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Commit 4c90151a authored by Armin Rauschenberger's avatar Armin Rauschenberger
Browse files

automation

parent a4d11521
This package was submitted to CRAN on 2019-07-31.
Once it is accepted, delete this file and tag the release (commit 030addd525).
This package was submitted to CRAN on 2019-08-02.
Once it is accepted, delete this file and tag the release (commit a4d115213f).
......@@ -80,9 +80,9 @@
#' \eqn{q} \code{\link[glmnet]{cv.glmnet}}-like objects.
#'
#' @examples
#' n <- 30; q <- 2; p <- 20
#' Y <- matrix(rnorm(n*q),nrow=n,ncol=q)
#' n <- 50; q <- 3; p <- 100
#' 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)
#'
joinet <- function(Y,X,family="gaussian",nfolds=10,foldid=NULL,type.measure="deviance",alpha.base=1,alpha.meta=0,...){
......@@ -254,13 +254,11 @@ joinet <- function(Y,X,family="gaussian",nfolds=10,foldid=NULL,type.measure="dev
#' with \eqn{n} rows (samples) and \eqn{q} columns (variables).
#'
#' @examples
#' n <- 30; q <- 2; p <- 20
#' #Y <- matrix(rnorm(n*q),nrow=n,ncol=q)
#' Y <- matrix(rbinom(n=n*q,size=1,prob=0.5),nrow=n,ncol=q)
#' #Y <- matrix(rpois(n=n*q,lambda=4),nrow=n,ncol=q)
#' n <- 50; q <- 3; p <- 100
#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
#' object <- joinet(Y=Y,X=X,family="binomial")
#' y_hat <- predict(object,newx=X)
#' Y <- replicate(n=q,expr=rnorm(n=n,mean=rowSums(X[,1:5])))
#' object <- joinet(Y=Y,X=X)
#' predict(object,newx=X)
#'
predict.joinet <- function(object,newx,type="response",...){
if(length(list(...))!=0){warning("Ignoring argument.",call.=FALSE)}
......@@ -326,9 +324,9 @@ predict.joinet <- function(object,newx,type="response",...){
#' in a matrix with \eqn{p} rows (inputs) and \eqn{q} columns.
#'
#' @examples
#' n <- 30; q <- 2; p <- 20
#' Y <- matrix(rnorm(n*q),nrow=n,ncol=q)
#' n <- 50; q <- 3; p <- 100
#' 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)
#'
......@@ -393,9 +391,9 @@ coef.joinet <- function(object,...){
#' in the row on the outcomes in the column.
#'
#' @examples
#' n <- 30; q <- 2; p <- 20
#' Y <- matrix(rnorm(n*q),nrow=n,ncol=q)
#' n <- 50; q <- 3; p <- 100
#' 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)
#'
......@@ -455,10 +453,34 @@ print.joinet <- function(x,...){
#' including the cross-validated loss.
#'
#' @examples
#' n <- 40; q <- 2; p <- 20
#' Y <- matrix(rnorm(n*q),nrow=n,ncol=q)
#' n <- 50; q <- 3; p <- 100
#' 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
#' n <- 50; q <- 3; p <- 100
#' mu <- rep(0,times=p)
#' Sigma <- 0.90^abs(col(diag(p))-row(diag(p)))
#' X <- MASS::mvrnorm(n=n,mu=mu,Sigma=Sigma)
#' mu <- rowSums(X[,sample(seq_len(p),size=5)])
#' Sigma <- diag(n)
#' Y <- t(MASS::mvrnorm(n=q,mu=mu,Sigma=Sigma))
#' cv.joinet(Y=Y,X=X)
#' }
#'
#' \dontrun{
#' # other distributions
#' n <- 50; q <- 3; p <- 100
#' mu <- rep(0,times=p)
#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
#' eta <- rowSums(X[,sample(seq_len(p),size=5)])
#' Y <- replicate(n=q,expr=rbinom(n=n,size=1,prob=1/(1+exp(-eta))))
#' cv.joinet(Y=Y,X=X,family="binomial")
#' Y <- replicate(n=q,expr=rpois(n=n,lambda=exp(scale(eta))))
#' cv.joinet(Y=Y,X=X,family="poisson")
#' }
#'
cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ext=NULL,foldid.int=NULL,type.measure="deviance",alpha.base=1,alpha.meta=0,mnorm=FALSE,spls=FALSE,sier=FALSE,mrce=FALSE,...){
......
