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joinet
Commit 20ba57e5 authored by Armin Rauschenberger's avatar Armin Rauschenberger
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competing models

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R/functions.R
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......@@ -90,7 +90,7 @@
#' \dontrun{
#' browseVignettes("joinet") # further examples}
#'
joinet <- function(Y,X,family="gaussian",nfolds=10,foldid=NULL,type.measure="deviance",alpha.base=1,alpha.meta=0,...){
joinet <- function(Y,X,family="gaussian",nfolds=10,foldid=NULL,type.measure="deviance",alpha.base=1,alpha.meta=1,...){
#--- temporary ---
# family <- "gaussian"; nfolds <- 10; foldid <- NULL; type.measure <- "deviance"
......@@ -100,7 +100,19 @@ joinet <- function(Y,X,family="gaussian",nfolds=10,foldid=NULL,type.measure="dev
X <- as.matrix(X)
cornet:::.check(x=Y,type="matrix",miss=TRUE)
if(any(stats::cor(Y,use="pairwise.complete.obs")<0,na.rm=TRUE)){warning("Negative correlation!",call.=FALSE)}
### trial start ###
for(i in 1:(ncol(Y)-1)){
for(j in i:ncol(Y)){
cor <- stats::cor.test(Y[,i],Y[,j],use="pairwise.complete.obs")
if(cor$statistic<0 & cor$p.value<0.05){
warning(paste("Columns",i,"and",j,"are negatively correlated."))
}
}
}
### trial end ###
#if(any(stats::cor(Y,use="pairwise.complete.obs")<0,na.rm=TRUE)){warning("Negative correlation!",call.=FALSE)}
cornet:::.check(x=X,type="matrix")
#cornet:::.check(x=family,type="vector",values=c("gaussian","binomial","poisson"))
if(nrow(Y)!=nrow(X)){stop("Contradictory sample size.",call.=FALSE)}
......@@ -515,11 +527,15 @@ 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=1,compare=NULL,mice=FALSE,cvpred=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=1,compare=NULL,mice=FALSE,cvpred=FALSE,times=FALSE,...){
if(FALSE){
nfolds.ext <- 5; nfolds.int <- 10; foldid.ext <- foldid.int <- NULL; type.measure <- "deviance"; alpha.base <- 1; alpha.meta <- 1; compare <- c("mnorm","spls","mars","mrf"); mice <- cvpred <- TRUE
nfolds.ext <- 1; foldid.ext <- fold; nfolds.int <- 10; foldid.int <- NULL
family <- "gaussian"; nfolds.ext <- 5; nfolds.int <- 10; foldid.ext <- foldid.int <- NULL; type.measure <- "deviance"; alpha.base <- alpha.meta <- 1; mice <- cvpred <- times <- FALSE
#nfolds.ext <- 1; foldid.ext <- fold; nfolds.int <- 10; foldid.int <- NULL; compare <- TRUE
}
if(!is.null(compare) && length(compare)==1 && compare){
compare <- c("mnorm","spls","mars","mrf","map","rmtl","mtps","mcen", "gpm","sier","mrce")
}
n <- nrow(Y)
......@@ -546,7 +562,7 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
for(i in seq_along(compare)){
pkg <- switch(compare[i],mnorm="glmnet",mars="earth",spls="spls",mtps="MTPS",
rmtl="RMTL",mrf="MultivariateRandomForest",mcen="mcen",
map="remMap",stop("Invalid method.",call.FALSE))
map="remMap",sier="SiER",gpm="GPM",mrce="MRCE",ecc="MLPUGS",stop("Invalid method.",call.=FALSE))
if(!pkg %in% pkgs){
stop("Method \"",compare[i],"\" requires package \"",pkg,"\".",call.=FALSE)
}
......@@ -564,14 +580,23 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
models <- c("base","meta",compare,"none")
pred <- lapply(X=models,function(x) matrix(data=NA,nrow=n,ncol=q))
names(pred) <- models
time <- lapply(X=models,function(x) NA)
names(pred) <- names(time) <- models
for(i in seq_len(nfolds.ext)){
Y0 <- Y[foldid.ext!=i,,drop=FALSE]
#Y1 <- Y[foldid.ext==i,,drop=FALSE]
X0 <- X[foldid.ext!=i,,drop=FALSE]
X1 <- X[foldid.ext==i,,drop=FALSE]
# standardise features (trial)
#mu <- apply(X=X0,MARGIN=2,FUN=function(x) mean(x))
#sd <- apply(X=X0,MARGIN=2,FUN=function(x) stats::sd(x))
#X0 <- t((t(X0)-mu)/sd)[,sd!=0]
#X1 <- t((t(X1)-mu)/sd)[,sd!=0]
