Commit 26f1de07 authored by GD's avatar GD

automatic generation of the doc

parent 23f5e65a
......@@ -99,14 +99,14 @@ new observations in \code{Xtest}, that are used to predict the
\description{
The function \code{logit.spls} performs compression and variable selection
in the context of binary classification (with possible prediction)
using Durif et al. (2017) algorithm based on Ridge IRLS and sparse PLS.
using Durif et al. (2018) algorithm based on Ridge IRLS and sparse PLS.
}
\details{
The columns of the data matrices \code{Xtrain} and \code{Xtest} may
not be standardized, since standardizing can be performed by the function
\code{logit.spls} as a preliminary step.
The procedure described in Durif et al. (2017) is used to compute
The procedure described in Durif et al. (2018) is used to compute
latent sparse components that are used in a logistic regression model.
In addition, when a matrix \code{Xtest} is supplied, the procedure
predicts the response associated to these new values of the predictors.
......@@ -147,10 +147,11 @@ sum(model1$hatYtest!=Ytest) / length(index.test)
}
\references{
Durif G., Modolo L., Michaelsson J., Mold J. E., Lambert-Lacroix S.,
Picard F. (2017). High Dimensional Classification with combined Adaptive
Sparse PLS and Logistic Regression, (in prep),
available on (\url{http://arxiv.org/abs/1502.05933}).
Durif, G., Modolo, L., Michaelsson, J., Mold, J.E., Lambert-Lacroix, S.,
Picard, F., 2018. High dimensional classification with combined
adaptive sparse PLS and logistic regression. Bioinformatics 34,
485--493. \url{https://doi.org/10.1093/bioinformatics/btx571}.
Available at \url{http://arxiv.org/abs/1502.05933}.
}
\seealso{
\code{\link{spls}}, \code{\link{logit.spls.cv}}
......
......@@ -88,14 +88,14 @@ error rate averaged over the folds. \code{cv.grid} is NULL if
The function \code{logit.spls.cv} chooses the optimal values for the
hyper-parameter of the \code{logit.spls} procedure, by minimizing the
averaged error of prediction over the hyper-parameter grid,
using Durif et al. (2017) LOGIT-SPLS algorithm.
using Durif et al. (2018) LOGIT-SPLS algorithm.
}
\details{
The columns of the data matrices \code{X} may not be standardized,
since standardizing is performed by the function \code{logit.spls.cv}
as a preliminary step.
The procedure is described in Durif et al. (2017). The K-fold
The procedure is described in Durif et al. (2018). The K-fold
cross-validation can be summarize as follow: the train set is partitioned
into K folds, for each value of hyper-parameters the model is fit K times,
using each fold to compute the prediction error rate, and fitting the
......@@ -141,10 +141,11 @@ str(cv1)
}
\references{
Durif G., Modolo L., Michaelsson J., Mold J. E., Lambert-Lacroix S.,
Picard F. (2017). High Dimensional Classification with combined Adaptive
Sparse PLS and Logistic Regression, (in prep),
available on (\url{http://arxiv.org/abs/1502.05933}).
Durif, G., Modolo, L., Michaelsson, J., Mold, J.E., Lambert-Lacroix, S.,
Picard, F., 2018. High dimensional classification with combined
adaptive sparse PLS and logistic regression. Bioinformatics 34,
485--493. \url{https://doi.org/10.1093/bioinformatics/btx571}.
Available at \url{http://arxiv.org/abs/1502.05933}.
}
\seealso{
\code{\link{logit.spls}}, \code{\link{logit.spls.stab}}
......
......@@ -67,7 +67,7 @@ the seed for pseudo-random number generation is set accordingly.}
\value{
An object with the following attributes
\item{q.Lambda}{A table with values of q.Lambda (c.f. Durif
et al. (2017) for the notation), being the averaged number of covariates
et al. (2018) for the notation), being the averaged number of covariates
selected among the entire grid of hyper-parameters candidates values,
for increasing size of hyper-parameter grid.}
\item{probs.lambda}{A table with estimated probability of selection for each
......@@ -81,7 +81,7 @@ candidate values \code{(ncomp, lambda.l1, lambda.ridge)} of hyper-parameters
on multiple sub-samplings in the data. The stability selection procedure
selects the covariates that are selected by most of the models among the
grid of hyper-parameters, following the procedure described in
Durif et al. (2017). Candidates values for \code{ncomp}, \code{lambda.l1}
Durif et al. (2018). Candidates values for \code{ncomp}, \code{lambda.l1}
and \code{lambda.l2} are respectively given by
the input arguments \code{ncomp.range}, \code{lambda.l1.range}
and \code{lambda.l2.range}.
