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Commit 448ee92c authored by Armin Rauschenberger's avatar Armin Rauschenberger
Browse files

competing models

parent ffa77bbb
Package: joinet Package: joinet
Version: 0.0.4 Version: 0.0.5
Title: Multivariate Elastic Net Regression Title: Multivariate Elastic Net Regression
Description: Implements high-dimensional multivariate regression by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>). 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. Description: Implements high-dimensional multivariate regression by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>). 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.
Depends: R (>= 3.0.0) Depends: R (>= 3.0.0)
...@@ -11,6 +11,6 @@ VignetteBuilder: knitr ...@@ -11,6 +11,6 @@ VignetteBuilder: knitr
License: GPL-3 License: GPL-3
LazyData: true LazyData: true
Language: en-GB Language: en-GB
RoxygenNote: 7.1.0 RoxygenNote: 7.1.1
URL: https://github.com/rauschenberger/joinet URL: https://github.com/rauschenberger/joinet
BugReports: https://github.com/rauschenberger/joinet/issues BugReports: https://github.com/rauschenberger/joinet/issues
## joinet 0.0.5 (2020-10-02)
* updated documentation
## joinet 0.0.4 (2020-05-06) ## joinet 0.0.4 (2020-05-06)
* added competing models * added competing models
......
...@@ -53,9 +53,8 @@ ...@@ -53,9 +53,8 @@
#' further arguments passed to \code{\link[glmnet]{glmnet}} #' further arguments passed to \code{\link[glmnet]{glmnet}}
#' #'
#' @references #' @references
#' Armin Rauschenberger, Enrico Glaab (2019) #' Armin Rauschenberger, Enrico Glaab (2020)
#' "joinet: predicting correlated outcomes jointly #' "Predicting correlated outcomes from molecular data"
#' to improve clinical prognosis"
#' \emph{Manuscript in preparation}. #' \emph{Manuscript in preparation}.
#' #'
#' @details #' @details
...@@ -472,6 +471,10 @@ print.joinet <- function(x,...){ ...@@ -472,6 +471,10 @@ print.joinet <- function(x,...){
#' @param cvpred #' @param cvpred
#' return cross-validated predicitions: logical #' return cross-validated predicitions: logical
#' #'
#' @param times
#' measure computation time\strong{:}
#' logical
#'
#' @param ... #' @param ...
#' further arguments passed to \code{\link[glmnet]{glmnet}} #' further arguments passed to \code{\link[glmnet]{glmnet}}
#' and \code{\link[glmnet]{cv.glmnet}} #' and \code{\link[glmnet]{cv.glmnet}}
...@@ -684,7 +687,8 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex ...@@ -684,7 +687,8 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
# tune nprune (use default nk)! # tune nprune (use default nk)!
} }
pred$mars[foldid.ext==i,] <- earth:::predict.earth(object=object,newdata=X1,type="response") #pred$mars[foldid.ext==i,] <- earth:::predict.earth(object=object,newdata=X1,type="response") # original
pred$mars[foldid.ext==i,] <- stats::predict(object=object,newdata=X1,type="response") # trial
end <- Sys.time() end <- Sys.time()
time$mars <- as.numeric(difftime(end,start,units="secs")) time$mars <- as.numeric(difftime(end,start,units="secs"))
} }
...@@ -784,7 +788,8 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex ...@@ -784,7 +788,8 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
object <- mcen::cv.mcen(x=X0,y=y0,family=type,folds=foldid,ky=1, object <- mcen::cv.mcen(x=X0,y=y0,family=type,folds=foldid,ky=1,
gamma_y=seq(from=0.1,to=5.1,by=1),ndelta=5) gamma_y=seq(from=0.1,to=5.1,by=1),ndelta=5)
# TEMPORARY gamma_y=seq(from=0.1,to=5.1,length.out=3) and ndelta=3 (for speed-up) # TEMPORARY gamma_y=seq(from=0.1,to=5.1,length.out=3) and ndelta=3 (for speed-up)
temp <- mcen:::predict.cv.mcen(object=object,newx=X1) #temp <- mcen:::predict.cv.mcen(object=object,newx=X1) # original
