competing models

docs/articles/joinet.html

@@ -169,9 +169,69 @@
 
 ```r
 #install.packages("MTPS")
-data("HIV",package="MTPS",verbose=TRUE)
-loss1 <- cv.joinet(Y=YY,X=XX,mnorm=TRUE,mtps=TRUE)
+data("HIV",package="MTPS")
+loss1 <- cv.joinet(Y=YY,X=XX,mnorm=TRUE,spls=TRUE,mtps=TRUE)
 
+#install.packages("spls")
+data(yeast,package="spls")
+loss2 <- cv.joinet(Y=yeast$y,X=yeast$x,mnorm=TRUE,spls=TRUE,mtps=TRUE)
+
+data(mice,package="spls")
+loss3 <- cv.joinet(Y=mice$y,X=mice$x,mnorm=TRUE,spls=TRUE,mtps=TRUE)
+
+# install.packages("MRCE")
+data(stock04,package="MRCE",verbose=TRUE)
+# otherwise simulated
+
+#install.packages("SiER")
+# simulated!
+library(MASS)
+total.noise <- 0.1
+rho <- 0.3
+rho.e <- 0.2
+nvar=500
+nvarq <- 3
+sigma2 <- total.noise/nvarq
+sigmaX=0.1
+nvar.eff=150
+Sigma=matrix(0,nvar.eff,nvar.eff)
+for(i in 1:nvar.eff){
+    for(j in 1:nvar.eff){
+        Sigma[i,j]=rho^(abs(i-j))
+    }
+}
+Sigma2.y <- matrix(sigma2*rho.e,nvarq, nvarq)
+diag(Sigma2.y) <- sigma2
+betas.true <- matrix(0, nvar, 3)
+betas.true[1:15,1]=rep(1,15)/sqrt(15)
+betas.true[16:45,2]=rep(0.5,30)/sqrt(30)
+betas.true[46:105,3]=rep(0.25,60)/sqrt(60)
+ntest <- 500
+ntrain <- 90
+ntot <- ntest+ntrain
+X <- matrix(0,ntot,nvar)
+X[,1:nvar.eff] <- mvrnorm(n=ntot, rep(0, nvar.eff), Sigma)
+X[,-(1:nvar.eff)] <- matrix(sigmaX*rnorm((nvar-nvar.eff)*dim(X)[1]),
+                           dim(X)[1],(nvar-nvar.eff))
+Y <- X%*%betas.true
+Y <- Y+mvrnorm(n=ntot, rep(0,nvarq), Sigma2.y)
+fold <- rep(c(0,1),times=c(ntrain,ntest))
+loss4 <- cv.joinet(Y=Y,X=X,foldid.ext=fold,mnorm=TRUE,spls=TRUE,mtps=TRUE)
+
+#install.pacakges("GPM")
+# simulated!
+
+#install.packages("RMTL")
+# simulated!
+data <- RMTL::Create_simulated_data(Regularization="L21", #type="Regression")
+#Y <- (do.call(what="cbind",args=data$Y)+1)/2
+#X <- data$X[[1]] # example
+#loss2 <- cv.joinet(Y=Y,X=X,mnorm=TRUE,spls=TRUE,mtps=TRUE)
+```
+-->
+<!--
+
+```r
 #install.packages("plsgenomics")
 data(Ecoli,package="plsgenomics")
 X <- Ecoli$CONNECdata

docs/pkgdown.yml

@@ -4,5 +4,5 @@ pkgdown_sha: ~
 articles:
   article: article.html
   joinet: joinet.html
-last_built: 2020-06-05T12:17Z
+last_built: 2020-06-08T15:55Z
 

docs/reference/cv.joinet.html

@@ -136,14 +136,8 @@
   <span class='kw'>foldid.int</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
   <span class='kw'>type.measure</span> <span class='kw'>=</span> <span class='st'>"deviance"</span>,
   <span class='kw'>alpha.base</span> <span class='kw'>=</span> <span class='fl'>1</span>,
-  <span class='kw'>alpha.meta</span> <span class='kw'>=</span> <span class='fl'>0</span>,
-  <span class='kw'>mnorm</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>spls</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>mrce</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>sier</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>mtps</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>rmtl</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-  <span class='kw'>gpm</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
+  <span class='kw'>alpha.meta</span> <span class='kw'>=</span> <span class='fl'>1</span>,
+  <span class='kw'>compare</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
   <span class='kw'>mice</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
   <span class='kw'>cvpred</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
   <span class='no'>...</span>
@@ -211,10 +205,11 @@ numeric between \(0\) (ridge) and \(1\) (lasso)</p></td>
 numeric between \(0\) (ridge) and \(1\) (lasso)</p></td>
     </tr>
     <tr>
-      <th>mnorm, spls, mrce, sier, mtps, rmtl, gpm</th>
+      <th>compare</th>
       <td><p>experimental arguments<strong>:</strong>
-logical