......@@ -91,7 +91,7 @@ The columns of the data matrices \code{X} may not be standardized,
since standardizing is performed by the function \code{logit.spls.stab}
as a preliminary step.
The procedure is described in Durif et al. (2017). The stability selection
The procedure is described in Durif et al. (2018). The stability selection
procedure can be summarize as follow (c.f. Meinshausen and Buhlmann, 2010).
(i) For each candidate values \code{(ncomp, lambda.l1, lambda.ridge)} of
......@@ -155,10 +155,11 @@ stability.selection(stab1, piThreshold=0.6, rhoError=10)
}
\references{
Durif G., Modolo L., Michaelsson J., Mold J. E., Lambert-Lacroix S.,
Picard F. (2017). High Dimensional Classification with combined Adaptive
Sparse PLS and Logistic Regression, (in prep),
available on (\url{http://arxiv.org/abs/1502.05933}).
Durif, G., Modolo, L., Michaelsson, J., Mold, J.E., Lambert-Lacroix, S.,
Picard, F., 2018. High dimensional classification with combined
adaptive sparse PLS and logistic regression. Bioinformatics 34,
485--493. \url{https://doi.org/10.1093/bioinformatics/btx571}.
Available at \url{http://arxiv.org/abs/1502.05933}.
Meinshausen, N., Buhlmann P. (2010). Stability Selection. Journal of the
Royal Statistical Society: Series B (Statistical Methodology)
......
......@@ -82,18 +82,18 @@ converge in less than \code{maxIter} iterations or not.}
\item{X.score}{list of nclass-1 different (n x ncomp) matrices being
the observations coordinates or scores in the new component basis produced
for each class in the multinomial model by the SPLS step (sparse PLS),
see Durif et al. (2017) for details.}
see Durif et al. (2018) for details.}
\item{X.weight}{list of nclass-1 different (p x ncomp) matrices being
the coefficients of predictors in each components produced for each class
in the multinomial model by the sparse PLS,
see Durif et al. (2017) for details.}
see Durif et al. (2018) for details.}
\item{X.score.full}{a ((n x (nclass-1)) x ncomp) matrix being the
observations coordinates or scores in the new component basis produced
by the SPLS step (sparse PLS) in the linearized multinomial model, see
Durif et al. (2017). Each column t.k of \code{X.score} is a SPLS component.}
Durif et al. (2018). Each column t.k of \code{X.score} is a SPLS component.}
\item{X.weight.full}{a (p x ncomp) matrix being the coefficients of predictors
in each components produced by sparse PLS in the linearized multinomial
model, see Durif et al. (2017). Each column w.k of
model, see Durif et al. (2018). Each column w.k of
\code{X.weight} verifies t.k = Xtrain x w.k (as a matrix product).}
\item{lambda.ridge}{the Ridge hyper-parameter used to fit the model.}
\item{lambda.l1}{the sparse hyper-parameter used to fit the model.}
......@@ -111,7 +111,7 @@ new observations in \code{Xtest}, that are used to predict the
\description{
The function \code{multinom.spls} performs compression and variable selection
in the context of multi-label ('nclass' > 2) classification
(with possible prediction) using Durif et al. (2017) algorithm
(with possible prediction) using Durif et al. (2018) algorithm
based on Ridge IRLS and sparse PLS.
}
\details{
......@@ -119,7 +119,7 @@ The columns of the data matrices \code{Xtrain} and \code{Xtest} may
not be standardized, since standardizing can be performed by the function
\code{multinom.spls} as a preliminary step.
The procedure described in Durif et al. (2017) is used to compute
The procedure described in Durif et al. (2018) is used to compute
latent sparse components that are used in a multinomial regression model.
In addition, when a matrix \code{Xtest} is supplied, the procedure
predicts the response associated to these new values of the predictors.
......@@ -161,10 +161,11 @@ sum(model1$hatYtest!=Ytest) / length(index.test)
}
\references{
Durif G., Modolo L., Michaelsson J., Mold J. E., Lambert-Lacroix S.,
Picard F. (2017). High Dimensional Classification with combined Adaptive
Sparse PLS and Logistic Regression, (in prep),
available on (\url{http://arxiv.org/abs/1502.05933}).