# or standardise once before cv?
# remove constant features
cond <- apply(X=X0,MARGIN=2,FUN=function(x) stats::sd(x)!=0)
X0 <- X0[,cond]; X1 <- X1[,cond]
......@@ -583,10 +608,14 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
}
# base and meta learners
start <- Sys.time()
fit <- joinet(Y=Y0,X=X0,family=family,type.measure=type.measure,alpha.base=alpha.base,alpha.meta=alpha.meta,foldid=foldid) # add ellipsis (...)
# also do not standardise!
temp <- predict.joinet(fit,newx=X1)
pred$base[foldid.ext==i,] <- temp$base
pred$meta[foldid.ext==i,] <- temp$meta
end <- Sys.time()
time$meta <- as.numeric(difftime(end,start,units="secs"))
# missing values
if(mice & any(is.na(Y0))){
......@@ -603,32 +632,54 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
# other learners
if("mnorm" %in% compare){
cat("mnorm"," ")
start <- Sys.time()
if(any(family!="gaussian")){
stop("mnorm requires \"gaussian\" family.",call.=FALSE)
}
net <- glmnet::cv.glmnet(x=X0,y=y0,family="mgaussian",foldid=foldid,alpha=alpha.base) # add ellipsis (...)
pred$mnorm[foldid.ext==i,] <- stats::predict(object=net,newx=X1,s="lambda.min",type="response")
end <- Sys.time()
time$mnorm <- as.numeric(difftime(end,start,units="secs"))
} else {
net <- glmnet::glmnet(x=X0,y=y0,family="mgaussian")
}
if("mars" %in% compare){
cat("mars"," ")
start <- Sys.time()
if(all(family=="gaussian")){
object <- earth::earth(x=X0,y=y0)
object <- earth::earth(x=X0,y=y0,nfold=nfolds.int) # add:pmethod="cv"; new: nfolds and pmethod
# equivalent: object <- mda::mars(x=X0,y=y0)
} else if(all(family=="binomial")){
object <- earth::earth(x=X0,y=y0,glm=list(family=binomial))
object <- earth::earth(x=X0,y=y0,glm=list(family=stats::binomial),nfold=nfolds.int) # add pmethod="cv", new: nfolds and pmethod
} else {
stop("Invalid.",call.=FALSE)
stop("MARS requires either \"gaussian\" or \"binomial\" family.",call.=FALSE)
}
pred$mars[foldid.ext==i,] <- earth:::predict.earth(object=object,newdata=X1,type="response")
#object <- mda::mars(x=X0,y=Y0)
#pred$mars[foldid.ext==i,] <- mda:::predict.mars(object=object,newdata=X1)
end <- Sys.time()
time$mars <- as.numeric(difftime(end,start,units="secs"))
#mean((Y-pred$mars)^2,na.rm=TRUE)
}
if("spls" %in% compare){
cv.spls <- spls::cv.spls(x=X0,y=y0,fold=nfolds.int,K=seq_len(10),
eta=seq(from=0.1,to=0.9,by=0.1),plot.it=FALSE)
object <- spls::spls(x=X0,y=y0,K=cv.spls$K.opt,eta=cv.spls$eta.opt)
cat("spls"," ")
start <- Sys.time()
if(any(family!="gaussian")){
stop("spls requires \"gaussian\" family.")
