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Commit 1740863b authored by Armin Rauschenberger's avatar Armin Rauschenberger
Browse files

automation

parent 05b38bf4
......@@ -6,7 +6,7 @@ Depends: R (>= 3.0.0)
Imports: glmnet, palasso, cornet
Suggests: knitr, testthat, MASS
Enhances: spls, SiER, MRCE
Authors@R: person("Armin","Rauschenberger",email="a.rauschenberger@vumc.nl",role=c("aut","cre"))
Authors@R: person("Armin","Rauschenberger",email="armin.rauschenberger@uni.lu",role=c("aut","cre"))
VignetteBuilder: knitr
License: GPL-3
LazyData: true
......
#.loss <- get(".loss",envir=asNamespace("palasso"))
#.folds <- get(".folds",envir=asNamespace("palasso"))
#.check <- get(".check",envir=asNamespace("cornet"))
# import unexported functions:
# FUNCTION <- get("FUNCTION",envir=asNamespace("PACKAGE"))
#--- Main function -------------------------------------------------------------
#' @export
#' @aliases joinet-package
#' @title
#' Multivariate Elastic Net Regression
#'
......
#' @name joinet-package
#' @keywords documentation
#' @docType package
#'
#' @title
#' Multivariate Elastic Net Regression
#'
#' @description
#' The R package \code{joinet} implements multivariate
#' ridge and lasso regression using stacked generalisation.
#' This multivariate regression typically outperforms
#' univariate regression at predicting correlated outcomes.
#' It provides predictive and interpretable models
#' in high-dimensional settings.
#'
#' @details
#' Use function \code{\link{joinet}} for model fitting.
#' Type \code{library(joinet)} and then \code{?joinet} or
#' \code{help("joinet)"} to open its help file.
#'
#' See the vignette for further examples.
#' Type \code{vignette("joinet")} or \code{browseVignettes("joinet")}
#' to open the vignette.
#'
#' @references
#' Armin Rauschenberger and Enrico Glaab (2019).
#' "joinet: predicting correlated outcomes jointly to improve clinical prognosis".
#' \emph{Manuscript in preparation}.
#'
#' \email{armin.rauschenberger@uni.lu}
#'
#' @examples
#' #--- data simulation ---
#' n <- 50; p <- 100; q <- 3
#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
#' Y <- replicate(n=q,expr=rnorm(n=n,mean=rowSums(X[,1:5])))
#' # n samples, p inputs, q outputs
#'
#' #--- model fitting ---
#' object <- joinet(Y=Y,X=X)
#' # slot "base": univariate
#' # slot "meta": multivariate
#'
#' #--- make predictions ---
#' y_hat <- predict(object,newx=X)
#' # n x q matrix "base": univariate
#' # n x q matrix "meta": multivariate
#'
#' #--- extract coefficients ---
#' coef <- coef(object)
#' # effects of inputs on outputs
#' # q vector "alpha": intercepts
#' # p x q matrix "beta": slopes
#'
#' #--- model comparison ---
#' loss <- cv.joinet(Y=Y,X=X)
#' # cross-validated loss
#' # row "base": univariate
#' # row "meta": multivariate
#'
NULL
......@@ -17,7 +17,7 @@ navbar:
- text: "functions"
href: reference/index.html
- text: "vignette"
href: articles/vignette.html
href: articles/joinet.html
- text: "article"
href: articles/article.html
- text: "news"
......@@ -29,12 +29,11 @@ navbar:
href: https://cran.r-project.org/package=joinet
reference:
- title: "User functions"
- title: "Functions"
contents:
- joinet
- predict.joinet
- coef.joinet
- weights.joinet
- title: "Internal functions"
contents:
- cv.joinet
- joinet-package
# Notes
- Early update because of manuscript submission (examples, vignettes).
- The maintainer email will change to armin.rauschenberger@uni.lu.
\ No newline at end of file
Please accept ::: calls to "palasso" and "cornet",
as maintainer email changes for all packages.