Durif, G., Modolo, L., Michaelsson, J., Mold, J.E., Lambert-Lacroix, S.,
Picard, F., 2018. High dimensional classification with combined
adaptive sparse PLS and logistic regression. Bioinformatics 34,
485--493. \url{https://doi.org/10.1093/bioinformatics/btx571}.
Available at \url{http://arxiv.org/abs/1502.05933}.
}
\seealso{
\code{\link{spls}}, \code{\link{logit.spls}},
......
......@@ -89,14 +89,14 @@ error rate averaged over the folds. \code{cv.grid} is NULL if
The function \code{multinom.spls.cv} chooses the optimal values for the
hyper-parameter of the \code{multinom.spls} procedure, by minimizing the
averaged error of prediction over the hyper-parameter grid,
using Durif et al. (2017) multinomial-SPLS algorithm.
using Durif et al. (2018) multinomial-SPLS algorithm.
}
\details{
The columns of the data matrices \code{X} may not be standardized,
since standardizing is performed by the function \code{multinom.spls.cv}
as a preliminary step.
The procedure is described in Durif et al. (2017). The K-fold
The procedure is described in Durif et al. (2018). The K-fold
cross-validation can be summarize as follow: the train set is partitioned
into K folds, for each value of hyper-parameters the model is fit K times,
using each fold to compute the prediction error rate, and fitting the
......@@ -143,10 +143,11 @@ str(cv1)
}
\references{
Durif G., Modolo L., Michaelsson J., Mold J. E., Lambert-Lacroix S.,
Picard F. (2017). High Dimensional Classification with combined Adaptive
Sparse PLS and Logistic Regression, (in prep),
available on (\url{http://arxiv.org/abs/1502.05933}).
Durif, G., Modolo, L., Michaelsson, J., Mold, J.E., Lambert-Lacroix, S.,
Picard, F., 2018. High dimensional classification with combined
adaptive sparse PLS and logistic regression. Bioinformatics 34,
485--493. \url{https://doi.org/10.1093/bioinformatics/btx571}.
Available at \url{http://arxiv.org/abs/1502.05933}.
}
\seealso{
\code{\link{multinom.spls}}, \code{\link{multinom.spls.stab}}
......
......@@ -68,7 +68,7 @@ the seed for pseudo-random number generation is set accordingly.}
\value{
An object with the following attributes
\item{q.Lambda}{A table with values of q.Lambda (c.f. Durif
et al. (2017) for the notation), being the averaged number of covariates
et al. (2018) for the notation), being the averaged number of covariates
selected among the entire grid of hyper-parameters candidates values,
for increasing size of hyper-parameter grid.}
\item{probs.lambda}{A table with estimated probability of selection for each
......@@ -82,7 +82,7 @@ each candidate values \code{(ncomp, lambda.l1, lambda.ridge)} of
hyper-parameters on multiple sub-samplings in the data. The stability
selection procedure selects the covariates that are selected by most of the
models among the grid of hyper-parameters, following the procedure
described in Durif et al. (2017). Candidates values for \code{ncomp},
described in Durif et al. (2018). Candidates values for \code{ncomp},
\code{lambda.l1} and \code{lambda.l2} are respectively given by
the input arguments \code{ncomp.range}, \code{lambda.l1.range}
and \code{lambda.l2.range}.
......@@ -92,7 +92,7 @@ The columns of the data matrices \code{X} may not be standardized,
since standardizing is performed by the function \code{multinom.spls.stab}
as a preliminary step.
The procedure is described in Durif et al. (2017). The stability selection
The procedure is described in Durif et al. (2018). The stability selection
procedure can be summarize as follow (c.f. Meinshausen and Buhlmann, 2010).
(i) For each candidate values \code{(ncomp, lambda.l1, lambda.ridge)} of
......@@ -160,10 +160,11 @@ stability.selection(stab1, piThreshold=0.6, rhoError=10)
}
\references{
Durif G., Modolo L., Michaelsson J., Mold J. E., Lambert-Lacroix S.,
Picard F. (2017). High Dimensional Classification with combined Adaptive
Sparse PLS and Logistic Regression, (in prep),
available on (\url{http://arxiv.org/abs/1502.05933}).