}
invisible(utils::capture.output(cv.spls <- spls::cv.spls(x=X0,y=y0,fold=nfolds.int,K=seq_len(min(ncol(X0),10)),
eta=seq(from=0.1,to=0.9,by=0.1),plot.it=FALSE))) #scale.x=FALSE
object <- spls::spls(x=X0,y=y0,K=cv.spls$K.opt,eta=cv.spls$eta.opt) #scale.x=FALSE
pred$spls[foldid.ext==i,] <- spls::predict.spls(object=object,newx=X1,type="fit")
end <- Sys.time()
time$spls <- as.numeric(difftime(end,start,units="secs"))
}
if("mtps" %in% compare){
cat("mtps"," ")
start <- Sys.time()
if(alpha.base==0){
step1 <- MTPS::glmnet.ridge
} else if(alpha.base==1){
......@@ -636,6 +687,7 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
} else {
stop("MTPS requires alpha.base 0 or 1.",call.=FALSE)
}
#-------------------------------
#--- manual lambda.min start ---
#body <- body(step1)
......@@ -650,14 +702,36 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
#}
#--- manual lambda.min end ---
#-----------------------------
step2 <- MTPS::rpart1
#---------------------------
#--- manual kmeans start ---
#fun <- MTPS::cv.multiFit
#body <- body(fun)
#body[[11]][[3]][[3]] <- nrow(unique(y0))
#body(fun) <- body
#assignInNamespace(x="cv.multiFit",value=step1,ns="MTPS")
#--- manual kmeans end ---
#-------------------------
if(all(family=="gaussian")){
step2 <- MTPS::rpart1
} else if(all(family=="binomial")){
step2 <- MTPS::glm1
} else {
stop("MTPS requires family gaussian or binomial.",call.=FALSE)
}
object <- MTPS::MTPS(xmat=X0,ymat=y0,family=family,nfold=nfolds.int,
method.step1=step1,method.step2=step2)
method.step1=step1,method.step2=step2,cv=TRUE,residual=TRUE)
pred$mtps[foldid.ext==i,] <- MTPS::predict.MTPS(object=object,newdata=X1,type="response")
# nfold has no effect for cv=FALSE (default)
end <- Sys.time()
time$mtps <- as.numeric(difftime(end,start,units="secs"))
# now using cross-validation residual stacking (CVRS)
}
if("rmtl" %in% compare){
cat("rmtl"," ")
start <- Sys.time()
if(all(family=="gaussian")){
type <- "Regression"
y0l <- lapply(seq_len(ncol(y0)),function(i) y0[,i,drop=FALSE])
......@@ -671,91 +745,180 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
X1l <- lapply(seq_len(ncol(y0)),function(i) X1)
#---------------------------
#--- manual tuning start ---
Lam2_seq <- 10^seq(from=2,to=-5,length.out=101)
cvm <- numeric()
Lam1_seq <- c(10^seq(from=1,to=-4,length.out=11),0)
Lam2_seq <- c(10^seq(from=1,to=-4,length.out=11),0)
cvMTL <- list()
seed <- .Random.seed
for(j in seq_along(Lam2_seq)){
set.seed(1)
cvm[j] <- min(RMTL::cvMTL(X=X0l,Y=y0l,type=type,Lam1=0,Lam2=Lam2_seq[j])$cvm)
.Random.seed <- seed
cvMTL[[j]] <- RMTL::cvMTL(X=X0l,Y=y0l,type=type,Lam1_seq=Lam1_seq,Lam2=Lam2_seq[j])
}
#graphics::plot(x=Lam2_seq,y=cvm)
cvm <- vapply(X=cvMTL,FUN=function(x) min(x$cvm),FUN.VALUE=numeric(1))
Lam1 <- cvMTL[[which.min(cvm)]]$Lam1.min
Lam2 <- Lam2_seq[which.min(cvm)]
#graphics::plot(x=Lam2_seq,y=cvm)
#cat(Lam1," ",Lam2,"\n")
#--- manual tuning end ----
#--------------------------
cvMTL <- RMTL::cvMTL(X=X0l,Y=y0l,type=type,Lam2=Lam2)
MTL <- RMTL::MTL(X=X0l,Y=y0l,type=type,Lam1=cvMTL$Lam1.min)
MTL <- RMTL::MTL(X=X0l,Y=y0l,type=type,Lam1=Lam1,Lam2=Lam2)
temp <- RMTL:::predict.MTL(object=MTL,newX=X1l)
pred$rmtl[foldid.ext==i,] <- do.call(what="cbind",args=temp)
end <- Sys.time()
time$rmtl <- as.numeric(difftime(end,start,units="secs"))
}
if("mrf" %in% compare){
pred$mrf[foldid.ext==i,] <- MultivariateRandomForest::build_forest_predict(trainX=X0,trainY=Y0,
n_tree=10,m_feature=floor(ncol(X0)/3),min_leaf=5,testX=X1)
# use n_tree = 500
cat("mrf"," ")
start <- Sys.time()
if(any(family!="gaussian")){
stop("mrf requires \"gaussian\" family.")