temp <- stats::predict(object=object,newx=X1) # trial
pred$mcen[foldid.ext==i,] <- as.matrix(temp) pred$mcen[foldid.ext==i,] <- as.matrix(temp)
# single cluster (ky=1) due to setting and error # single cluster (ky=1) due to setting and error
end <- Sys.time() end <- Sys.time()
...@@ -835,7 +840,8 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex ...@@ -835,7 +840,8 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
#--- manual tuning end ---- #--- manual tuning end ----
#-------------------------- #--------------------------
MTL <- RMTL::MTL(X=X0l,Y=y0l,type=type,Lam1=Lam1,Lam2=Lam2) MTL <- RMTL::MTL(X=X0l,Y=y0l,type=type,Lam1=Lam1,Lam2=Lam2)
temp <- RMTL:::predict.MTL(object=MTL,newX=X1l) #temp <- RMTL:::predict.MTL(object=MTL,newX=X1l) # original
temp <- stats::predict(object=MTL,newX=X1l)
pred$rmtl[foldid.ext==i,] <- do.call(what="cbind",args=temp) pred$rmtl[foldid.ext==i,] <- do.call(what="cbind",args=temp)
end <- Sys.time() end <- Sys.time()
time$rmtl <- as.numeric(difftime(end,start,units="secs")) time$rmtl <- as.numeric(difftime(end,start,units="secs"))
...@@ -901,25 +907,25 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex ...@@ -901,25 +907,25 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
# --- development --- # --- development ---
if(FALSE){ if(FALSE){
# --- MLPUGS --- (binary outcome only) ## --- MLPUGS --- (binary outcome only)
X0f <- as.data.frame(X0) #X0f <- as.data.frame(X0)
y0f <- as.data.frame(y0) #y0f <- as.data.frame(y0)
X1f <- as.data.frame(X1) #X1f <- as.data.frame(X1)
object <- MLPUGS::ecc(x=X0f,y=y0f,.f=randomForest::randomForest) #object <- MLPUGS::ecc(x=X0f,y=y0f,.f=randomForest::randomForest)
pred_ecc <- predict(object,newdata=X1f,n.iters=300,burn.in=100,thin=2, #pred_ecc <- predict(object,newdata=X1f,n.iters=300,burn.in=100,thin=2,
.f = function(rF,newdata){randomForest:::predict.randomForest(rF, newdata, type = "prob")}) # .f = function(rF,newdata){randomForest:::predict.randomForest(rF, newdata, type = "prob")})
pred$ecc[foldid.ext==i,] <- summary(pred_ecc,type="prob") #pred$ecc[foldid.ext==i,] <- summary(pred_ecc,type="prob")
# --- MSGLasso --- (many user inputs) ## --- MSGLasso --- (many user inputs)
MSGLasso::MSGLasso.cv(X=X0,Y=Y0) #MSGLasso::MSGLasso.cv(X=X0,Y=Y0)
# --- PMA --- (not for prediction?) ## --- PMA --- (not for prediction?)
# --- MSP --- (not for hd data?) ## --- MSP --- (not for hd data?)
object <- MBSP::mbsp.tpbn(X=X0,Y=Y0) #object <- MBSP::mbsp.tpbn(X=X0,Y=Y0)
X1 %*% object$B # adjust for intercept #X1 %*% object$B # adjust for intercept
# --- bgsmtr --- (for SNPs only?) ## --- bgsmtr --- (for SNPs only?)
temp <- bgsmtr::bgsmtr(X=t(X0),Y=t(Y0),group=rep(1,times=ncol(X0))) #temp <- bgsmtr::bgsmtr(X=t(X0),Y=t(Y0),group=rep(1,times=ncol(X0)))
# --- MGLM --- (for multinomial data only?) ## --- MGLM --- (for multinomial data only?)
MGLM::MGLMsparsereg.fit() #MGLM::MGLMsparsereg.fit()
MGLM::MGLMtune() #MGLM::MGLMtune()
} }
pred$none[foldid.ext==i,] <- matrix(colMeans(Y0,na.rm=TRUE),nrow=sum(foldid.ext==i),ncol=ncol(Y0),byrow=TRUE) pred$none[foldid.ext==i,] <- matrix(colMeans(Y0,na.rm=TRUE),nrow=sum(foldid.ext==i),ncol=ncol(Y0),byrow=TRUE)
...@@ -950,8 +956,6 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex ...@@ -950,8 +956,6 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
return(loss) return(loss)
} }
plot.matrix <- function (X, margin = 0, labels = TRUE, las = 1, cex = 1, range = NULL, cutoff = 0, digits=2) { plot.matrix <- function (X, margin = 0, labels = TRUE, las = 1, cex = 1, range = NULL, cutoff = 0, digits=2) {
#margin <- 0; labels <- TRUE; las <- 1; cex <- 1; range <- NULL; cutoff <- 0; digits <- 2 #margin <- 0; labels <- TRUE; las <- 1; cex <- 1; range <- NULL; cutoff <- 0; digits <- 2
......
