R/tune.R
In mixOmicsTeam/mixOmics: Omics Data Integration Project
# ========================================================================================================
# tun: chose the optimal number of parameters per component on a "method"
# ========================================================================================================
#' Wrapper function to tune pls-derived methods.
#'
#' @template description/tune
#'
#' @details
#' See the help file corresponding to the corresponding \code{method}, e.g.
#' \code{tune.splsda} for further details. Note that only the arguments used in
#' the tune function corresponding to \code{method} are passed on.
#'
#' More details about the prediction distances in \code{?predict} and the
#' supplemental material of the mixOmics article (Rohart et al. 2017). More
#' details about the PLS modes are in \code{?pls}.
#'
#' @inheritParams block.splsda
#' @param method This parameter is used to pass all other argument to the
#' suitable function. \code{method} has to be one of the following: "spls",
#' "splsda", "block.splsda", "mint.splsda", "rcc", "pca", "spca" or "pls".
#' @param X numeric matrix of predictors. \code{NA}s are allowed.
#' @param Y Either a factor or a class vector for the discrete outcome, or a
#' numeric vector or matrix of continuous responses (for multi-response
#' models).
#' @param multilevel Design matrix for multilevel analysis (for repeated
#' measurements) that indicates the repeated measures on each individual, i.e.
#' the individuals ID. See Details.
#' @param ncomp the number of components to include in the model.
#' @param study grouping factor indicating which samples are from the same
#' study
#' @param test.keepX numeric vector for the different number of variables to
#' test from the \eqn{X} data set
#' @param test.keepY If \code{method = 'spls'}, numeric vector for the
#' different number of variables to test from the \eqn{Y} data set
#' @param already.tested.X Optional, if \code{ncomp > 1} A numeric vector
#' indicating the number of variables to select from the \eqn{X} data set on
#' the firsts components.
#' @param already.tested.Y if \code{method = 'spls'} and \code{if(ncomp > 1)}
#' numeric vector indicating the number of variables to select from the \eqn{Y}
#' data set on the first components
#' @param mode character string. What type of algorithm to use, (partially)
#' matching one of \code{"regression"}, \code{"canonical"}, \code{"invariant"}
#' or \code{"classic"}. See Details.
#' @param nrepeat Number of times the Cross-Validation process is repeated.
#' @param grid1,grid2 vector numeric defining the values of \code{lambda1} and
#' \code{lambda2} at which cross-validation score should be computed. Defaults
#' to \code{grid1=grid2=seq(0.001, 1, length=5)}.
#' @param validation character. What kind of (internal) validation to use,
#' matching one of \code{"Mfold"} or \code{"loo"} (see below). Default is
#' \code{"Mfold"}.
#' @param folds the folds in the Mfold cross-validation. See Details.
#' @param dist distance metric to estimate the
#' classification error rate, should be a subset of \code{"centroids.dist"},
#' \code{"mahalanobis.dist"} or \code{"max.dist"} (see Details).
#' @param measure The tuning measure used for different methods. See details.
#' @param auc if \code{TRUE} calculate the Area Under the Curve (AUC)
#' performance of the model.
#' @param progressBar by default set to \code{TRUE} to output the progress bar
#' of the computation.
#' @param near.zero.var Logical, see the internal \code{\link{nearZeroVar}}
#' function (should be set to TRUE in particular for data with many zero
#' values). Default value is FALSE
#' @param logratio one of ('none','CLR'). Default to 'none'
#' @param center a logical value indicating whether the variables should be
#' shifted to be zero centered. Alternately, a vector of length equal the
#' number of columns of \code{X} can be supplied. The value is passed to
#' \code{\link{scale}}.
#' @param scale a logical value indicating whether the variables should be
#' scaled to have unit variance before the analysis takes place. The default is
#' \code{FALSE} for consistency with \code{prcomp} function, but in general
#' scaling is advisable. Alternatively, a vector of length equal the number of
#' columns of \code{X} can be supplied. The value is passed to
#' \code{\link{scale}}.
#' @param max.iter Integer, the maximum number of iterations.
#' @param tol Numeric, convergence tolerance criteria.
#' @param light.output if set to FALSE, the prediction/classification of each
#' sample for each of \code{test.keepX} and each comp is returned.
#' @param weighted tune using either the performance of the Majority vote or
#' the Weighted vote.
#' @param signif.threshold numeric between 0 and 1 indicating the significance
#' threshold required for improvement in error rate of the components. Default
#' to 0.01.
