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Commit ffa77bbb 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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......@@ -530,8 +530,10 @@ print.joinet <- function(x,...){
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=FALSE,mice=FALSE,cvpred=FALSE,times=FALSE,...){
if(FALSE){
fold <- foldid.ext
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
foldid.ext <- fold; nfolds.ext <- 1
#nfolds.ext <- 1; nfolds.int <- 10; foldid.int <- NULL; compare <- TRUE
}
if(length(compare)==1 && compare==TRUE){
......@@ -659,19 +661,28 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
cat("mars"," ")
start <- Sys.time()
if(all(family=="gaussian")){
object <- earth::earth(x=X0,y=y0) # add:pmethod="cv"nfold=nfolds.int
object <- earth::earth(x=X0,y=y0)
# equivalent: object <- mda::mars(x=X0,y=y0)
} else if(all(family=="binomial")){
object <- earth::earth(x=X0,y=y0,glm=list(family=stats::binomial)) # add pmethod="cv",nfold=nfolds.int
object <- earth::earth(x=X0,y=y0,glm=list(family=stats::binomial))
} else {
stop("MARS requires either \"gaussian\" or \"binomial\" family.",call.=FALSE)
}
# pmethod="cv" not available for multivariate outputs
## nk = min(200, max(20, 2 * ncol(x))) + 1
## nprune <- seq(from=2,to=nk,length.out=10)
### start trial ###
if(FALSE){
#nk <- min(200, max(20, 2 * ncol(X0))) + 1
#nprune <- round(seq(from=2,to=nk,length.out=10))
#object <- list()
#for(j in seq_along(nprune)){
# object[[j]] <- earth::earth(x=X0,y=y0,nprune=nprune[j],pmethod="cv",nfold=nfolds.int)
#}
#sapply(object,function(x) x$gcv)
## i.e. run earth/mars with tryCatch for each nprune
## and select run with best cvm (here gcv)
# tune nprune (use default nk)!
}
pred$mars[foldid.ext==i,] <- earth:::predict.earth(object=object,newdata=X1,type="response")
end <- Sys.time()
......@@ -715,13 +726,17 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
}
mean <- colMeans(y0)
y0s <- y0-matrix(data=mean,nrow=nrow(X0),ncol=ncol(y0),byrow=TRUE)
#lamL1.v <- exp(seq(from=log(10),to=log(20),length.out=11)) # original
lamL1.v <- seq(from=0,to=20,length.out=11) # trial
lamL2.v <- seq(from=0,to=5,length.out=11)
lamL1.v <- lamL2.v <- exp(seq(from=0,to=5,length.out=11))
cv <- remMap::remMap.CV(X=X0,Y=y0s,lamL1.v=lamL1.v,lamL2.v=lamL2.v,fold=nfolds.int)
#graphics::plot(x=lamL1.v,y=log(as.numeric(cv$ols.cv[,3])))
index <- which(cv$ols.cv==min(cv$ols.cv),arr.ind=TRUE)[1,]
object <- remMap::remMap(X.m=X0,Y.m=y0s,lamL1=lamL1.v[index[1]],lamL2=lamL2.v[index[2]])
pick <- which.min(as.vector(cv$ols.cv))
lamL1 <- cv$l.index[1,pick]
lamL2 <- cv$l.index[2,pick]
# index <- which(cv$ols.cv==min(cv$ols.cv),arr.ind=TRUE)[1,]
# rev(lamL1.v)[index[1]]
# rev(lamL2.v)[index[2]]
##cat("lam1:",lamL1,", lam2:",lamL2)
object <- remMap::remMap(X.m=X0,Y.m=y0s,lamL1=lamL1,lamL2=lamL2)
pred$map[foldid.ext==i,] <- matrix(data=mean,nrow=nrow(X1),ncol=ncol(y0),byrow=TRUE) + X1 %*% object$phi
end <- Sys.time()
time$map <- as.numeric(difftime(end,start,units="secs"))
......@@ -881,7 +896,7 @@ cv.joinet <- function(Y,X,family="gaussian",nfolds.ext=5,nfolds.int=10,foldid.ex
# now using cross-validation residual stacking (CVRS)
}
if(length(compare)>1){cat("\n")} # was !is.null(compare)
if(is.character(compare)){cat("\n")}
# --- development ---
......
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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>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). “joinet: predicting correlated outcomes jointly to improve clinical prognosis”. <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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docs/pkgdown.yml
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......@@ -4,5 +4,5 @@ pkgdown_sha: ~
articles:
article: article.html
joinet: joinet.html
last_built: 2020-07-02T16:34Z
last_built: 2020-07-03T15:21Z
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<meta property="og:description" content="Extracts pooled coefficients.
(The meta learners linearly combines
the coefficients from the base learners.)" />
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<h1>Extract Coefficients</h1>
<small class="dont-index">Source: <a href='https://github.com/rauschenberger/joinet/blob/master/R/functions.R'><code>R/functions.R</code></a></small>
<div class="hidden name"><code>coef.joinet.Rd</code></div>
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<div class="ref-description">
<p>Extracts pooled coefficients.
(The meta learners linearly combines
the coefficients from the base learners.)</p>
</div>
<pre class="usage"><span class='co'># S3 method for joinet</span>
<span class='fu'><a href='https://rdrr.io/r/stats/coef.html'>coef</a></span>(<span class='no'>object</span>, <span class='no'>...</span>)</pre>
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
<table class="ref-arguments">
<colgroup><col class="name" /><col class="desc" /></colgroup>
<tr>
<th>object</th>
<td><p><a href='joinet.html'>joinet</a> object</p></td>
</tr>
<tr>
<th>...</th>
<td><p>further arguments (not applicable)</p></td>
</tr>
</table>
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>This function returns the pooled coefficients.
The slot <code>alpha</code> contains the intercepts
in a vector of length \(q\),
and the slot <code>beta</code> contains the slopes
in a matrix with \(p\) rows (inputs) and \(q\) columns.</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='no'>n</span> <span class='kw'>&lt;-</span> <span class='fl'>50</span>; <span class='no'>p</span> <span class='kw'>&lt;-</span> <span class='fl'>100</span>; <span class='no'>q</span> <span class='kw'>&lt;-</span> <span class='fl'>3</span>
<span class='no'>X</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/stats/Normal.html'>rnorm</a></span>(<span class='no'>n</span>*<span class='no'>p</span>),<span class='kw'>nrow</span><span class='kw'>=</span><span class='no'>n</span>,<span class='kw'>ncol</span><span class='kw'>=</span><span class='no'>p</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='fu'><a href='https://rdrr.io/r/base/colSums.html'>rowSums</a></span>(<span class='no'>X</span>[,<span class='fl'>1</span>:<span class='fl'>5</span>])))
<span class='no'>object</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='joinet.html'>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='no'>coef</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/stats/coef.html'>coef</a></span>(<span class='no'>object</span>)</div></pre>
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