Commit d4dfc9da authored by Armin Rauschenberger's avatar Armin Rauschenberger

automation

parent 1740863b
......@@ -205,7 +205,7 @@ joinet <- function(Y,X,family="gaussian",nfolds=10,foldid=NULL,type.measure="dev
}
.mean.function <- function(x,family){
if(family=="gaussian"){
if(family %in% c("gaussian","cox")){
return(x)
} else if(family=="binomial"){
return(1/(1+exp(-x)))
......@@ -217,7 +217,7 @@ joinet <- function(Y,X,family="gaussian",nfolds=10,foldid=NULL,type.measure="dev
}
.link.function <- function(x,family){
if(family=="gaussian"){
if(family %in% c("gaussian","cox")){
return(x)
} else if(family=="binomial"){
if(any(x<0|x>1)){stop("Invalid!",call.=FALSE)}
......@@ -262,7 +262,8 @@ joinet <- function(Y,X,family="gaussian",nfolds=10,foldid=NULL,type.measure="dev
#' 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)
#' 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",...){
......@@ -279,8 +280,8 @@ predict.joinet <- function(object,newx,type="response",...){
# base learners
for(i in seq_len(q)){
#base[,i] <- as.numeric(stats::predict(object=x$base[[i]]$glmnet.fit,newx=newx,s=x$base[[i]]$lambda.min,type="link"))
base[,i] <- as.numeric(glmnet::predict.cv.glmnet(object=x$base[[i]],newx=newx,s="lambda.min",type="link"))
base[,i] <- as.numeric(stats::predict(object=x$base[[i]]$glmnet.fit,newx=newx,s=x$base[[i]]$lambda.min,type="link"))
#base[,i] <- as.numeric(glmnet:::predict.cv.glmnet(object=x$base[[i]],newx=newx,s="lambda.min",type="link"))
# check whether fine for "binomial" family
}
......@@ -340,7 +341,7 @@ coef.joinet <- function(object,...){
# base coefficients
base <- list()
coef <- sapply(object$base,function(x) glmnet::coef.glmnet(object=x$glmnet.fit,s=x$lambda.min))
coef <- sapply(object$base,function(x) stats::coef(object=x$glmnet.fit,s=x$lambda.min))
base$alpha <- sapply(coef,function(x) x[1,])
base$beta <- sapply(coef,function(x) x[-1,])
names(base$alpha) <- colnames(base$beta) <- names(object$base)
......@@ -405,7 +406,7 @@ coef.joinet <- function(object,...){
weights.joinet <- function(object,...){
if(length(list(...))!=0){warning("Ignoring argument.",call.=FALSE)}
x <- object$meta
coef <- lapply(object$meta,function(x) glmnet::coef.glmnet(object=x,s=x$lambda.min))
coef <- lapply(object$meta,function(x) stats::coef(object=x,s=x$lambda.min))
coef <- do.call(what="cbind",args=coef)
coef <- as.matrix(coef)
colnames(coef) <- names(object$meta)
......@@ -449,6 +450,9 @@ print.joinet <- function(x,...){
#' experimental arguments\strong{:}
#' logical (requires packages \code{spls}, \code{SiER}, or \code{MRCE})
#'
#' @param cvpred
#' return cross-validated predicition: logical
#'
#' @param ...
#' further arguments passed to \code{\link[glmnet]{glmnet}}
#' and \code{\link[glmnet]{cv.glmnet}}
......@@ -504,7 +508,7 @@ print.joinet <- function(x,...){
#' set.seed(1)
#' cv.joinet(Y=Y,X=X,alpha.base=0) # ridge}
#'
cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ext=NULL,foldid.int=NULL,type.measure="deviance",alpha.base=1,alpha.meta=0,mnorm=FALSE,spls=FALSE,sier=FALSE,mrce=FALSE,...){
cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ext=NULL,foldid.int=NULL,type.measure="deviance",alpha.base=1,alpha.meta=0,mnorm=FALSE,spls=FALSE,sier=FALSE,mrce=FALSE,cvpred=FALSE,...){
n <- nrow(Y)
q <- ncol(Y)
......@@ -557,7 +561,7 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
if(mnorm){
net <- glmnet::cv.glmnet(x=X0,y=y0,family="mgaussian",foldid=foldid,...) # ellipsis
pred$mnorm[foldid.ext==i,] <- glmnet::predict.cv.glmnet(object=net,newx=X1,s="lambda.min",type="response")
pred$mnorm[foldid.ext==i,] <- stats::predict(object=net,newx=X1,s="lambda.min",type="response")
}
if(spls){
cv.spls <- spls::cv.spls(x=x0,y=y0,fold=nfolds.int,K=seq_len(10),
......@@ -595,5 +599,9 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
#fit <- joinet(Y=Y,X=X,family=family,type.measure=type.measure,alpha.base=alpha.base,alpha.meta=alpha.meta) # add ,...
#list <- list(loss=loss,fit=fit)
if(cvpred){
loss <- list(loss=loss,cvpred=pred$meta)
}
return(loss)
}
# Notes
Please accept ::: calls to "palasso" and "cornet",
as maintainer email changes for all packages.
\ No newline at end of file
- Package runs without errors with palasso 0.0.6 (pending manual inspection).
\ No newline at end of file
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<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.">
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<a href="#installation" class="anchor"></a>Installation</h2>
<p>Install the current release from <a href="https://CRAN.R-project.org/package=joinet">CRAN</a>:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="kw"><a href="https://www.rdocumentation.org/packages/utils/topics/install.packages">install.packages</a></span>(<span class="st">"joinet"</span>)</a></code></pre></div>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="kw"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span>(<span class="st">"joinet"</span>)</a></code></pre></div>
<p>or the latest development version from <a href="https://github.com/rauschenberger/joinet">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://www.rdocumentation.org/packages/devtools/topics/reexports">install_github</a></span>(<span class="st">"rauschenberger/joinet"</span>)</a></code></pre></div>
<a class="sourceLine" id="cb2-2" title="2">devtools<span class="op">::</span><span class="kw"><a href="https://rdrr.io/pkg/devtools/man/remote-reexports.html">install_github</a></span>(<span class="st">"rauschenberger/joinet"</span>)</a></code></pre></div>
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<a href="#reference" class="anchor"></a>Reference</h2>
<p>Armin Rauschenberger and Enrico Glaab (2019). “joinet: predicting correlated outcomes jointly to improve clinical prognosis”. <em>Manuscript in preparation.</em></p>
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