model.
 }
 \examples{
+\dontshow{
 if(!grepl('SunOS',Sys.info()['sysname'])){
 n <- 50; p <- 100; q <- 3
 X <- matrix(rnorm(n*p),nrow=n,ncol=p)
 Y <- replicate(n=q,expr=rnorm(n=n,mean=rowSums(X[,1:5])))
 Y[,1] <- 1*(Y[,1]>median(Y[,1]))
 object <- joinet(Y=Y,X=X,family=c("binomial","gaussian","gaussian"))
-predict(object,newx=X)
-}
+predict(object,newx=X)}}
+\dontrun{
+n <- 50; p <- 100; q <- 3
+X <- matrix(rnorm(n*p),nrow=n,ncol=p)
+Y <- replicate(n=q,expr=rnorm(n=n,mean=rowSums(X[,1:5])))
+Y[,1] <- 1*(Y[,1]>median(Y[,1]))
+object <- joinet(Y=Y,X=X,family=c("binomial","gaussian","gaussian"))
+predict(object,newx=X)}
 
 }

man/weights.joinet.Rd

@@ -24,12 +24,18 @@ Extracts coefficients from the meta learner,
 i.e. the weights for the base learners.
 }
 \examples{
+\dontshow{
 if(!grepl('SunOS',Sys.info()['sysname'])){
 n <- 50; p <- 100; q <- 3
 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)
-weights(object)
-}
+weights(object)}}
+\dontrun{
+n <- 50; p <- 100; q <- 3
+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)
+weights(object)}
 
 }