#' @param V Matrix used in the logratio transformation id provided (for tune.pca)
#' @param BPPARAM A \linkS4class{BiocParallelParam} object indicating the type
#' of parallelisation. See examples.
#' @param seed set a number here if you want the function to give reproducible outputs.
#' Not recommended during exploratory analysis. Note if RNGseed is set in 'BPPARAM', this will be overwritten by 'seed'.
#' @return Depending on the type of analysis performed and the input arguments,
#' a list that may contain:
#'
#' \item{error.rate}{returns the prediction error for each \code{test.keepX} on
#' each component, averaged across all repeats and subsampling folds. Standard
#' deviation is also output. All error rates are also available as a list.}
#' \item{choice.keepX}{returns the number of variables selected (optimal keepX)
#' on each component.} \item{choice.ncomp}{For supervised models; returns the
#' optimal number of components for the model for each prediction distance
#' using one-sided t-tests that test for a significant difference in the mean
#' error rate (gain in prediction) when components are added to the model. See
#' more details in Rohart et al 2017 Suppl. For more than one block, an optimal
#' ncomp is returned for each prediction framework.}
#' \item{error.rate.class}{returns the error rate for each level of \code{Y}
#' and for each component computed with the optimal keepX}
#'
#' \item{predict}{Prediction values for each sample, each \code{test.keepX},
#' each comp and each repeat. Only if light.output=FALSE}
#' \item{class}{Predicted class for each sample, each \code{test.keepX}, each
#' comp and each repeat. Only if light.output=FALSE}
#'
#' \item{auc}{AUC mean and standard deviation if the number of categories in
#' \code{Y} is greater than 2, see details above. Only if auc = TRUE}
#'
#' \item{cor.value}{only if multilevel analysis with 2 factors: correlation
#' between latent variables.}
#' @author Florian Rohart, Francois Bartolo, Kim-Anh Lê Cao, Al J Abadi
#' @seealso \code{\link{tune.rcc}}, \code{\link{tune.mint.splsda}},
#' \code{\link{tune.pca}}, \code{\link{tune.splsda}},
#' \code{\link{tune.splslevel}} and http://www.mixOmics.org for more details.
#' @references
#' Singh A., Shannon C., Gautier B., Rohart F., Vacher M., Tebbutt S.
#' and Lê Cao K.A. (2019), DIABLO: an integrative approach for identifying key
#' molecular drivers from multi-omics assays, Bioinformatics,
#' Volume 35, Issue 17, 1 September 2019, Pages 3055–3062.
#'
#' mixOmics article:
#'
#' Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: an R package for 'omics
#' feature selection and multiple data integration. PLoS Comput Biol 13(11):
#' e1005752
#'
#' MINT:
#'
#' Rohart F, Eslami A, Matigian, N, Bougeard S, Lê Cao K-A (2017). MINT: A
#' multivariate integrative approach to identify a reproducible biomarker
#' signature across multiple experiments and platforms. BMC Bioinformatics
#' 18:128.
#'
#' PLS and PLS citeria for PLS regression: Tenenhaus, M. (1998). \emph{La
#' regression PLS: theorie et pratique}. Paris: Editions Technic.
#'
#' Chavent, Marie and Patouille, Brigitte (2003). Calcul des coefficients de
#' regression et du PRESS en regression PLS1. \emph{Modulad n}, \bold{30} 1-11.
#' (this is the formula we use to calculate the Q2 in perf.pls and perf.spls)
#'
#' Mevik, B.-H., Cederkvist, H. R. (2004). Mean Squared Error of Prediction
#' (MSEP) Estimates for Principal Component Regression (PCR) and Partial Least
#' Squares Regression (PLSR). \emph{Journal of Chemometrics} \bold{18}(9),
#' 422-429.
#'
#' sparse PLS regression mode:
#'
#' Lê Cao, K. A., Rossouw D., Robert-Granie, C. and Besse, P. (2008). A sparse
#' PLS for variable selection when integrating Omics data. \emph{Statistical
#' Applications in Genetics and Molecular Biology} \bold{7}, article 35.
#'
#' One-sided t-tests (suppl material):
#'
#' Rohart F, Mason EA, Matigian N, Mosbergen R, Korn O, Chen T, Butcher S,
#' Patel J, Atkinson K, Khosrotehrani K, Fisk NM, Lê Cao K-A&, Wells CA&
#' (2016). A Molecular Classification of Human Mesenchymal Stromal Cells. PeerJ
#' 4:e1845.