\ No newline at end of file
......@@ -40,7 +40,7 @@
<a href="../reference/index.html">functions</a>
</li>
<li>
<a href="../articles/vignette.html">vignette</a>
<a href="../articles/joinet.html">vignette</a>
</li>
<li>
<a href="../articles/article.html">article</a>
......
......@@ -70,7 +70,7 @@
<a href="../reference/index.html">functions</a>
</li>
<li>
<a href="../articles/vignette.html">vignette</a>
<a href="../articles/joinet.html">vignette</a>
</li>
<li>
<a href="../articles/article.html">article</a>
......@@ -114,7 +114,7 @@
<ul>
<li><a href="article.html">Multivariate Elastic Net Regression</a></li>
<li><a href="vignette.html">Multivariate Elastic Net Regression</a></li>
<li><a href="joinet.html">Multivariate Elastic Net Regression</a></li>
</ul>
</div>
</div>
......
......@@ -40,7 +40,7 @@
<a href="../reference/index.html">functions</a>
</li>
<li>
<a href="../articles/vignette.html">vignette</a>
<a href="../articles/joinet.html">vignette</a>
</li>
<li>
<a href="../articles/article.html">article</a>
......@@ -77,8 +77,8 @@
<h1>Multivariate Elastic Net Regression</h1>
<small class="dont-index">Source: <a href="https://github.com/rauschenberger/joinet/blob/master/vignettes/vignette.Rmd"><code>vignettes/vignette.Rmd</code></a></small>
<div class="hidden name"><code>vignette.Rmd</code></div>
<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>
</div>
......
......@@ -70,7 +70,7 @@
<a href="reference/index.html">functions</a>
</li>
<li>
<a href="articles/vignette.html">vignette</a>
<a href="articles/joinet.html">vignette</a>
</li>
<li>
<a href="articles/article.html">article</a>
......
......@@ -40,7 +40,7 @@
<a href="reference/index.html">functions</a>
</li>
<li>
<a href="articles/vignette.html">vignette</a>
<a href="articles/joinet.html">vignette</a>
</li>
<li>
<a href="articles/article.html">article</a>
......
......@@ -70,7 +70,7 @@
<a href="../reference/index.html">functions</a>
</li>
<li>
<a href="../articles/vignette.html">vignette</a>
<a href="../articles/joinet.html">vignette</a>
</li>
<li>
<a href="../articles/article.html">article</a>
......
......@@ -3,5 +3,5 @@ pkgdown: 1.3.0
pkgdown_sha: ~
articles:
article: article.html
vignette: vignette.html
joinet: joinet.html
......@@ -75,7 +75,7 @@ the coefficients from the base learners.)" />
<a href="../reference/index.html">functions</a>
</li>
<li>
<a href="../articles/vignette.html">vignette</a>
<a href="../articles/joinet.html">vignette</a>
</li>
<li>
<a href="../articles/article.html">article</a>
......
......@@ -73,7 +73,7 @@
<a href="../reference/index.html">functions</a>
</li>
<li>
<a href="../articles/vignette.html">vignette</a>
<a href="../articles/joinet.html">vignette</a>
</li>
<li>
<a href="../articles/article.html">article</a>
......