-(<code>TRUE</code> requires packages <code>spls</code>, <code>MRCE</code>, <code>SiER</code>, <code>MTPS</code>, <code>RMTL</code> or <code>GPM</code>)</p></td>
+character vector with entries "mnorm", "spls", "mrce",
+"sier", "mtps", "rmtl", "gpm" and others
+(requires packages <code>spls</code>, <code>MRCE</code>, <code>SiER</code>, <code>MTPS</code>, <code>RMTL</code> or <code>GPM</code>)</p></td>
     </tr>
     <tr>
       <th>mice</th>

man/cv.joinet.Rd

@@ -14,14 +14,8 @@ cv.joinet(
   foldid.int = NULL,
   type.measure = "deviance",
   alpha.base = 1,
-  alpha.meta = 0,
-  mnorm = FALSE,
-  spls = FALSE,
-  mrce = FALSE,
-  sier = FALSE,
-  mtps = FALSE,
-  rmtl = FALSE,
-  gpm = FALSE,
+  alpha.meta = 1,
+  compare = NULL,
   mice = FALSE,
   cvpred = FALSE,
   ...
@@ -66,9 +60,10 @@ numeric between \eqn{0} (ridge) and \eqn{1} (lasso)}
 \item{alpha.meta}{elastic net mixing parameter for meta learner\strong{:}
 numeric between \eqn{0} (ridge) and \eqn{1} (lasso)}
 
-\item{mnorm, spls, mrce, sier, mtps, rmtl, gpm}{experimental arguments\strong{:}
-logical
-(\code{TRUE} requires packages \code{spls}, \code{MRCE}, \code{SiER}, \code{MTPS}, \code{RMTL} or \code{GPM})}
+\item{compare}{experimental arguments\strong{:}
+character vector with entries "mnorm", "spls", "mrce",
+"sier", "mtps", "rmtl", "gpm" and others
+(requires packages \code{spls}, \code{MRCE}, \code{SiER}, \code{MTPS}, \code{RMTL} or \code{GPM})}
 
 \item{mice}{missing data imputation\strong{:}
 logical (\code{mice=TRUE} requires package \code{mice})}

vignettes/joinet.Rmd

@@ -125,12 +125,73 @@ cv.joinet(Y=Y,X=X,family=family)
 
 Armin Rauschenberger and Enrico Glaab (2020). "joinet: predicting correlated outcomes jointly to improve clinical prognosis". *Manuscript in preparation.* 
 
+
 <!--
 ```{r,eval=FALSE}
 #install.packages("MTPS")
-data("HIV",package="MTPS",verbose=TRUE)
-loss1 <- cv.joinet(Y=YY,X=XX,mnorm=TRUE,mtps=TRUE)
+data("HIV",package="MTPS")
+loss1 <- cv.joinet(Y=YY,X=XX,mnorm=TRUE,spls=TRUE,mtps=TRUE)
+
+#install.packages("spls")
+data(yeast,package="spls")
+loss2 <- cv.joinet(Y=yeast$y,X=yeast$x,mnorm=TRUE,spls=TRUE,mtps=TRUE)
+
+data(mice,package="spls")
+loss3 <- cv.joinet(Y=mice$y,X=mice$x,mnorm=TRUE,spls=TRUE,mtps=TRUE)
+
+# install.packages("MRCE")
+data(stock04,package="MRCE",verbose=TRUE)
+# otherwise simulated
+
+#install.packages("SiER")
+# simulated!
+library(MASS)
+total.noise <- 0.1
+rho <- 0.3
+rho.e <- 0.2
+nvar=500
+nvarq <- 3
+sigma2 <- total.noise/nvarq
+sigmaX=0.1
+nvar.eff=150
+Sigma=matrix(0,nvar.eff,nvar.eff)
+for(i in 1:nvar.eff){
+    for(j in 1:nvar.eff){
+        Sigma[i,j]=rho^(abs(i-j))
+    }
+}
+Sigma2.y <- matrix(sigma2*rho.e,nvarq, nvarq)
+diag(Sigma2.y) <- sigma2
+betas.true <- matrix(0, nvar, 3)
+betas.true[1:15,1]=rep(1,15)/sqrt(15)
+betas.true[16:45,2]=rep(0.5,30)/sqrt(30)
+betas.true[46:105,3]=rep(0.25,60)/sqrt(60)
+ntest <- 500
+ntrain <- 90
+ntot <- ntest+ntrain
+X <- matrix(0,ntot,nvar)
+X[,1:nvar.eff] <- mvrnorm(n=ntot, rep(0, nvar.eff), Sigma)
+X[,-(1:nvar.eff)] <- matrix(sigmaX*rnorm((nvar-nvar.eff)*dim(X)[1]),
+                           dim(X)[1],(nvar-nvar.eff))
+Y <- X%*%betas.true
+Y <- Y+mvrnorm(n=ntot, rep(0,nvarq), Sigma2.y)
+fold <- rep(c(0,1),times=c(ntrain,ntest))
+loss4 <- cv.joinet(Y=Y,X=X,foldid.ext=fold,mnorm=TRUE,spls=TRUE,mtps=TRUE)
+
+#install.pacakges("GPM")
+# simulated!
+
+#install.packages("RMTL")
+# simulated!
+data <- RMTL::Create_simulated_data(Regularization="L21", #type="Regression")
+#Y <- (do.call(what="cbind",args=data$Y)+1)/2
+#X <- data$X[[1]] # example
+#loss2 <- cv.joinet(Y=Y,X=X,mnorm=TRUE,spls=TRUE,mtps=TRUE)
+```
+-->
 
+<!--
+```{r,eval=FALSE}
 #install.packages("plsgenomics")
 data(Ecoli,package="plsgenomics")
 X <- Ecoli$CONNECdata