# Notes
- DOI will be added after publication.
\ No newline at end of file
Thanks, I improved the description, added a DOI, and added the value fields.
\ No newline at end of file
......@@ -104,6 +104,8 @@
<div class="links">
<h2>Links</h2>
<ul class="list-unstyled">
<li>Download from CRAN at <br><a href="https://cloud.r-project.org/package=joinet">https://​cloud.r-project.org/​package=joinet</a>
</li>
<li>Browse source code at <br><a href="https://github.com/rauschenberger/mixnet">https://​github.com/​rauschenberger/​mixnet</a>
</li>
<li>Report a bug at <br><a href="https://github.com/rauschenberger/mixnet/issues">https://​github.com/​rauschenberger/​mixnet/​issues</a>
......
......@@ -149,9 +149,9 @@ 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='no'>n</span> <span class='kw'>&lt;-</span> <span class='fl'>30</span>; <span class='no'>q</span> <span class='kw'>&lt;-</span> <span class='fl'>2</span>; <span class='no'>p</span> <span class='kw'>&lt;-</span> <span class='fl'>20</span>
<span class='no'>Y</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'>q</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'>q</span>)
<pre class="examples"><div class='input'><span class='no'>n</span> <span class='kw'>&lt;-</span> <span class='fl'>50</span>; <span class='no'>q</span> <span class='kw'>&lt;-</span> <span class='fl'>3</span>; <span class='no'>p</span> <span class='kw'>&lt;-</span> <span class='fl'>100</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'><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://www.rdocumentation.org/packages/stats/topics/coef'>coef</a></span>(<span class='no'>object</span>)</div></pre>
</div>
......
......@@ -204,13 +204,35 @@ including the cross-validated loss.</p>
<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'>40</span>; <span class='no'>q</span> <span class='kw'>&lt;-</span> <span class='fl'>2</span>; <span class='no'>p</span> <span class='kw'>&lt;-</span> <span class='fl'>20</span>
<span class='no'>Y</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'>q</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'>q</span>)
<pre class="examples"><div class='input'><span class='no'>n</span> <span class='kw'>&lt;-</span> <span class='fl'>50</span>; <span class='no'>q</span> <span class='kw'>&lt;-</span> <span class='fl'>3</span>; <span class='no'>p</span> <span class='kw'>&lt;-</span> <span class='fl'>100</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='fu'>cv.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><div class='output co'>#&gt; [,1] [,2]
#&gt; base 1.040051 0.7996735
#&gt; meta 1.030695 0.7142891
#&gt; none 1.010304 0.7142039</div><div class='input'>
<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='fu'>cv.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><div class='output co'>#&gt; [,1] [,2] [,3]
#&gt; base 2.114462 1.909676 1.559970
#&gt; meta 1.163044 1.430791 1.327709
#&gt; none 6.028672 6.220040 6.206345</div><div class='input'>
</div><span class='co'># NOT RUN {</span>
<span class='co'># correlated features</span>
<span class='no'>n</span> <span class='kw'>&lt;-</span> <span class='fl'>50</span>; <span class='no'>q</span> <span class='kw'>&lt;-</span> <span class='fl'>3</span>; <span class='no'>p</span> <span class='kw'>&lt;-</span> <span class='fl'>100</span>
<span class='no'>mu</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/rep'>rep</a></span>(<span class='fl'>0</span>,<span class='kw'>times</span><span class='kw'>=</span><span class='no'>p</span>)
<span class='no'>Sigma</span> <span class='kw'>&lt;-</span> <span class='fl'>0.90</span>^<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/MathFun'>abs</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/col'>col</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/diag'>diag</a></span>(<span class='no'>p</span>))-<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/row'>row</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/diag'>diag</a></span>(<span class='no'>p</span>)))
<span class='no'>X</span> <span class='kw'>&lt;-</span> <span class='kw pkg'>MASS</span><span class='kw ns'>::</span><span class='fu'><a href='https://www.rdocumentation.org/packages/MASS/topics/mvrnorm'>mvrnorm</a></span>(<span class='kw'>n</span><span class='kw'>=</span><span class='no'>n</span>,<span class='kw'>mu</span><span class='kw'>=</span><span class='no'>mu</span>,<span class='kw'>Sigma</span><span class='kw'>=</span><span class='no'>Sigma</span>)