}
pred$mrf[foldid.ext==i,] <- MultivariateRandomForest::build_forest_predict(trainX=X0,trainY=y0,
n_tree=100,m_feature=min(ncol(X)-1,5),min_leaf=5,testX=X1)
# use n_tree=500, m_feature=floor(ncol(X0)/3)
# alternative: IntegratedMRF
# Check why this does not work!
# Check why this does not work well!
end <- Sys.time()
time$mrf <- as.numeric(difftime(end,start,units="secs"))
}
if("mcen" %in% compare){
if(all(family=="gaussian")){mfamily <- "mgaussian"}
if(all(family=="binomial")){mfamily <- "mbinomial"}
object <- mcen::cv.mcen(x=X0,y=y0,family=mfamily,folds=foldid,ky=1)
cat("mcen"," ")
start <- Sys.time()
if(all(family=="gaussian")){
type <- "mgaussian"
} else if(all(family=="binomial")){
type <- "mbinomial"
} else {
stop("mcen requires either \"gaussian\" or \"binomial\".",call.=FALSE)
}
object <- mcen::cv.mcen(x=X0,y=y0,family=type,folds=foldid,ky=1,
gamma_y=seq(from=0.1,to=5.1,length.out=3),ndelta=3)
# TEMPORARY gamma_y and ndelta (for speed-up)
temp <- mcen:::predict.cv.mcen(object=object,newx=X1)
pred$mcen[foldid.ext==i,] <- as.matrix(temp)
# default returns error (thus ky=1)
# single cluster (ky=1) due to setting and error
end <- Sys.time()
time$mcen <- as.numeric(difftime(end,start,units="secs"))
}
if("map" %in% compare){
#if(alpha.base==0){
# lamL1.v <- 0
# lamL2.v <- fit$base$y1$lambda
#} else if(alpha.base==1){
# lamL1.v <- fit$base$y1$lambda
# lamL2.v <- 0
#} else {
# stop("")
#}
cat("map"," ")
start <- Sys.time()
if(any(family!="gaussian")){
stop("map requires \"gaussian\" family.")
}
mean <- colMeans(y0)
y0s <- y0-matrix(data=mean,nrow=nrow(X0),ncol=ncol(y0),byrow=TRUE)
temp <- glmnet::glmnet(x=X0,y=y0s,family="mgaussian",intercept=FALSE)
lamL1.v <- exp(seq(from=log(min(temp$lambda)),to=log(max(temp$lambda)),length.out=30))
lamL2.v <- seq(from=0,to=5,by=1) # TEMPORARY
lamL1.v <- exp(seq(from=log(10),to=log(20),length.out=11))
lamL2.v <- seq(from=0,to=5,length.out=11)
cv <- remMap::remMap.CV(X=X0,Y=y0s,lamL1.v=lamL1.v,lamL2.v=lamL2.v)
#graphics::plot(x=lamL1.v,y=log(as.numeric(cv$ols.cv[,3])))
index <- which(cv$ols.cv==min(cv$ols.cv),arr.ind=TRUE)
index <- which(cv$ols.cv==min(cv$ols.cv),arr.ind=TRUE)[1,]
#cat(index,"\n")
object <- remMap::remMap(X.m=X0,Y.m=y0s,lamL1=lamL1.v[index[1]],lamL2=lamL2.v[index[2]])
pred$map[foldid.ext==i,] <- matrix(data=mean,nrow=nrow(X1),ncol=ncol(y0),byrow=TRUE) + X1 %*% object$phi
# alternative: groupRemMap
end <- Sys.time()
time$map <- as.numeric(difftime(end,start,units="secs"))
