competing models

448ee92c · Armin Rauschenberger · ffa77bbb · 448ee92c · 448ee92c · 448ee92c
Commit 448ee92c authored Oct 02, 2020 by Armin Rauschenberger
27 changed files
--- a/DESCRIPTION
+++ b/DESCRIPTION
 Package: joinet
-Version: 0.0.4
+Version: 0.0.5
 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.
 Depends: R (>= 3.0.0)
@@ -11,6 +11,6 @@ VignetteBuilder: knitr
 License: GPL-3
 LazyData: true
 Language: en-GB
-RoxygenNote: 7.1.0
+RoxygenNote: 7.1.1
 URL: https://github.com/rauschenberger/joinet
 BugReports: https://github.com/rauschenberger/joinet/issues
--- a/NEWS.md
+++ b/NEWS.md

+## joinet 0.0.5 (2020-10-02)
+
+* updated documentation
+
 ## joinet 0.0.4 (2020-05-06)

 * added competing models

--- a/R/functions.R
+++ b/R/functions.R
@@ -53,9 +53,8 @@
 #' further arguments passed to \code{\link[glmnet]{glmnet}}
 #' 
 #' @references 
-#' Armin Rauschenberger, Enrico Glaab (2019)
-#' "joinet: predicting correlated outcomes jointly
-#' to improve clinical prognosis"
+#' Armin Rauschenberger, Enrico Glaab (2020)
+#' "Predicting correlated outcomes from molecular data"
 #' \emph{Manuscript in preparation}.
 #' 
 #' @details
@@ -472,6 +471,10 @@ print.joinet <- function(x,...){
 #' @param cvpred
 #' return cross-validated predicitions: logical
 #' 
+#' @param times
+#' measure computation time\strong{:}
+#' logical
+#' 
 #' @param ...
 #' further arguments passed to \code{\link[glmnet]{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
      # 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$mars[foldid.ext==i,] <- stats::predict(object=object,newdata=X1,type="response") # trial
      end <- Sys.time()
      time$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
      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)
      # 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$mcen[foldid.ext==i,] <- as.matrix(temp)
      # single cluster (ky=1) due to setting and error
      end <- Sys.time()
@@ -835,7 +840,8 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
      #--- manual tuning end ----
      #--------------------------
      MTL <- RMTL::MTL(X=X0l,Y=y0l,type=type,Lam1=Lam1,Lam2=Lam2)
-      temp <- RMTL:::predict.MTL(object=MTL,newX=X1l)
+      #temp <- RMTL:::predict.MTL(object=MTL,newX=X1l) # original
+      temp <- stats::predict(object=MTL,newX=X1l)
      pred$rmtl[foldid.ext==i,] <- do.call(what="cbind",args=temp)
      end <- Sys.time()
      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
    # --- development ---
    
    if(FALSE){
-      # --- MLPUGS --- (binary outcome only)
-      X0f <- as.data.frame(X0)
-      y0f <- as.data.frame(y0)
-      X1f <- as.data.frame(X1)
-      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")})
-      pred$ecc[foldid.ext==i,] <- summary(pred_ecc,type="prob")
-      # --- MSGLasso --- (many user inputs)
-      MSGLasso::MSGLasso.cv(X=X0,Y=Y0)
-      # --- PMA --- (not for prediction?)
-      # --- MSP --- (not for hd data?)
-      object <- MBSP::mbsp.tpbn(X=X0,Y=Y0)
-      X1 %*% object$B # adjust for intercept
-      # --- bgsmtr --- (for SNPs only?)
-      temp <- bgsmtr::bgsmtr(X=t(X0),Y=t(Y0),group=rep(1,times=ncol(X0)))
-      # --- MGLM --- (for multinomial data only?)
-      MGLM::MGLMsparsereg.fit()
-      MGLM::MGLMtune()
+      ## --- MLPUGS --- (binary outcome only)
+      #X0f <- as.data.frame(X0)
+      #y0f <- as.data.frame(y0)
+      #X1f <- as.data.frame(X1)
+      #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")})
+      #pred$ecc[foldid.ext==i,] <- summary(pred_ecc,type="prob")
+      ## --- MSGLasso --- (many user inputs)
+      #MSGLasso::MSGLasso.cv(X=X0,Y=Y0)
+      ## --- PMA --- (not for prediction?)
+      ## --- MSP --- (not for hd data?)
+      #object <- MBSP::mbsp.tpbn(X=X0,Y=Y0)
+      #X1 %*% object$B # adjust for intercept
+      ## --- bgsmtr --- (for SNPs only?)
+      #temp <- bgsmtr::bgsmtr(X=t(X0),Y=t(Y0),group=rep(1,times=ncol(X0)))
+      ## --- MGLM --- (for multinomial data only?)
+      #MGLM::MGLMsparsereg.fit()
+      #MGLM::MGLMtune()
    }
    