<span class='no'>mu</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/colSums'>rowSums</a></span>(<span class='no'>X</span>[,<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/sample'>sample</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/seq'>seq_len</a></span>(<span class='no'>p</span>),<span class='kw'>size</span><span class='kw'>=</span><span class='fl'>5</span>)])
<span class='no'>Sigma</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/diag'>diag</a></span>(<span class='no'>n</span>)
<span class='no'>Y</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/t'>t</a></span>(<span class='kw pkg'>MASS</span><span class='kw ns'>::</span><span class='fu'><a href='https://www.rdocumentation.org/packages/MASS/topics/mvrnorm'>mvrnorm</a></span>(<span class='kw'>n</span><span class='kw'>=</span><span class='no'>q</span>,<span class='kw'>mu</span><span class='kw'>=</span><span class='no'>mu</span>,<span class='kw'>Sigma</span><span class='kw'>=</span><span class='no'>Sigma</span>))
<span class='fu'>cv.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>)
<span class='co'># }</span><div class='input'>
</div><span class='co'># NOT RUN {</span>
<span class='co'># other distributions</span>
<span class='no'>n</span> <span class='kw'>&lt;-</span> <span class='fl'>50</span>; <span class='no'>q</span> <span class='kw'>&lt;-</span> <span class='fl'>3</span>; <span class='no'>p</span> <span class='kw'>&lt;-</span> <span class='fl'>100</span>
<span class='no'>mu</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/rep'>rep</a></span>(<span class='fl'>0</span>,<span class='kw'>times</span><span class='kw'>=</span><span class='no'>p</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'>eta</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/colSums'>rowSums</a></span>(<span class='no'>X</span>[,<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/sample'>sample</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/seq'>seq_len</a></span>(<span class='no'>p</span>),<span class='kw'>size</span><span class='kw'>=</span><span class='fl'>5</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/Binomial'>rbinom</a></span>(<span class='kw'>n</span><span class='kw'>=</span><span class='no'>n</span>,<span class='kw'>size</span><span class='kw'>=</span><span class='fl'>1</span>,<span class='kw'>prob</span><span class='kw'>=</span><span class='fl'>1</span>/(<span class='fl'>1</span>+<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/Log'>exp</a></span>(-<span class='no'>eta</span>))))
<span class='fu'>cv.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>,<span class='kw'>family</span><span class='kw'>=</span><span class='st'>"binomial"</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/Poisson'>rpois</a></span>(<span class='kw'>n</span><span class='kw'>=</span><span class='no'>n</span>,<span class='kw'>lambda</span><span class='kw'>=</span><span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/Log'>exp</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/scale'>scale</a></span>(<span class='no'>eta</span>))))
<span class='fu'>cv.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>,<span class='kw'>family</span><span class='kw'>=</span><span class='st'>"poisson"</span>)
<span class='co'># }</span><div class='input'>
</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
......
......@@ -206,9 +206,9 @@ ridge renders dense models (<code>alpha</code>\(=0\))</p>
<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'>30</span>; <span class='no'>q</span> <span class='kw'>&lt;-</span> <span class='fl'>2</span>; <span class='no'>p</span> <span class='kw'>&lt;-</span> <span class='fl'>20</span>
<span class='no'>Y</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'>q</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'>q</span>)
<pre class="examples"><div class='input'><span class='no'>n</span> <span class='kw'>&lt;-</span> <span class='fl'>50</span>; <span class='no'>q</span> <span class='kw'>&lt;-</span> <span class='fl'>3</span>; <span class='no'>p</span> <span class='kw'>&lt;-</span> <span class='fl'>100</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>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
......