}
if(FALSE){ #"mrce" %in% compare
# bug?
lam1 <- rev(10^seq(from=-2,to=0,by=0.5))
lam2 <- rev(10^seq(from=-2,to=0,by=0.5))
#lam1 <- lam2 <- 10^seq(from=0,to=-0.7,length.out=5)
#lam2 <- rev(10^seq(from=0,to=2,by=0.5))
#lam2 <- c(2,1,0.5)
#lam1 <- 10^seq(from=0,to=-5,length.out=11)
#lam2 <- seq(from=1,to=0.1,by=-0.1)
object <- MRCE::mrce(X=X0,Y=y0,lam1.vec=lam1,lam2.vec=lam2,method="cv",silent=FALSE,cov.tol=0.1,tol.out=1e-10)
pred$mrce[foldid.ext==i,] <- matrix(object$muhat,nrow=nrow(X1),ncol=q,byrow=TRUE) + x1 %*% object$Bhat
}
if(FALSE){ # "sier" %in% compare
stop("SiER not yet implemented",call.=FALSE) # slow!
object <- SiER::cv.SiER(X=X0,Y=y0,K.cv=5,thres=0.5)
if("sier" %in% compare){
cat("sier"," ")
start <- Sys.time()
if(any(family!="gaussian")){
stop("SiER requires \"gaussian\" family.",call.=FALSE)
}
invisible(utils::capture.output(object <- SiER::cv.SiER(X=X0,Y=y0,K.cv=5,upper.comp=10,thres=0.01)))
# should be upper.comp=10 and thres=0.01
pred$sier[foldid.ext==i,] <- SiER::pred.SiER(cv.fit=object,X.new=X1)
end <- Sys.time()
time$sier <- as.numeric(difftime(end,start,units="secs"))
}
if(FALSE){ #"gpm" %in% compare
if("gpm" %in% compare){
cat("gpm"," ")
start <- Sys.time()
if(any(family!="gaussian")){
stop("GPM requires \"gaussian\" family.",call.=FALSE)
}
object <- GPM::Fit(X=X0,Y=Y0)
object <- GPM::Fit(X=X0,Y=y0)
pred$gpm[foldid.ext==i,] <- GPM::Predict(XF=X1,Model=object)$YF
# Check why this does not work!
end <- Sys.time()
time$gpm <- as.numeric(difftime(end,start,units="secs"))
}
if("mrce" %in% compare){
cat("mrce"," ")
start <- Sys.time()
if(any(family!="gaussian")){
stop("MRCE requires \"gaussian\" family.",call.=FALSE) # check this!
}
lam1 <- lam2 <- 10^seq(from=1,to=-4,length.out=11)
invisible(utils::capture.output(trials <- lapply(lam2,function(x) tryCatch(expr=MRCE::mrce(X=X0,Y=y0,lam1.vec=lam1,lam2.vec=x,method="cv"),error=function(x) NULL))))
cv.err <- sapply(trials,function(x) ifelse(is.null(x),Inf,min(x$cv.err)))
object <- trials[[which.min(cv.err)]]
pred$mrce[foldid.ext==i,] <- matrix(object$muhat,nrow=nrow(X1),ncol=q,byrow=TRUE) + X1 %*% object$Bhat
end <- Sys.time()
time$mrce <- as.numeric(difftime(end,start,units="secs"))
}
if(FALSE){ "MSGLasso" %in% compare
if(!is.null(compare)){cat("\n")}
#--- development snippets ---
if(FALSE){ # "ecc"
start <- Sys.time()
cat("ecc"," ")
if(any(family!="binomial")){
stop("MLPUGS requires \"binomial\" family.",call.=FALSE)
}
X0f <- as.data.frame(X0)
y0f <- as.data.frame(y0)
X1f <- as.data.frame(X1)