Durif, G., Modolo, L., Michaelsson, J., Mold, J.E., Lambert-Lacroix, S.,
Picard, F., 2018. High dimensional classification with combined
adaptive sparse PLS and logistic regression. Bioinformatics 34,
485--493. \url{https://doi.org/10.1093/bioinformatics/btx571}.
Available at \url{http://arxiv.org/abs/1502.05933}.
Meinshausen, N., Buhlmann P. (2010). Stability Selection. Journal of the
Royal Statistical Society: Series B (Statistical Methodology)
......
......@@ -5,8 +5,8 @@
\title{Generates covariate matrix X with correlated block of covariates and
a binary random reponse depening on X through a logistic model}
\usage{
sample.bin(n, p, kstar, lstar, beta.min, beta.max, mean.H = 0, sigma.H,
sigma.F, seed = NULL)
sample.bin(n, p, kstar, lstar, beta.min, beta.max, mean.H = 0, sigma.H = 1,
sigma.F = 1, seed = NULL)
}
\arguments{
\item{n}{the number of observations in the sample.}
......@@ -94,7 +94,7 @@ the ones with null coefficients are not.
The response is generated as by drawing one observation of n different
Bernoulli random variables of parameters logit^\{-1\}(XB).
The details of the procedure are developped by Durif et al. (2017).
The details of the procedure are developped by Durif et al. (2018).
}
\examples{
### load plsgenomics library
......@@ -111,10 +111,11 @@ str(sample1)
}
\references{
Durif G., Modolo L., Michaelsson J., Mold J. E., Lambert-Lacroix S.,
Picard F. (2017). High Dimensional Classification with combined Adaptive
Sparse PLS and Logistic Regression, (in prep),
available on (\url{http://arxiv.org/abs/1502.05933}).
Durif, G., Modolo, L., Michaelsson, J., Mold, J.E., Lambert-Lacroix, S.,
Picard, F., 2018. High dimensional classification with combined
adaptive sparse PLS and logistic regression. Bioinformatics 34,
485--493. \url{https://doi.org/10.1093/bioinformatics/btx571}.
Available at \url{http://arxiv.org/abs/1502.05933}.
}
\seealso{
\code{\link{sample.cont}}
......
......@@ -6,7 +6,7 @@
a multi-label random reponse depening on X through a multinomial model}
\usage{
sample.multinom(n, p, nb.class = 2, kstar, lstar, beta.min, beta.max,
mean.H = 0, sigma.H, sigma.F, seed = NULL)
mean.H = 0, sigma.H = 1, sigma.F = 1, seed = NULL)
}
\arguments{
\item{n}{the number of observations in the sample.}
......@@ -96,7 +96,7 @@ the ones with null coefficients are not.
The response is generated as by drawing one observation of n different
Bernoulli random variables of parameters logit^\{-1\}(XB).
The details of the procedure are developped by Durif et al. (2017).
The details of the procedure are developped by Durif et al. (2018).
}
\examples{
### load plsgenomics library
......@@ -115,10 +115,11 @@ str(sample1)
}
\references{
Durif G., Modolo L., Michaelsson J., Mold J. E., Lambert-Lacroix S.,
Picard F. (2017). High Dimensional Classification with combined Adaptive
Sparse PLS and Logistic Regression, (in prep),
available on (\url{http://arxiv.org/abs/1502.05933}).
Durif, G., Modolo, L., Michaelsson, J., Mold, J.E., Lambert-Lacroix, S.,
Picard, F., 2018. High dimensional classification with combined
adaptive sparse PLS and logistic regression. Bioinformatics 34,
485--493. \url{https://doi.org/10.1093/bioinformatics/btx571}.
Available at \url{http://arxiv.org/abs/1502.05933}.
}
\seealso{
\code{\link{sample.cont}}
......
......@@ -121,14 +121,14 @@ step was adaptive or not.}
\description{
The function \code{spls.adapt} performs compression and variable selection
in the context of linear regression (with possible prediction)
using Durif et al. (2017) adaptive SPLS algorithm.
using Durif et al. (2018) adaptive SPLS algorithm.
}
\details{
The columns of the data matrices \code{Xtrain} and \code{Xtest} may
not be standardized, since standardizing can be performed by the function
\code{spls} as a preliminary step.
The procedure described in Durif et al. (2017) is used to compute
The procedure described in Durif et al. (2018) is used to compute
latent sparse components that are used in a regression model.
In addition, when a matrix \code{Xtest} is supplied, the procedure
predicts the response associated to these new values of the predictors.
