automation

R/functions.R

@@ -492,11 +492,9 @@ print.starnet <- function(x,...){
 #' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
 #' y <- rnorm(n=n,mean=rowSums(X[,1:20]))
 #' \dontshow{
-#' loss <- cv.starnet(y=y,X=X,nfolds.ext=2,nfolds.int=3)
-#' }
+#' loss <- cv.starnet(y=y,X=X,nfolds.ext=2,nfolds.int=3)}
 #' \dontrun{
-#' loss <- cv.starnet(y=y,X=X)
-#' }
+#' 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,...){
   
@@ -574,9 +572,7 @@ cv.starnet <- function(y,X,family="gaussian",nalpha=21,alpha=NULL,nfolds.ext=10,
   return(list(meta=meta,base=base,extra=extra))
 }
 
-
-#' @name
-#' .simulate
+#' @name .simulate
 #' @title
 #' Simulation
 #'
@@ -622,8 +618,7 @@ cv.starnet <- function(y,X,family="gaussian",nalpha=21,alpha=NULL,nfolds.ext=10,
   }
   return(list(y=y,X=X))
 }
-
-#' @describeIn .simulate.block
+#' @rdname .simulate
 #' @param rho
 #' correlation\strong{:}
 #' numeric between \eqn{0} and \eqn{1}
@@ -644,8 +639,7 @@ cv.starnet <- function(y,X,family="gaussian",nalpha=21,alpha=NULL,nfolds.ext=10,
   y <- switch(family,gaussian=y,binomial=round(1/(1+exp(-y))),stop("Invalid."))
   return(list(y=y,X=X,beta=beta))
 }
-
-#' @describeIn .simulate.block
+#' @rdname .simulate
 #' 
 .simulate.mode <- function(n,p,mode,family="gaussian"){
   mean <- rep(x=0,times=p)
@@ -661,7 +655,6 @@ cv.starnet <- function(y,X,family="gaussian",nalpha=21,alpha=NULL,nfolds.ext=10,
   return(list(y=y,X=X,beta=beta))
 }
 
-
 #' @title 
 #' Loss
 #' 

R/pkgname.R

@@ -51,7 +51,8 @@
 #' # vector "beta": slopes
 #' 
 #' #--- model comparison ---
-#' loss <- cv.starnet(y=y,X=X)
+#' \dontrun{
+#' loss <- cv.starnet(y=y,X=X)}
 #' # cross-validated loss for different alpha,
 #' # and for tuning and stacking
 #' 

docs/articles/starnet.html

@@ -97,7 +97,7 @@
 <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>
-<p>A Rauschenberger, E Glaab, and MA van de Wiel (2020). “Predictive and interpretable models via the stacked elastic net”. <em>Manuscript in preparation.</em></p>
+<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>
 </div>
   </div>
 

docs/reference/cv.starnet.html

@@ -240,10 +240,8 @@ or <code>NULL</code>
 <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/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'>20</span>]))
 <span class='co'># \dontshow{</span>
-<span class='no'>loss</span> <span class='kw'>&lt;-</span> <span class='fu'>cv.starnet</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'>nfolds.ext</span><span class='kw'>=</span><span class='fl'>2</span>,<span class='kw'>nfolds.int</span><span class='kw'>=</span><span class='fl'>3</span>)</div><div class='img'><img src='cv.starnet-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># }</span>
-<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
-<span class='no'>loss</span> <span class='kw'>&lt;-</span> <span class='fu'>cv.starnet</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'>loss</span> <span class='kw'>&lt;-</span> <span class='fu'>cv.starnet</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'>nfolds.ext</span><span class='kw'>=</span><span class='fl'>2</span>,<span class='kw'>nfolds.int</span><span class='kw'>=</span><span class='fl'>3</span>)<span class='co'># }</span></div><div class='img'><img src='cv.starnet-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='no'>loss</span> <span class='kw'>&lt;-</span> <span class='fu'>cv.starnet</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>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>

docs/reference/starnet-package.html

@@ -172,10 +172,10 @@ Predictive and interpretable models via the stacked elastic net".
 <span class='co'># vector "beta": slopes</span>
 
 <span class='co'>#--- model comparison ---</span>
-<span class='no'>loss</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='cv.starnet.html'>cv.starnet</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>)</div><div class='img'><img src='starnet-package-1.png' alt='' width='700' height='433' /></div><div class='input'># cross-validated loss for different alpha,
-# and for tuning and stacking
-
-</div></pre>
+<span class='kw'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='no'>loss</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='cv.starnet.html'>cv.starnet</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='co'># cross-validated loss for different alpha,</span>
+<span class='co'># and for tuning and stacking</span></div></pre>
   </div>
   <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
     <h2>Contents</h2>

man/cv.starnet.Rd

@@ -90,10 +90,8 @@ n <- 50; p <- 20
 X <- matrix(rnorm(n*p),nrow=n,ncol=p)
 y <- rnorm(n=n,mean=rowSums(X[,1:20]))
 \dontshow{
-loss <- cv.starnet(y=y,X=X,nfolds.ext=2,nfolds.int=3)
-}
+loss <- cv.starnet(y=y,X=X,nfolds.ext=2,nfolds.int=3)}
 \dontrun{
-loss <- cv.starnet(y=y,X=X)
-}
+loss <- cv.starnet(y=y,X=X)}
 
 }

man/starnet-package.Rd

@@ -42,7 +42,8 @@ coef <- coef(object)
 # vector "beta": slopes
 
 #--- model comparison ---
-loss <- cv.starnet(y=y,X=X)
+\dontrun{
+loss <- cv.starnet(y=y,X=X)}
 # cross-validated loss for different alpha,
 # and for tuning and stacking
 

vignettes/starnet.Rmd

@@ -30,4 +30,4 @@ devtools::install_github("rauschenberger/starnet")
 
 A Rauschenberger, E Glaab, and MA van de Wiel (2020).
 "Predictive and interpretable models via the stacked elastic net".
-*Manuscript in preparation.*
+*Bioinformatics.* In press.