......@@ -70,7 +70,7 @@
<a href="../reference/index.html">functions</a>
</li>
<li>
<a href="../articles/vignette.html">vignette</a>
<a href="../articles/joinet.html">vignette</a>
</li>
<li>
<a href="../articles/article.html">article</a>
......@@ -119,7 +119,7 @@
<tbody>
<tr>
<th colspan="2">
<h2 id="section-user-functions" class="hasAnchor"><a href="#section-user-functions" class="anchor"></a>User functions</h2>
<h2 id="section-functions" class="hasAnchor"><a href="#section-functions" class="anchor"></a>Functions</h2>
<p class="section-desc"></p>
</th>
</tr>
......@@ -147,20 +147,18 @@
<p><code><a href="weights.joinet.html">weights(<i>&lt;joinet&gt;</i>)</a></code> </p>
</td>
<td><p>Extract Weights</p></td>
</tr>
</tbody><tbody>
<tr>
<th colspan="2">
<h2 id="section-internal-functions" class="hasAnchor"><a href="#section-internal-functions" class="anchor"></a>Internal functions</h2>
<p class="section-desc"></p>
</th>
</tr>
<tr>
</tr><tr>
<td>
<p><code><a href="cv.joinet.html">cv.joinet()</a></code> </p>
</td>
<td><p>Model comparison</p></td>
</tr><tr>
<td>
<p><code><a href="joinet-package.html">joinet-package</a></code> </p>
</td>
<td><p>Multivariate Elastic Net Regression</p></td>
</tr>
</tbody>
</table>
......@@ -169,8 +167,7 @@
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
<ul class="nav nav-pills nav-stacked">
<li><a href="#section-user-functions">User functions</a></li>
<li><a href="#section-internal-functions">Internal functions</a></li>
<li><a href="#section-functions">Functions</a></li>
</ul>
</div>
</div>
......
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This multivariate regression typically outperforms
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It provides predictive and interpretable models
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.2</span>
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<div class="page-header">
<h1>Multivariate Elastic Net Regression</h1>
<small class="dont-index">Source: <a href='https://github.com/rauschenberger/joinet/blob/master/R/pkgname.R'><code>R/pkgname.R</code></a></small>
<div class="hidden name"><code>joinet-package.Rd</code></div>
</div>
<div class="ref-description">
<p>The R package <code>joinet</code> implements multivariate
ridge and lasso regression using stacked generalisation.
This multivariate regression typically outperforms
univariate regression at predicting correlated outcomes.
It provides predictive and interpretable models
in high-dimensional settings.</p>
</div>
<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
<p>Use function <code><a href='joinet.html'>joinet</a></code> for model fitting.
Type <code><a href='https://www.rdocumentation.org/packages/base/topics/library'>library(joinet)</a></code> and then <code><a href='joinet.html'>?joinet</a></code> or
<code>help("joinet)"</code> to open its help file.</p>
<p>See the vignette for further examples.
Type <code><a href='../articles/joinet.html'>vignette("joinet")</a></code> or <code><a href='https://www.rdocumentation.org/packages/utils/topics/browseVignettes'>browseVignettes("joinet")</a></code>
to open the vignette.</p>
<h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>
<p>Armin Rauschenberger and Enrico Glaab (2019).
"joinet: predicting correlated outcomes jointly to improve clinical prognosis".
<em>Manuscript in preparation</em>.</p>
<p><a href='mailto:armin.rauschenberger@uni.lu'>armin.rauschenberger@uni.lu</a></p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='co'>#--- data simulation ---</span>
<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://www.rdocumentation.org/packages/base/topics/matrix'>matrix</a></span>(<span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/Normal'>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://www.rdocumentation.org/packages/base/topics/lapply'>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://www.rdocumentation.org/packages/stats/topics/Normal'>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://www.rdocumentation.org/packages/base/topics/colSums'>rowSums</a></span>(<span class='no'>X</span>[,<span class='fl'>1</span>:<span class='fl'>5</span>])))
<span class='co'># n samples, p inputs, q outputs</span>
<span class='co'>#--- model fitting ---</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='co'># slot "base": univariate</span>
<span class='co'># slot "meta": multivariate</span>
<span class='co'>#--- make predictions ---</span>
<span class='no'>y_hat</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/predict'>predict</a></span>(<span class='no'>object</span>,<span class='kw'>newx</span><span class='kw'>=</span><span class='no'>X</span>)
<span class='co'># n x q matrix "base": univariate</span>
<span class='co'># n x q matrix "meta": multivariate </span>
<span class='co'>#--- extract coefficients ---</span>
<span class='no'>coef</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/coef'>coef</a></span>(<span class='no'>object</span>)
<span class='co'># effects of inputs on outputs</span>
<span class='co'># q vector "alpha": intercepts</span>
<span class='co'># p x q matrix "beta": slopes</span>
<span class='co'>#--- model comparison ---</span>
<span class='no'>loss</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='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='co'># cross-validated loss</span>
<span class='co'># row "base": univariate</span>
<span class='co'># row "meta": multivariate</span></div></pre>
</div>
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<h2>Contents</h2>
<ul class="nav nav-pills nav-stacked">
<li><a href="#details">Details</a></li>
<li><a href="#references">References</a></li>
<li><a href="#examples">Examples</a></li>
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......@@ -73,7 +73,7 @@
<a href="../reference/index.html">functions</a>
</li>
<li>
<a href="../articles/vignette.html">vignette</a>
<a href="../articles/joinet.html">vignette</a>
</li>
<li>
<a href="../articles/article.html">article</a>
......