#' @keywords regression multivariate
#' @export
#' @example ./examples/tune-examples.R
tune <-
function (method = c("pls", "spls", "plsda", "splsda", "block.plsda", "block.splsda", "mint.plsda", "mint.splsda", "rcc", "pca", "spca"),
# Input data
X,
Y,
# Sparse params
test.keepX = c(5, 10, 15),
test.keepY = NULL,
already.tested.X,
already.tested.Y,
# Number of components
ncomp,
## Params related to model building
V, # PCA
center = TRUE, # PCA
grid1 = seq(0.001, 1, length = 5), # CCA
grid2 = seq(0.001, 1, length = 5), # CCA
mode = c("regression", "canonical", "invariant", "classic"), # PLS
indY, # block PLSDA
weighted = TRUE, # block PLSDA
design, # block PLSDA
study, # MINT PLSDA
tol = 1e-09, # PLSDA, block PLSDA, mint PLSDA, PCA
scale = TRUE, # PLS, PLSDA, block PLSDA, mint PLSDA, PCA
logratio = c('none','CLR'), # PLS, PLSDA, PCA
near.zero.var = FALSE, # PLS, PLSDA, block PLSDA, mint PLSDA
max.iter = 100, # PLS, PLSDA, block PLSDA, mint PLSDA, PCA
multilevel = NULL, # PLS, PLSDA, PCA
## Params related to cross-validation
validation = "Mfold",
nrepeat = 1,
folds = 10,
signif.threshold = 0.01,
# How to measure accuracy - only for DA methods
dist = "max.dist",
measure = ifelse(method == "spls", "cor", "BER"),
auc = FALSE,
# Running params
seed = NULL,
BPPARAM = SerialParam(),
progressBar = FALSE,
# Output params
light.output = TRUE
)
{
## ----------- checks ----------- ##
method = match.arg(method)
mode <- match.arg(mode)
# hardcode scheme and init
scheme = "horst"
init = "svd.single"
## ----------- PCA ----------- ##
if (method == "pca") {
message("Calling 'tune.pca'")
if (missing(ncomp))
ncomp = NULL
result = tune.pca(
# model building params
X = X, ncomp = ncomp,
center = center, scale = scale, max.iter = max.iter, tol = tol)
## ----------- sPCA ----------- ##
} else if (method == "spca") {
message("Calling 'tune.spca'")
if (missing(ncomp))
ncomp = 2
#TODO use match.call() arg matching for these function calls
result = tune.spca(
# model building params
X = X, ncomp = ncomp,
center = center, scale = scale,
# sparsity params
test.keepX = test.keepX,
# CV params
nrepeat = nrepeat, folds = folds,
# running params
BPPARAM = BPPARAM, seed = seed)
## ----------- CCA ----------- ##
} else if (method == "rcc") {
message("Calling 'tune.rcc'")
result = tune.rcc(
# model building params
X = X, Y = Y,
# sparsity params
grid1 = grid1, grid2 = grid2,
# CV params
validation = validation, folds = folds,
# running params
BPPARAM = BPPARAM, seed = seed)
## ----------- PLS ----------- ##
} else if (method == "pls") {
message("Calling 'tune.pls'")
result = tune.pls(
# model building params
X = X, Y = Y, ncomp = ncomp, mode = mode,
scale = scale, logratio = logratio, tol = tol,
max.iter = max.iter, near.zero.var = near.zero.var,
# CV params
validation = validation, nrepeat = nrepeat, folds = folds,
# PA params
measure = measure,
# running params
progressBar = progressBar,
BPPARAM = BPPARAM, seed = seed)