    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
  return(loss)
 }

-
-
 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
  

--- a/R/pkgname.R
+++ b/R/pkgname.R
@@ -24,8 +24,8 @@
 #' to open the vignette.
 #' 
 #' @references
-#' Armin Rauschenberger and Enrico Glaab (2019).
-#' "joinet: predicting correlated outcomes jointly to improve clinical prognosis".
+#' Armin Rauschenberger and Enrico Glaab (2020).
+#' "Predicting correlated outcomes from molecular data".
 #' \emph{Manuscript in preparation}.
 #' 
 #' \email{armin.rauschenberger@uni.lu}

--- a/README.Rmd
+++ b/README.Rmd
@@ -40,7 +40,7 @@ devtools::install_github("rauschenberger/joinet")

 ## 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 RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/joinet)](https://CRAN.R-project.org/package=joinet)

--- a/README.md
+++ b/README.md
@@ -33,9 +33,8 @@ devtools::install_github("rauschenberger/joinet")

 ## 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)

--- a/docs/404.html
+++ b/docs/404.html
@@ -71,7 +71,7 @@
      </button>
      <span class="navbar-brand">
        <a class="navbar-link" href="index.html">joinet</a>
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.4</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.5</span>
      </span>
    </div>


--- a/docs/articles/article.html
+++ b/docs/articles/article.html
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+    <div class="page-header toc-ignore">
+      <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>
+      <div class="hidden name"><code>article.Rmd</code></div>
+
+    </div>
+
+    
+    
+<p>The <code>joinet</code> manuscript is in preparation. Click <a href="https://CRAN.R-project.org/package=joinet">here</a> for the R package.</p>
+<div id="reference" class="section level2">
+<h2 class="hasAnchor">
+<a href="#reference" class="anchor"></a>Reference</h2>
+<p>Armin Rauschenberger and Enrico Glaab (2020). “Predicting correlated outcomes from molecular data”. <em>Manuscript in preparation.</em></p>
+</div>
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+    <div class="section ">
+      <h3>All vignettes</h3>
+      <p class="section-desc"></p>
+
+      <dl>
+        <dt><a href="article.html">Multivariate Elastic Net Regression</a></dt>
+        <dd></dt>
+        <dt><a href="joinet.html">Multivariate Elastic Net Regression</a></dt>
+        <dd></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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+    </div>
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+    
+<div id="installation" class="section level2">
+<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>
+</div>
+<div id="initialisation" class="section level2">
+<h2 class="hasAnchor">
+<a href="#initialisation" class="anchor"></a>Initialisation</h2>
+<p>Load and attach the package:</p>
+<div class="sourceCode" id="cb3"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">joinet</span>)</pre></body></html></div>
+<p>And access the <a href="https://rauschenberger.github.io/joinet/">documentation</a>:</p>
+<div class="sourceCode" id="cb4"><html><body><pre class="r">?<span class="no">joinet</span>
+<span class="fu"><a href="https://rdrr.io/r/utils/help.html">help</a></span>(<span class="no">joinet</span>)
+<span class="fu"><a href="https://rdrr.io/r/utils/browseVignettes.html">browseVignettes</a></span>(<span class="st">"joinet"</span>)</pre></body></html></div>
+</div>
+<div id="simulation" class="section level2">
+<h2 class="hasAnchor">
+<a href="#simulation" class="anchor"></a>Simulation</h2>
+<p>For <code>n</code> samples, we simulate <code>p</code> inputs (features, covariates) and <code>q</code> outputs (outcomes, responses). We assume high-dimensional inputs (<code>p</code> <span class="math inline">\(\gg\)</span> <code>n</code>) and low-dimensional outputs (<code>q</code> <span class="math inline">\(\ll\)</span> <code>n</code>).</p>
+<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="no">n</span> <span class="kw">&lt;-</span> <span class="fl">100</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">500</span></pre></body></html></div>