if(FALSE){
# works!
object <- MLPUGS::ecc(x=X0f,y=y0f,.f=stats::glm.fit,family=stats::binomial(link="logit"))
pred_glm <- predict(object,X1f,
.f = function(glm_fit, newdata) {
eta <- as.matrix(newdata) %*% glm_fit$coef
output <- glm_fit$family$linkinv(eta)
colnames(output) <- "1"
return(output)
}, n.iters = 10, burn.in = 0, thin = 1)
}
if(FALSE){
# fails! respect same form for X0 and X1
object <- MLPUGS::ecc(x=X0,y=y0,.f=glmnet::cv.glmnet,family="binomial")
#temp <- MLPUGS:::predict.ECC(object,newx=X1,.f=glmnet::predict.cv.glmnet,newdata=X1)
pred_glm <- predict(object,X1,
.f = function(fit, newdata) {
newx <- as.matrix(newdata)
pred <- glmnet:::predict.cv.glmnet(fit,newx=newx,s="lambda.min",type="response")
return(as.matrix(pred))
}, n.iters = 10, burn.in = 0, thin = 1)
}
object <- MLPUGS::ecc(x=X0f,y=y0f,.f=randomForest::randomForest)
pred_ecc <- predict(object,newdata=X1f,n.iters=300,burn.in=100,thin=2,
.f = function(rF,newdata){randomForest:::predict.randomForest(rF, newdata, type = "prob")})
# works with n.iter=2 , burn.int=0 and thin=1 (bad choices!)
pred$ecc[foldid.ext==i,] <- summary(pred_ecc,type="prob")
# default seems to work, too, but might also leads to bad results in the
# (unrealistic) simulation
end <- Sys.time()
time$ecc <- as.numeric(difftime(end,start,units="secs"))
}
# if family=="binomial" test whether any pred outside unit interval
if(FALSE){ # "MSGLasso" %in% compare
MSGLasso::MSGLasso.cv(X=X0,Y=Y0)
# requires many user inputs
}
......@@ -801,6 +964,9 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
if(cvpred){
loss <- list(loss=loss,cvpred=pred$meta)
}
if(times){
loss <- list(loss=loss,time=time)
}
return(loss)
}
......
docs/articles/joinet.html
View file @ 20ba57e5
......@@ -158,95 +158,13 @@
<div class="sourceCode" id="cb11"><html><body><pre class="r"><span class="fu"><a href="../reference/cv.joinet.html">cv.joinet</a></span>(<span class="kw">Y</span><span class="kw">=</span><span class="no">Y</span>,<span class="kw">X</span><span class="kw">=</span><span class="no">X</span>,<span class="kw">family</span><span class="kw">=</span><span class="no">family</span>)</pre></body></html></div>
<pre><code>## [,1] [,2]
## base 1.204741 1.523550
## meta 1.161487 1.283678
## meta 1.185200 1.278125
## none 3.206394 3.495571</code></pre>
</div>
<div id="reference" class="section level2">
<h2 class="hasAnchor">
<a href="#reference" class="anchor"></a>Reference</h2>
<p>Armin Rauschenberger and Enrico Glaab (2020). “joinet: predicting correlated outcomes jointly to improve clinical prognosis”. <em>Manuscript in preparation.</em></p>
<!--
```r
#install.packages("MTPS")
data("HIV",package="MTPS")
loss1 <- cv.joinet(Y=YY,X=XX,mnorm=TRUE,spls=TRUE,mtps=TRUE)
#install.packages("spls")
data(yeast,package="spls")
loss2 <- cv.joinet(Y=yeast$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
Y <- Ecoli$GEdata
loss2 <- cv.joinet(Y=Y,X=X,mnorm=TRUE,mtps=TRUE)
#install.packages("BiocManager")
#BiocManager::install("mixOmics")
data(liver.toxicity,package="mixOmics")
X <- as.matrix(liver.toxicity$gene)
Y <- as.matrix(liver.toxicity$clinic)
Y[,"Cholesterol.mg.dL."] <- -Y[,"Cholesterol.mg.dL."]
