Commit c55948c0 authored by DURIF Ghislain's avatar DURIF Ghislain

switch reference to official Durif et al (2018)

parent a654b3de
......@@ -31,14 +31,14 @@
#' @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.
......@@ -117,10 +117,11 @@
#' \code{hatYtest} labels.}
#'
#' @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}.
#'
#' @author
#' Ghislain Durif (\url{http://thoth.inrialpes.fr/people/gdurif/}).
......
......@@ -32,14 +32,14 @@
#' 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
......@@ -107,10 +107,11 @@
#' \code{return.grid} is set to FALSE.}
#'
#' @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}.
#'
#' @author
#' Ghislain Durif (\url{http://thoth.inrialpes.fr/people/gdurif/}).
......
......@@ -32,7 +32,7 @@
#' 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}.
......@@ -43,7 +43,7 @@
#' 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
......@@ -105,7 +105,7 @@
#'
#' @return 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
......@@ -114,10 +114,11 @@
#' model.}
#'
#' @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)
......
......@@ -33,7 +33,7 @@
#' @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
......@@ -41,7 +41,7 @@
#' 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.
......@@ -104,18 +104,18 @@
#' \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.}
......@@ -131,10 +131,11 @@
#' \code{hatYtest} labels.}
#'
#' @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}.
#'
#' @author
#' Ghislain Durif (\url{http://thoth.inrialpes.fr/people/gdurif/}).
......
......@@ -32,14 +32,14 @@
#' 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
......@@ -108,10 +108,11 @@
#' \code{return.grid} is set to FALSE.}
#'
#' @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}.
#'
#' @author
#' Ghislain Durif (\url{http://thoth.inrialpes.fr/people/gdurif/}).
......
......@@ -32,7 +32,7 @@
#' 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}.
......@@ -43,7 +43,7 @@
#' 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
......@@ -109,7 +109,7 @@
#'
#' @return 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
......@@ -118,10 +118,11 @@
#' model.}
#'
#' @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)
......
......@@ -49,7 +49,7 @@
#' 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).
#'
#' @param n the number of observations in the sample.
#' @param p the number of covariates in the sample.
......@@ -103,10 +103,11 @@
#' (with the command \code{set.seed}) used for random number generation.}
#'
#' @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}.
#'
#' @author
#' Ghislain Durif (\url{http://thoth.inrialpes.fr/people/gdurif/}).
......
......@@ -51,7 +51,7 @@
#' 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).
#'
#' @param n the number of observations in the sample.
#' @param p the number of covariates in the sample.
......@@ -106,10 +106,11 @@
#' (with the command \code{set.seed}) used for random number generation.}
#'
#' @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}.
#'
#' @author
#' Ghislain Durif (\url{http://thoth.inrialpes.fr/people/gdurif/}).
......
......@@ -32,14 +32,14 @@
#' @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.
......@@ -142,10 +142,11 @@
#' step was adaptive or not.}
#'
#' @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
......
......@@ -31,14 +31,14 @@
#' 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
......@@ -103,10 +103,11 @@
#' \code{cv.grid} is NULL if \code{return.grid} is set to FALSE.}
#'
#' @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}.
#'
#' @author
#' Ghislain Durif (\url{http://thoth.inrialpes.fr/people/gdurif/}).
......
......@@ -32,7 +32,7 @@
#' 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}.
#'
......@@ -42,7 +42,7 @@
#' 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
......@@ -106,7 +106,7 @@
#'
#' @return 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
......@@ -115,10 +115,11 @@
#' model.}
#'
#' @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)
......
......@@ -28,13 +28,13 @@
#' @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
......@@ -49,7 +49,7 @@
#' 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
......@@ -59,9 +59,9 @@
#' @param stab.out the output of the functions \code{\link{spls.stab}},
#' \code{\link{logit.spls.stab}} or \code{\link{multinom.spls.stab}}.
#' @param 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).
#' @param 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).
#'
#' @return An object with the following attributes:
#' \item{selected.predictors}{The list of the name of covariates that
......@@ -70,6 +70,17 @@
#' each covariate, i.e. the maximal values on the reduced grid of
#' hyper-parameters.}
#'
#' @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.
#'
#' @author
#' Ghislain Durif (\url{http://thoth.inrialpes.fr/people/gdurif/}).
#'
......@@ -142,11 +153,11 @@ stability.selection <- function(stab.out, piThreshold=0.9, rhoError=10) {
#' 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
......@@ -161,7 +172,7 @@ stability.selection <- function(stab.out, piThreshold=0.9, rhoError=10) {
#' 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}},
......
......@@ -49,7 +49,7 @@ sample.cont(n, p, kstar, lstar, beta.min, beta.max, mean.H=0, sigma.H,
The response is generated as Y = X \%*\% B + E, where E is some gaussian noise N(0,sigma.E^2).
The details of the procedure are developped by Durif et al. (2015).
The details of the procedure are developped by Durif et al. (2018).
}
......@@ -86,13 +86,16 @@ sample.cont(n, p, kstar, lstar, beta.min, beta.max, mean.H=0, sigma.H,
\references{
G. Durif, F. Picard, S. Lambert-Lacroix (2015). Adaptive sparse PLS for logistic regression,
(in prep), available on (\url{http://arxiv.org/}).
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}.
}
\author{
Ghislain Durif (\url{http://lbbe.univ-lyon1.fr/-Durif-Ghislain-.html}).
Ghislain Durif (\url{http://thoth.inrialpes.fr/people/gdurif/}).
}
\seealso{\code{\link{sample.bin}}.}
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