+<p>We simulate the <code>p</code> inputs from a multivariate normal distribution. For the mean, we use the <code>p</code>-dimensional vector <code>mu</code>, where all elements equal zero. For the covariance, we use the <code>p</code> <span class="math inline">\(\times\)</span> <code>p</code> matrix <code>Sigma</code>, where the entry in row <span class="math inline">\(i\)</span> and column <span class="math inline">\(j\)</span> equals <code>rho</code><span class="math inline">\(^{|i-j|}\)</span>. The parameter <code>rho</code> determines the strength of the correlation among the inputs, with small <code>rho</code> leading weak correlations, and large <code>rho</code> leading to strong correlations (0 &lt; <code>rho</code> &lt; 1). The input matrix <code>X</code> has <code>n</code> rows and <code>p</code> columns.</p>
+<div class="sourceCode" id="cb6"><html><body><pre class="r"><span class="no">mu</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html">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">rho</span> <span class="kw">&lt;-</span> <span class="fl">0.90</span>
+<span class="no">Sigma</span> <span class="kw">&lt;-</span> <span class="no">rho</span>^<span class="fu"><a href="https://rdrr.io/r/base/MathFun.html">abs</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/col.html">col</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/diag.html">diag</a></span>(<span class="no">p</span>))-<span class="fu"><a href="https://rdrr.io/r/base/row.html">row</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/diag.html">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://rdrr.io/pkg/MASS/man/mvrnorm.html">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>)</pre></body></html></div>
+<p>We simulate the input-output effects from independent Bernoulli distributions. The parameter <code>pi</code> determines the number of effects, with small <code>pi</code> leading to few effects, and large <code>pi</code> leading to many effects (0 &lt; <code>pi</code> &lt; 1). The scalar <code>alpha</code> represents the intercept, and the <code>p</code>-dimensional vector <code>beta</code> represents the slopes.</p>
+<div class="sourceCode" id="cb7"><html><body><pre class="r"><span class="no">pi</span> <span class="kw">&lt;-</span> <span class="fl">0.01</span>
+<span class="no">alpha</span> <span class="kw">&lt;-</span> <span class="fl">0</span>
+<span class="no">beta</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Binomial.html">rbinom</a></span>(<span class="kw">n</span><span class="kw">=</span><span class="no">p</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="no">pi</span>)</pre></body></html></div>
+<p>From the intercept <code>alpha</code>, the slopes <code>beta</code> and the inputs <code>X</code>, we calculate the linear predictor, the <code>n</code>-dimensional vector <code>eta</code>. Rescale the linear predictor to make the effects weaker or stronger. Set the argument <code>family</code> to <code>"gaussian"</code>, <code>"binomial"</code>, or <code>"poisson"</code> to define the distribution. The <code>n</code> times <code>p</code> matrix <code>Y</code> represents the outputs. We assume the outcomes are <em>positively</em> correlated.</p>
+<div class="sourceCode" id="cb8"><html><body><pre class="r"><span class="no">eta</span> <span class="kw">&lt;-</span> <span class="no">alpha</span> + <span class="no">X</span> <span class="kw">%*%</span> <span class="no">beta</span>
+<span class="no">eta</span> <span class="kw">&lt;-</span> <span class="fl">1.5</span>*<span class="fu"><a href="https://rdrr.io/r/base/scale.html">scale</a></span>(<span class="no">eta</span>)
+<span class="no">family</span> <span class="kw">&lt;-</span> <span class="st">"gaussian"</span>
+
+<span class="kw">if</span>(<span class="no">family</span><span class="kw">==</span><span class="st">"gaussian"</span>){
+  <span class="no">mean</span> <span class="kw">&lt;-</span> <span class="no">eta</span>
+  <span class="no">Y</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">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://rdrr.io/r/stats/Normal.html">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="no">mean</span>))
+}
+
+<span class="kw">if</span>(<span class="no">family</span><span class="kw">==</span><span class="st">"binomial"</span>){
+  <span class="no">prob</span> <span class="kw">&lt;-</span> <span class="fl">1</span>/(<span class="fl">1</span>+<span class="fu"><a href="https://rdrr.io/r/base/Log.html">exp</a></span>(-<span class="no">eta</span>))
+  <span class="no">Y</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">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://rdrr.io/r/stats/Binomial.html">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="no">prob</span>))
+}