......@@ -183,10 +183,11 @@ points(-1000:1000,-1000:1000, type="l")
}
\references{
Durif G., Modolo L., Michaelsson J., Mold J. E., Lambert-Lacroix S.,
Picard F. (2017). High Dimensional Classification with combined Adaptive
Sparse PLS and Logistic Regression, (in prep),
available on (\url{http://arxiv.org/abs/1502.05933}).
Durif, G., Modolo, L., Michaelsson, J., Mold, J.E., Lambert-Lacroix, S.,
Picard, F., 2018. High dimensional classification with combined
adaptive sparse PLS and logistic regression. Bioinformatics 34,
485--493. \url{https://doi.org/10.1093/bioinformatics/btx571}.
Available at \url{http://arxiv.org/abs/1502.05933}.
Chun, H., & Keles, S. (2010). Sparse partial least squares regression for
simultaneous dimension reduction and variable selection. Journal of the
......
......@@ -83,14 +83,14 @@ error rate over the folds.
The function \code{spls.cv} chooses the optimal values for the
hyper-parameter of the \code{spls} procedure, by minimizing the mean
squared error of prediction over the hyper-parameter grid,
using Durif et al. (2017) adaptive SPLS algorithm.
using Durif et al. (2018) adaptive SPLS algorithm.
}
\details{
The columns of the data matrices \code{Xtrain} and \code{Xtest} may not
be standardized, since standardizing can be performed by the function
\code{spls.cv} as a preliminary step.
The procedure is described in Durif et al. (2017). The K-fold
The procedure is described in Durif et al. (2018). The K-fold
cross-validation can be summarize as follow: the train set is partitioned
into K folds, for each value of hyper-parameters the model is fit K times,
using each fold to compute the prediction error rate, and fitting the
......@@ -136,10 +136,11 @@ cv1$ncomp.opt
}
\references{
Durif G., Modolo L., Michaelsson J., Mold J. E., Lambert-Lacroix S.,
Picard F. (2017). High Dimensional Classification with combined Adaptive
Sparse PLS and Logistic Regression, (in prep),
available on (\url{http://arxiv.org/abs/1502.05933}).
Durif, G., Modolo, L., Michaelsson, J., Mold, J.E., Lambert-Lacroix, S.,
Picard, F., 2018. High dimensional classification with combined
adaptive sparse PLS and logistic regression. Bioinformatics 34,
485--493. \url{https://doi.org/10.1093/bioinformatics/btx571}.
Available at \url{http://arxiv.org/abs/1502.05933}.
}
\seealso{
\code{\link{spls}}
......
......@@ -69,7 +69,7 @@ the seed for pseudo-random number generation is set accordingly.}
\value{
An object with the following attributes
\item{q.Lambda}{A table with values of q.Lambda (c.f. Durif
et al. (2017) for the notation), being the averaged number of covariates
et al. (2018) for the notation), being the averaged number of covariates
selected among the entire grid of hyper-parameters candidates values,
for increasing size of hyper-parameter grid.}
\item{probs.lambda}{A table with estimated probability of selection for each
......@@ -83,7 +83,7 @@ candidate values \code{(ncomp, lambda.l1)} of hyper-parameters
on multiple sub-samplings in the data. The stability selection procedure
selects the covariates that are selected by most of the models among the
grid of hyper-parameters, following the procedure described in
Durif et al. (2017). Candidates values for \code{ncomp} and \code{lambda.l1}
Durif et al. (2018). Candidates values for \code{ncomp} and \code{lambda.l1}
are respectively given by the input arguments \code{ncomp.range} and
\code{lambda.l1.range}.
}
......@@ -92,7 +92,7 @@ The columns of the data matrices \code{X} may not be standardized,
since standardizing is performed by the function \code{spls.stab}
as a preliminary step.
The procedure is described in Durif et al. (2017). The stability selection
The procedure is described in Durif et al. (2018). The stability selection
procedure can be summarize as follow (c.f. Meinshausen and Buhlmann, 2010).
(i) For each candidate values \code{(ncomp, lambda.l1)} of
......@@ -149,10 +149,11 @@ stability.selection(stab1, piThreshold=0.6, rhoError=10)
}
\references{
Durif G., Modolo L., Michaelsson J., Mold J. E., Lambert-Lacroix S.,
Picard F. (2017). High Dimensional Classification with combined Adaptive
Sparse PLS and Logistic Regression, (in prep),
available on (\url{http://arxiv.org/abs/1502.05933}).