# multilevel??
## ----------- sPLS +/- multilevel ----------- ##
} else if (method == "spls") {
if(missing(multilevel))
{
message("Calling 'tune.spls'")
result = tune.spls(
# model building params
X = X, Y = Y, ncomp = ncomp, mode = mode,
scale = scale, logratio = logratio, tol = tol,
max.iter = max.iter, near.zero.var = near.zero.var,
# sparsity params
test.keepX = test.keepX, test.keepY = test.keepY,
# CV params
validation = validation, nrepeat = nrepeat, folds = folds,
# PA params
measure = measure,
# running params
progressBar = progressBar,
BPPARAM = BPPARAM, seed = seed)
} else {
message("Calling 'tune.splslevel' with method = 'spls'")
if (missing(ncomp))
ncomp = 1
if (missing(already.tested.X))
already.tested.X = NULL
if (missing(already.tested.Y))
already.tested.Y = NULL
result = tune.splslevel(
# model building params
X = X, Y = Y, ncomp = ncomp, mode = mode,
# multilevel params
multilevel = multilevel,
# sparsity params
test.keepX = test.keepX, test.keepY = test.keepY,
already.tested.X = already.tested.X, already.tested.Y = already.tested.Y,
# running params
BPPARAM = BPPARAM, seed = seed)
}
## ----------- plsda ----------- ##
} else if (method == "plsda") {
message("Calling 'tune.plsda'")
if (missing(ncomp))
ncomp = 1
result = tune.plsda(
# model building params
X = X, Y = Y, ncomp = ncomp, multilevel = multilevel,
scale = scale, logratio = logratio, tol = tol,
max.iter = max.iter, near.zero.var = near.zero.var,
# CV params
validation = validation, folds = folds, nrepeat = nrepeat,
# PA params
dist = dist, auc = auc,
# running params
progressBar = progressBar, light.output = light.output,
BPPARAM = BPPARAM, seed = seed)
## ----------- splsda ----------- ##
} else if (method == "splsda") {
message("Calling 'tune.splsda'")
if (missing(ncomp))
ncomp = 1
result = tune.splsda(
# model building params
X = X, Y = Y, ncomp = ncomp, multilevel = multilevel,
scale = scale, logratio = logratio, tol = tol,
max.iter = max.iter, near.zero.var = near.zero.var,
# sparsity params
test.keepX = test.keepX, already.tested.X = already.tested.X,
# CV params
validation = validation, folds = folds, nrepeat = nrepeat,
# PA params
dist = dist, measure = measure, auc = auc,
# running params
progressBar = progressBar, light.output = light.output,
BPPARAM = BPPARAM, seed = seed)
## ----------- block.plsda ----------- ##
} else if (method == "block.plsda") {
message("Calling 'tune.block.plsda'")
result = tune.block.plsda(
# model building params
X = X, Y = Y, indY = indY, ncomp = ncomp, design = design,
tol = tol, scale = scale, max.iter = max.iter, near.zero.var = near.zero.var,
# CV params
validation = validation, folds = folds, nrepeat = nrepeat, signif.threshold = signif.threshold,
# PA params
dist = dist, weighted = weighted,
# running params
progressBar = progressBar, light.output = light.output,
BPPARAM = BPPARAM, seed = seed)
## ----------- block.splsda ----------- ##
} else if (method == "block.splsda") {
message("Calling 'tune.block.splsda'")
result = tune.block.splsda(
# model building params
X = X, Y = Y, indY = indY, ncomp = ncomp, design = design,
tol = tol, scale = scale, max.iter = max.iter, near.zero.var = near.zero.var,
# sparsity params
test.keepX = test.keepX, already.tested.X = already.tested.X,
# CV params
validation = validation, folds = folds, nrepeat = nrepeat, signif.threshold = signif.threshold,
# PA params
dist = dist, weighted = weighted, measure = measure,
# running params
progressBar = progressBar, light.output = light.output,
BPPARAM = BPPARAM, seed = seed)
## ----------- mint.plsda ----------- ##
} else if (method == "mint.plsda") {
message("Calling 'tune.mint.plsda' with Leave-One-Group-Out Cross Validation (nrepeat = 1)")
if (missing(ncomp))
ncomp = 1
result = tune.mint.plsda(
# model building params
X = X, Y = Y, ncomp = ncomp, study = study,
scale = scale, tol = tol, max.iter = max.iter, near.zero.var = near.zero.var,
# PA params
dist = dist, auc = auc,
# running params - no need for seed or BPPARAM due to LOO CV
progressBar = progressBar,light.output = light.output)
## ----------- mint.splsda ----------- ##
} else if (method == "mint.splsda") {
message("Calling 'tune.mint.splsda' with Leave-One-Group-Out Cross Validation (nrepeat = 1)")
if (missing(ncomp))
ncomp = 1
result = tune.mint.splsda(
# model building params
X = X, Y = Y, ncomp = ncomp, study = study,
scale = scale, tol = tol, max.iter = max.iter, near.zero.var = near.zero.var,
# sparsity params
test.keepX = test.keepX, already.tested.X = already.tested.X,
# PA params
dist = dist, measure = measure, auc = auc,
# running params - no need for seed or BPPARAM due to LOO CV
progressBar = progressBar,light.output = light.output)
}
## ----------- end ----------- ##
result$call = match.call()
return(result)
}
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# ========================================================================================================
# tun: chose the optimal number of parameters per component on a "method"
# ========================================================================================================
#' Wrapper function to tune pls-derived methods.