......@@ -153,13 +153,117 @@ 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='no'>n</span> <span class='kw'>&lt;-</span> <span class='fl'>30</span>; <span class='no'>q</span> <span class='kw'>&lt;-</span> <span class='fl'>2</span>; <span class='no'>p</span> <span class='kw'>&lt;-</span> <span class='fl'>20</span>
<span class='co'>#Y &lt;- matrix(rnorm(n*q),nrow=n,ncol=q)</span>
<span class='no'>Y</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/Binomial'>rbinom</a></span>(<span class='kw'>n</span><span class='kw'>=</span><span class='no'>n</span>*<span class='no'>q</span>,<span class='kw'>size</span><span class='kw'>=</span><span class='fl'>1</span>,<span class='kw'>prob</span><span class='kw'>=</span><span class='fl'>0.5</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'>q</span>)
<span class='co'>#Y &lt;- matrix(rpois(n=n*q,lambda=4),nrow=n,ncol=q)</span>
<pre class="examples"><div class='input'><span class='no'>n</span> <span class='kw'>&lt;-</span> <span class='fl'>50</span>; <span class='no'>q</span> <span class='kw'>&lt;-</span> <span class='fl'>3</span>; <span class='no'>p</span> <span class='kw'>&lt;-</span> <span class='fl'>100</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'>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='st'>"binomial"</span>)
<span class='no'>y_hat</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/predict'>predict</a></span>(<span class='no'>object</span>,<span class='kw'>newx</span><span class='kw'>=</span><span class='no'>X</span>)</div></pre>
<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'><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://www.rdocumentation.org/packages/stats/topics/predict'>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
#&gt; [,1] [,2] [,3]
#&gt; [1,] -0.6800725 -1.21872520 -0.310657961
#&gt; [2,] 2.5231822 2.01759432 2.319092565
#&gt; [3,] 3.0147246 2.95961835 3.071353923
#&gt; [4,] -2.0321731 -1.16697865 -1.131585864
#&gt; [5,] 1.1989575 1.94542159 1.205214655
#&gt; [6,] -5.2181676 -4.73651108 -5.106512539
#&gt; [7,] 2.6630929 2.28298777 1.837353182
#&gt; [8,] 4.1517740 3.68154548 2.859426063
#&gt; [9,] 2.2255938 2.62966728 2.209039320
#&gt; [10,] -0.1976240 -0.26331544 -0.084736597
#&gt; [11,] -2.2848625 -2.88205673 -3.371158126
#&gt; [12,] 0.1395562 0.12678281 1.952331938
#&gt; [13,] 0.5319628 1.08030034 0.877379283
#&gt; [14,] -3.2092512 -3.20808021 -5.574818243
#&gt; [15,] 2.1373104 1.54594741 0.792750994
#&gt; [16,] 1.3677467 0.24229789 1.303661371
#&gt; [17,] 2.4203610 1.97149218 2.164521950
#&gt; [18,] -1.6439085 -1.13752492 -0.148270982
#&gt; [19,] -2.6971069 -3.21077918 -3.448781770
#&gt; [20,] 0.4847756 1.32920669 1.045263833
#&gt; [21,] 1.0580634 1.41141884 0.487897356
#&gt; [22,] -0.1159397 0.33252059 1.764586212
#&gt; [23,] -2.4576322 -2.61545228 -1.960495924
#&gt; [24,] -2.8963417 -2.86185997 -3.306570648
#&gt; [25,] 1.0818188 1.14369892 2.186762596
#&gt; [26,] -3.9271814 -3.84456309 -5.731322146
#&gt; [27,] -2.3652352 -2.86340632 -3.227402032
#&gt; [28,] -2.1800217 -1.48891855 -2.572985868
#&gt; [29,] 0.7460273 -0.27982198 -0.506445835
#&gt; [30,] -0.8429611 0.13194537 -0.737426505
#&gt; [31,] -0.7316201 -1.51707620 -1.153890045
#&gt; [32,] -2.5021179 -1.84304055 -3.478808874