Durif, G., Modolo, L., Michaelsson, J., Mold, J.E., Lambert-Lacroix, S.,
Picard, F., 2018. High dimensional classification with combined
adaptive sparse PLS and logistic regression. Bioinformatics 34,
485--493. \url{https://doi.org/10.1093/bioinformatics/btx571}.
Available at \url{http://arxiv.org/abs/1502.05933}.
Meinshausen, N., Buhlmann P. (2010). Stability Selection. Journal of the
Royal Statistical Society: Series B (Statistical Methodology)
......
......@@ -12,10 +12,10 @@ stability.selection(stab.out, piThreshold = 0.9, rhoError = 10)
\code{\link{logit.spls.stab}} or \code{\link{multinom.spls.stab}}.}
\item{piThreshold}{a value in (0,1], corresponding to the threshold
probability used to select covariate (c.f. Durif et al., 2017).}
probability used to select covariate (c.f. Durif et al., 2018).}
\item{rhoError}{a positive value used to restrict the grid of
hyper-parameter candidate values (c.f. Durif et al., 2017).}
hyper-parameter candidate values (c.f. Durif et al., 2018).}
}
\value{
An object with the following attributes:
......@@ -28,12 +28,12 @@ hyper-parameters.}
\description{
The function \code{stability.selection} returns the list of selected
covariates, when following the stability selection procedure described in
Durif et al. (2017). In particular, it selects covariates that are selected
Durif et al. (2018). In particular, it selects covariates that are selected
by most of the sparse PLS, the logit-SPLS or the multinomial-SPLS models
when exploring the grid of hyper-parameter candidate values.
}
\details{
The procedure is described in Durif et al. (2017). The stability selection
The procedure is described in Durif et al. (2018). The stability selection
procedure can be summarize as follow (c.f. Meinshausen and Buhlmann, 2010).
(i) For each candidate values of hyper-parameters, a model is trained
......@@ -48,7 +48,7 @@ function \code{\link{stability.selection.heatmap}}.
set of covariates that were selected by most of the training among the
grid of hyper-parameters candidate values, based on a threshold probability
\code{piThreshold} and a restriction of the grid of hyper-parameters based
on \code{rhoError} (c.f. Durif et al., 2017, for details).
on \code{rhoError} (c.f. Durif et al., 2018, for details).
This function achieves the second step (ii) of the stability selection
procedure. The first step (i) is achieved by the functions
......@@ -87,6 +87,17 @@ str(stab1)
stability.selection(stab1, piThreshold=0.6, rhoError=10)
}
}
\references{
Durif, G., Modolo, L., Michaelsson, J., Mold, J.E., Lambert-Lacroix, S.,
Picard, F., 2018. High dimensional classification with combined
adaptive sparse PLS and logistic regression. Bioinformatics 34,
485--493. \url{https://doi.org/10.1093/bioinformatics/btx571}.
Available at \url{http://arxiv.org/abs/1502.05933}.
Meinshausen, N., Buhlmann P. (2010). Stability Selection. Journal of the
Royal Statistical Society: Series B (Statistical Methodology)
72, no. 4, 417-473.
}
\seealso{
\code{\link{spls.stab}}, \code{\link{logit.spls.stab}},
......
......@@ -22,10 +22,10 @@ The function \code{stability.selection.heatmap} allows to visualize
estimated probabilities to be selected for each covariate depending on the
value of hyper-parameters in the spls, logit-spls or multinomial-spls models.
These estimated probabilities are used in the stability selection procedure
described in Durif et al. (2017).
described in Durif et al. (2018).
}
\details{
The procedure is described in Durif et al. (2017). The stability selection
The procedure is described in Durif et al. (2018). The stability selection
procedure can be summarize as follow (c.f. Meinshausen and Buhlmann, 2010).
(i) For each candidate values of hyper-parameters, a model is trained
......@@ -40,7 +40,7 @@ function \code{\link{stability.selection.heatmap}}.
set of covariates that were selected by most of the training among the
grid of hyper-parameters candidate values, based on a threshold probability
\code{piThreshold} and a restriction of the grid of hyper-parameters based
on \code{rhoError} (c.f. Durif et al., 2017, for details).
on \code{rhoError} (c.f. Durif et al., 2018, for details).
This function allows to visualize probabalities estimated at the first
step (i) of the stability selection by the functions \code{\link{spls.stab}},
......
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