...@@ -24,8 +24,8 @@ ...@@ -24,8 +24,8 @@
#' to open the vignette. #' to open the vignette.
#' #'
#' @references #' @references
#' Armin Rauschenberger and Enrico Glaab (2019). #' Armin Rauschenberger and Enrico Glaab (2020).
#' "joinet: predicting correlated outcomes jointly to improve clinical prognosis". #' "Predicting correlated outcomes from molecular data".
#' \emph{Manuscript in preparation}. #' \emph{Manuscript in preparation}.
#' #'
#' \email{armin.rauschenberger@uni.lu} #' \email{armin.rauschenberger@uni.lu}
......
...@@ -40,7 +40,7 @@ devtools::install_github("rauschenberger/joinet") ...@@ -40,7 +40,7 @@ devtools::install_github("rauschenberger/joinet")
## Reference ## Reference
Armin Rauschenberger and Enrico Glaab (2019). "Predicting correlated outcomes jointly to improve clinical prognosis". *Manuscript in preparation.* Armin Rauschenberger and Enrico Glaab (2020). "Predicting correlated outcomes from molecular data". *Manuscript in preparation.*
[![CRAN version](https://www.r-pkg.org/badges/version/joinet)](https://CRAN.R-project.org/package=joinet) [![CRAN version](https://www.r-pkg.org/badges/version/joinet)](https://CRAN.R-project.org/package=joinet)
[![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/joinet)](https://CRAN.R-project.org/package=joinet) [![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/joinet)](https://CRAN.R-project.org/package=joinet)
......
...@@ -33,9 +33,8 @@ devtools::install_github("rauschenberger/joinet") ...@@ -33,9 +33,8 @@ devtools::install_github("rauschenberger/joinet")
## Reference ## Reference
Armin Rauschenberger and Enrico Glaab (2019). “Predicting correlated Armin Rauschenberger and Enrico Glaab (2020). “Predicting correlated
outcomes jointly to improve clinical prognosis”. *Manuscript in outcomes from molecular data”. *Manuscript in preparation.*
preparation.*
[![CRAN [![CRAN
version](https://www.r-pkg.org/badges/version/joinet)](https://CRAN.R-project.org/package=joinet) version](https://www.r-pkg.org/badges/version/joinet)](https://CRAN.R-project.org/package=joinet)
......
...@@ -71,7 +71,7 @@ ...@@ -71,7 +71,7 @@
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......
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<h1 data-toc-skip>Multivariate Elastic Net Regression</h1>
<small class="dont-index">Source: <a href="https://github.com/rauschenberger/joinet/blob/master/vignettes/article.Rmd"><code>vignettes/article.Rmd</code></a></small>
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<p>Armin Rauschenberger and Enrico Glaab (2020). “Predicting correlated outcomes from molecular data”. <em>Manuscript in preparation.</em></p>
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<dt><a href="article.html">Multivariate Elastic Net Regression</a></dt>
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<dt><a href="joinet.html">Multivariate Elastic Net Regression</a></dt>
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<h1 data-toc-skip>Multivariate Elastic Net Regression</h1>
<small class="dont-index">Source: <a href="https://github.com/rauschenberger/joinet/blob/master/vignettes/joinet.Rmd"><code>vignettes/joinet.Rmd</code></a></small>
<div class="hidden name"><code>joinet.Rmd</code></div>
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<h2 class="hasAnchor">
<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"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span>(<span class="st">"joinet"</span>)</pre></body></html></div>
<p>Or install the latest development version from <a href="https://github.com/rauschenberger/joinet">GitHub</a>:</p>
<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="co">#install.packages("devtools")</span>
<span class="kw pkg">devtools</span><span class="kw ns">::</span><span class="fu"><a href="https://devtools.r-lib.org//reference/remote-reexports.html">install_github</a></span>(<span class="st">"rauschenberger/joinet"</span>)</pre></body></html></div>
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<a href=