......@@ -73,7 +73,7 @@
<a href="../reference/index.html">functions</a>
</li>
<li>
<a href="../articles/vignette.html">vignette</a>
<a href="../articles/joinet.html">vignette</a>
</li>
<li>
<a href="../articles/article.html">article</a>
......@@ -158,110 +158,110 @@ with \(n\) rows (samples) and \(q\) columns (variables).</p>
<span class='no'>Y</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/lapply'>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://www.rdocumentation.org/packages/stats/topics/Normal'>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://www.rdocumentation.org/packages/base/topics/colSums'>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='fu'><a href='https://www.rdocumentation.org/packages/stats/topics/predict'>predict</a></span>(<span class='no'>object</span>,<span class='kw'>newx</span><span class='kw'>=</span><span class='no'>X</span>)</div><div class='output co'>#&gt; $base
#&gt; [,1] [,2] [,3]
#&gt; [1,] -0.6800725 -1.21872520 -0.310657961
#&gt; [2,] 2.5231822 2.01759432 2.319092565
#&gt; [3,] 3.0147246 2.95961835 3.071353923
#&gt; [4,] -2.0321731 -1.16697865 -1.131585864
#&gt; [5,] 1.1989575 1.94542159 1.205214655
#&gt; [6,] -5.2181676 -4.73651108 -5.106512539
#&gt; [7,] 2.6630929 2.28298777 1.837353182
#&gt; [8,] 4.1517740 3.68154548 2.859426063
#&gt; [9,] 2.2255938 2.62966728 2.209039320
#&gt; [10,] -0.1976240 -0.26331544 -0.084736597
#&gt; [11,] -2.2848625 -2.88205673 -3.371158126
#&gt; [12,] 0.1395562 0.12678281 1.952331938
#&gt; [13,] 0.5319628 1.08030034 0.877379283
#&gt; [14,] -3.2092512 -3.20808021 -5.574818243
#&gt; [15,] 2.1373104 1.54594741 0.792750994
#&gt; [16,] 1.3677467 0.24229789 1.303661371
#&gt; [17,] 2.4203610 1.97149218 2.164521950
#&gt; [18,] -1.6439085 -1.13752492 -0.148270982
#&gt; [19,] -2.6971069 -3.21077918 -3.448781770
#&gt; [20,] 0.4847756 1.32920669 1.045263833
#&gt; [21,] 1.0580634 1.41141884 0.487897356
#&gt; [22,] -0.1159397 0.33252059 1.764586212
#&gt; [23,] -2.4576322 -2.61545228 -1.960495924
#&gt; [24,] -2.8963417 -2.86185997 -3.306570648
#&gt; [25,] 1.0818188 1.14369892 2.186762596
#&gt; [26,] -3.9271814 -3.84456309 -5.731322146
#&gt; [27,] -2.3652352 -2.86340632 -3.227402032
#&gt; [28,] -2.1800217 -1.48891855 -2.572985868
#&gt; [29,] 0.7460273 -0.27982198 -0.506445835
#&gt; [30,] -0.8429611 0.13194537 -0.737426505
#&gt; [31,] -0.7316201 -1.51707620 -1.153890045
#&gt; [32,] -2.5021179 -1.84304055 -3.478808874
#&gt; [33,] 0.8250113 -0.21440315 0.524465823