loss3 <- cv.joinet(Y=Y,X=X,mnorm=TRUE,mtps=TRUE)
```
-->
</div>
</div>
......
docs/pkgdown.yml
View file @ 20ba57e5
......@@ -4,5 +4,5 @@ pkgdown_sha: ~
articles:
article: article.html
joinet: joinet.html
last_built: 2020-06-08T15:55Z
last_built: 2020-06-29T15:24Z
docs/reference/cv.joinet.html
View file @ 20ba57e5
......@@ -140,6 +140,7 @@
<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='kw'>times</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
<span class='no'>...</span>
)</pre>
......
docs/reference/joinet.html
View file @ 20ba57e5
......@@ -134,7 +134,7 @@
<span class='kw'>foldid</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'>alpha.meta</span> <span class='kw'>=</span> <span class='fl'>1</span>,
<span class='no'>...</span>
)</pre>
......
docs/reference/predict.joinet.html
View file @ 20ba57e5
......@@ -218,57 +218,57 @@ with \(n\) rows (samples) and \(q\) columns (variables).</p>
#&gt; [50,] 0.32655598 -1.650189753 -0.59297402
#&gt;
#&gt; $meta
#&gt; [,1] [,2] [,3]
#&gt; [1,] 0.120026857 -2.63552370 -2.58637745
#&gt; [2,] 0.311517089 -1.57742790 -1.80530334
#&gt; [3,] 0.970038575 2.83530787 2.50770389
#&gt; [4,] 0.988790465 3.94203727 3.41749124
#&gt; [5,] 0.702397389 0.20191699 -0.07301232
#&gt; [6,] 0.069874337 -3.01383050 -2.75113316
#&gt; [7,] 0.178293600 -1.79055373 -1.33607264
#&gt; [8,] 0.994417547 4.65204823 4.42063500
#&gt; [9,] 0.825454272 0.92712611 0.33068070
#&gt; [10,] 0.122295626 -2.58218395 -2.83894610
#&gt; [11,] 0.992972765 4.45732386 4.34260769
#&gt; [12,] 0.315048704 -0.99259865 -0.55695917
#&gt; [13,] 0.006376865 -5.52584767 -5.43421552
#&gt; [14,] 0.604946394 0.15758601 0.59296195
#&gt; [15,] 0.356113893 -0.95306415 -0.75195663
#&gt; [16,] 0.496057561 -0.75220207 -1.26978360
#&gt; [17,] 0.320794509 -1.10958000 -1.05497247
#&gt; [18,] 0.804808814 0.64766735 0.02552338
#&gt; [19,] 0.834600977 1.22772641 1.64662367
#&gt; [20,] 0.062637571 -3.37591257 -3.52530885
#&gt; [21,] 0.271715919 -1.32688363 -0.85251362
#&gt; [22,] 0.507637975 -0.60258988 -0.83624877
#&gt; [23,] 0.694579538 0.19562106 -0.51989858
#&gt; [24,] 0.406609311 -0.57645076 -0.18326513
#&gt; [25,] 0.012589218 -4.82289140 -4.55079851
#&gt; [26,] 0.935270035 2.28086412 2.23449684
#&gt; [27,] 0.143615352 -2.33101536 -2.18288391
#&gt; [28,] 0.971697981 2.86526752 2.61585773
#&gt; [29,] 0.013944811 -4.69966856 -4.55513448
#&gt; [30,] 0.015768781 -4.71928228 -4.44410746
#&gt; [31,] 0.891631228 1.50966480 1.39712353
#&gt; [32,] 0.853434630 1.11696980 0.44705866