#&gt; [33,] 0.8250113 -0.21440315 0.524465823
#&gt; [34,] 3.0831834 2.23039182 3.727105270
#&gt; [35,] 0.4993630 -1.34134635 -1.164539640
#&gt; [36,] 1.6239756 0.78835676 0.528243360
#&gt; [37,] 2.1683663 0.91851657 1.521169026
#&gt; [38,] -0.9397605 0.08426956 -0.751842377
#&gt; [39,] -0.2803268 -1.20184123 -0.560565492
#&gt; [40,] 1.9729289 3.26803044 2.090648484
#&gt; [41,] 0.7398532 -0.83577871 1.020203057
#&gt; [42,] -1.7847439 -2.02797038 -3.409427348
#&gt; [43,] 0.9128885 1.60609986 2.616774138
#&gt; [44,] 1.1536491 2.16601146 -0.015506128
#&gt; [45,] 0.6315267 0.22276528 0.985103241
#&gt; [46,] 5.4299519 5.38132076 6.438669837
#&gt; [47,] 1.3572642 -0.09607223 -1.249216010
#&gt; [48,] 0.3873464 0.41138065 -0.001234302
#&gt; [49,] 2.9278013 2.67791507 3.629988512
#&gt; [50,] -2.4898856 -2.53479214 -3.853167277
#&gt;
#&gt; $meta
#&gt; [,1] [,2] [,3]
#&gt; [1,] -0.59563267 -0.90002433 -0.8795599
#&gt; [2,] 2.67095768 2.47810147 2.5808935
#&gt; [3,] 3.45840247 3.30020002 3.3891269
#&gt; [4,] -1.46188457 -1.61687371 -1.8540286
#&gt; [5,] 1.63993174 1.62517210 1.4469087
#&gt; [6,] -5.46260433 -5.55647351 -6.0376931
#&gt; [7,] 2.55669035 2.49244482 2.4744017
#&gt; [8,] 3.95001353 3.95357827 3.9602483
#&gt; [9,] 2.67230496 2.60445934 2.5473784
#&gt; [10,] -0.06333041 -0.24056875 -0.3260788
#&gt; [11,] -3.08902202 -3.15794007 -3.4647823
#&gt; [12,] 1.14770503 0.68919861 0.8998934
#&gt; [13,] 1.01616716 0.90735085 0.7856638
#&gt; [14,] -4.57317204 -4.32126018 -5.0121129
#&gt; [15,] 1.66901306 1.65576325 1.5602187
#&gt; [16,] 1.30059629 0.97347727 1.1517702
#&gt; [17,] 2.54722966 2.37283946 2.4500803
#&gt; [18,] -0.83871748 -1.15700321 -1.2025308
#&gt; [19,] -3.35980259 -3.47252937 -3.7627773
#&gt; [20,] 1.14619325 1.05179463 0.9069345
#&gt; [21,] 1.10144529 1.11690231 0.9085406
#&gt; [22,] 1.01964166 0.62895804 0.7479229
#&gt; [23,] -2.39597908 -2.66035613 -2.7942077
#&gt; [24,] -3.27011549 -3.34669753 -3.6958912
#&gt; [25,] 1.86402073 1.54195928 1.6734630
#&gt; [26,] -5.07189459 -4.90372974 -5.5570170
#&gt; [27,] -3.04298327 -3.13330855 -3.4259584
#&gt; [28,] -2.29854182 -2.26246106 -2.6930823
#&gt; [29,] 0.06371202 -0.04398019 -0.1212793
#&gt; [30,] -0.50607066 -0.50300677 -0.8264913
#&gt; [31,] -1.10152893 -1.31322625 -1.3820411
#&gt; [32,] -2.94579505 -2.81601879 -3.3576427
#&gt; [33,] 0.60928985 0.33633488 0.4268274
#&gt; [34,] 3.60951584 3.21244112 3.5572606
#&gt; [35,] -0.62221676 -0.80139080 -0.8062647
#&gt; [36,] 1.15893179 1.05945946 1.0229827
#&gt; [37,] 1.86880093 1.62188578 1.7719707
#&gt; [38,] -0.56005416 -0.56280918 -0.8874085
#&gt; [39,] -0.57012886 -0.82870684 -0.8213634
#&gt; [40,] 2.69250620 2.75566901 2.5358990
#&gt; [41,] 0.65637153 0.19064789 0.4769644
#&gt; [42,] -2.70523844 -2.61931747 -3.0558713
#&gt; [43,] 2.13395943 1.81842962 1.9202530
#&gt; [44,] 1.08933714 1.32199345 0.8918323
#&gt; [45,] 0.87872139 0.60266852 0.6715000
#&gt; [46,] 6.58840317 6.27388617 6.6620334
#&gt; [47,] -0.03083230 0.02072140 -0.1677526
#&gt; [48,] 0.36265531 0.28827976 0.1348278
#&gt; [49,] 3.62457049 3.32176166 3.5519022
#&gt; [50,] -3.30474852 -3.23086153 -3.7021257
#&gt; </div><div class='input'>
</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
......