#&gt; [34,] 3.0831834 2.23039182 3.727105270
#&gt; [35,] 0.4993630 -1.34134635 -1.164539640
#&gt; [36,] 1.6239756 0.78835676 0.528243360
#&gt; [37,] 2.1683663 0.91851657 1.521169026
#&gt; [38,] -0.9397605 0.08426956 -0.751842377
#&gt; [39,] -0.2803268 -1.20184123 -0.560565492
#&gt; [40,] 1.9729289 3.26803044 2.090648484
#&gt; [41,] 0.7398532 -0.83577871 1.020203057
#&gt; [42,] -1.7847439 -2.02797038 -3.409427348
#&gt; [43,] 0.9128885 1.60609986 2.616774138
#&gt; [44,] 1.1536491 2.16601146 -0.015506128
#&gt; [45,] 0.6315267 0.22276528 0.985103241
#&gt; [46,] 5.4299519 5.38132076 6.438669837
#&gt; [47,] 1.3572642 -0.09607223 -1.249216010
#&gt; [48,] 0.3873464 0.41138065 -0.001234302
#&gt; [49,] 2.9278013 2.67791507 3.629988512
#&gt; [50,] -2.4898856 -2.53479214 -3.853167277
#&gt; [,1] [,2] [,3]
#&gt; [1,] -2.6382210 -2.9659453 -2.306391444
#&gt; [2,] 2.6552680 1.4525574 2.505892331
#&gt; [3,] 1.1295699 1.4181935 1.024506708
#&gt; [4,] 0.2566896 -1.1868062 0.770974904
#&gt; [5,] 0.9235359 2.1951153 1.435281993
#&gt; [6,] 0.7376982 1.7955112 1.613380715
#&gt; [7,] -0.3904051 -0.9624476 -1.305940614
#&gt; [8,] -4.8823206 -5.1329853 -5.632617590
#&gt; [9,] -2.8826967 -4.2866886 -3.319082823
#&gt; [10,] 2.4610522 1.7177360 2.029169444
#&gt; [11,] 0.9047337 1.7704820 0.138986055
#&gt; [12,] -3.4383168 -3.4492442 -4.077404028
#&gt; [13,] 0.3573390 1.0892937 0.751307437
#&gt; [14,] 2.6856775 4.1461204 4.109918838
#&gt; [15,] -2.5859515 -3.0620775 -3.039261367
#&gt; [16,] 1.7188957 1.1177316 2.666496452
#&gt; [17,] -2.8405784 -4.3156935 -3.265928842
#&gt; [18,] 2.5205082 3.2674372 4.021626346
#&gt; [19,] 2.4668067 2.2547918 1.521037026
#&gt; [20,] -3.2163874 -5.1967661 -3.914674437
#&gt; [21,] -1.8090909 -2.4841106 -2.800940136
#&gt; [22,] 0.1219993 -0.4755235 -1.346081912
#&gt; [23,] 0.5620446 2.9490435 0.834109773
#&gt; [24,] 0.7231336 -0.8008719 0.006685054
#&gt; [25,] 0.3882856 -0.4834631 -0.389710153
#&gt; [26,] 0.8986010 0.3555128 0.582610989
#&gt; [27,] 0.9810074 0.5822080 -0.083507593
#&gt; [28,] 1.3495727 0.2573044 0.950629861
#&gt; [29,] 1.5941948 1.6883281 2.610167833
#&gt; [30,] 0.5201647 -0.4452296 -1.053437483
#&gt; [31,] 2.5336520 3.9861323 3.091867020
#&gt; [32,] 1.2177945 0.4127256 1.337044870
#&gt; [33,] 2.3160903 2.8285159 2.868384803
#&gt; [34,] -0.9393410 -0.3716184 0.146997363
#&gt; [35,] 2.0302346 2.9987535 2.602339175
#&gt; [36,] 2.5560019 4.2112428 2.946193469
#&gt; [37,] 1.5872347 0.8023620 2.234054545
#&gt; [38,] 1.1985388 1.9795947 1.730649774
#&gt;