......@@ -148,13 +148,15 @@ 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='no'>n</span> <span class='kw'>&lt;-</span> <span class='fl'>30</span>; <span class='no'>q</span> <span class='kw'>&lt;-</span> <span class='fl'>2</span>; <span class='no'>p</span> <span class='kw'>&lt;-</span> <span class='fl'>20</span>
<span class='no'>Y</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'>q</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'>q</span>)
<pre class="examples"><div class='input'><span class='no'>n</span> <span class='kw'>&lt;-</span> <span class='fl'>50</span>; <span class='no'>q</span> <span class='kw'>&lt;-</span> <span class='fl'>3</span>; <span class='no'>p</span> <span class='kw'>&lt;-</span> <span class='fl'>100</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'>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>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Negative correlation!</span></div><div class='input'><span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/weights'>weights</a></span>(<span class='no'>object</span>)</div><div class='output co'>#&gt; y1 y2
#&gt; (Intercept) 0.065960017 -0.20281935
#&gt; V1 0.000000000 0.05786884
#&gt; V2 0.002141384 0.02447026</div><div class='input'>
<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'><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://www.rdocumentation.org/packages/stats/topics/weights'>weights</a></span>(<span class='no'>object</span>)</div><div class='output co'>#&gt; y1 y2 y3
#&gt; (Intercept) 0.4123532 0.2048455 0.3295532
#&gt; V1 0.3347547 0.7144811 0.4977934
#&gt; V2 0.4795655 0.1919470 0.3724097
#&gt; V3 0.6110722 0.4751144 0.4351376</div><div class='input'>
</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
......
......@@ -24,9 +24,9 @@ Extracts pooled coefficients.
the coefficients from the base learners.)
}
\examples{
n <- 30; q <- 2; p <- 20
Y <- matrix(rnorm(n*q),nrow=n,ncol=q)
n <- 50; q <- 3; p <- 100
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)
......
......@@ -62,9 +62,33 @@ including the cross-validated loss.
Compares univariate and multivariate regression
}
\examples{
n <- 40; q <- 2; p <- 20
Y <- matrix(rnorm(n*q),nrow=n,ncol=q)
n <- 50; q <- 3; p <- 100
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
n <- 50; q <- 3; p <- 100
mu <- rep(0,times=p)
Sigma <- 0.90^abs(col(diag(p))-row(diag(p)))
X <- MASS::mvrnorm(n=n,mu=mu,Sigma=Sigma)
mu <- rowSums(X[,sample(seq_len(p),size=5)])
Sigma <- diag(n)
Y <- t(MASS::mvrnorm(n=q,mu=mu,Sigma=Sigma))
cv.joinet(Y=Y,X=X)
}
\dontrun{
# other distributions
n <- 50; q <- 3; p <- 100
mu <- rep(0,times=p)
X <- matrix(rnorm(n*p),nrow=n,ncol=p)
eta <- rowSums(X[,sample(seq_len(p),size=5)])
Y <- replicate(n=q,expr=rbinom(n=n,size=1,prob=1/(1+exp(-eta))))
cv.joinet(Y=Y,X=X,family="binomial")
Y <- replicate(n=q,expr=rpois(n=n,lambda=exp(scale(eta))))
cv.joinet(Y=Y,X=X,family="poisson")
}
}
......@@ -65,9 +65,9 @@ lasso renders sparse models (\code{alpha}\eqn{=1}),
ridge renders dense models (\code{alpha}\eqn{=0})
}
\examples{
n <- 30; q <- 2; p <- 20
Y <- matrix(rnorm(n*q),nrow=n,ncol=q)
n <- 50; q <- 3; p <- 100
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)
}
......
......@@ -26,12 +26,10 @@ with \eqn{n} rows (samples) and \eqn{q} columns (variables).
Predicts outcome from features with stacked model.
}
\examples{
n <- 30; q <- 2; p <- 20
#Y <- matrix(rnorm(n*q),nrow=n,ncol=q)
Y <- matrix(rbinom(n=n*q,size=1,prob=0.5),nrow=n,ncol=q)
#Y <- matrix(rpois(n=n*q,lambda=4),nrow=n,ncol=q)
n <- 50; q <- 3; p <- 100
X <- matrix(rnorm(n*p),nrow=n,ncol=p)
object <- joinet(Y=Y,X=X,family="binomial")
y_hat <- predict(object,newx=X)
Y <- replicate(n=q,expr=rnorm(n=n,mean=rowSums(X[,1:5])))
object <- joinet(Y=Y,X=X)
predict(object,newx=X)
}
......@@ -24,9 +24,9 @@ Extracts coefficients from the meta learner,
i.e. the weights for the base learners.
}
\examples{
n <- 30; q <- 2; p <- 20
Y <- matrix(rnorm(n*q),nrow=n,ncol=q)
n <- 50; q <- 3; p <- 100
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)
......
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