#'
#' @template description/tune
#'
#' @details
#' See the help file corresponding to the corresponding \code{method}, e.g.
#' \code{tune.splsda} for further details. Note that only the arguments used in
#' the tune function corresponding to \code{method} are passed on.
#'
#' More details about the prediction distances in \code{?predict} and the
#' supplemental material of the mixOmics article (Rohart et al. 2017). More
#' details about the PLS modes are in \code{?pls}.
#'
#' @inheritParams block.splsda
#' @param method This parameter is used to pass all other argument to the
#' suitable function. \code{method} has to be one of the following: "spls",
#' "splsda", "block.splsda", "mint.splsda", "rcc", "pca", "spca" or "pls".
#' @param X numeric matrix of predictors. \code{NA}s are allowed.
#' @param Y Either a factor or a class vector for the discrete outcome, or a
#' numeric vector or matrix of continuous responses (for multi-response
#' models).
#' @param multilevel Design matrix for multilevel analysis (for repeated
#' measurements) that indicates the repeated measures on each individual, i.e.
#' the individuals ID. See Details.
#' @param ncomp the number of components to include in the model.
#' @param study grouping factor indicating which samples are from the same
#' study
#' @param test.keepX numeric vector for the different number of variables to
#' test from the \eqn{X} data set
#' @param test.keepY If \code{method = 'spls'}, numeric vector for the
#' different number of variables to test from the \eqn{Y} data set
#' @param already.tested.X Optional, if \code{ncomp > 1} A numeric vector
#' indicating the number of variables to select from the \eqn{X} data set on
#' the firsts components.
#' @param already.tested.Y if \code{method = 'spls'} and \code{if(ncomp > 1)}
#' numeric vector indicating the number of variables to select from the \eqn{Y}
#' data set on the first components
#' @param mode character string. What type of algorithm to use, (partially)
#' matching one of \code{"regression"}, \code{"canonical"}, \code{"invariant"}
#' or \code{"classic"}. See Details.
#' @param nrepeat Number of times the Cross-Validation process is repeated.
#' @param grid1,grid2 vector numeric defining the values of \code{lambda1} and
#' \code{lambda2} at which cross-validation score should be computed. Defaults
#' to \code{grid1=grid2=seq(0.001, 1, length=5)}.
#' @param validation character. What kind of (internal) validation to use,
#' matching one of \code{"Mfold"} or \code{"loo"} (see below). Default is
#' \code{"Mfold"}.
#' @param folds the folds in the Mfold cross-validation. See Details.
#' @param dist distance metric to estimate the
#' classification error rate, should be a subset of \code{"centroids.dist"},
#' \code{"mahalanobis.dist"} or \code{"max.dist"} (see Details).
#' @param measure The tuning measure used for different methods. See details.
#' @param auc if \code{TRUE} calculate the Area Under the Curve (AUC)
#' performance of the model.
#' @param progressBar by default set to \code{TRUE} to output the progress bar
#' of the computation.
#' @param near.zero.var Logical, see the internal \code{\link{nearZeroVar}}
#' function (should be set to TRUE in particular for data with many zero
#' values). Default value is FALSE
#' @param logratio one of ('none','CLR'). Default to 'none'
#' @param center a logical value indicating whether the variables should be
#' shifted to be zero centered. Alternately, a vector of length equal the
#' number of columns of \code{X} can be supplied. The value is passed to
#' \code{\link{scale}}.
#' @param scale a logical value indicating whether the variables should be
#' scaled to have unit variance before the analysis takes place. The default is
#' \code{FALSE} for consistency with \code{prcomp} function, but in general
#' scaling is advisable. Alternatively, a vector of length equal the number of
#' columns of \code{X} can be supplied. The value is passed to
#' \code{\link{scale}}.
#' @param max.iter Integer, the maximum number of iterations.
#' @param tol Numeric, convergence tolerance criteria.
#' @param light.output if set to FALSE, the prediction/classification of each
#' sample for each of \code{test.keepX} and each comp is returned.
#' @param weighted tune using either the performance of the Majority vote or
#' the Weighted vote.
#' @param signif.threshold numeric between 0 and 1 indicating the significance
#' threshold required for improvement in error rate of the components. Default
#' to 0.01.
#' @param V Matrix used in the logratio transformation id provided (for tune.pca)
#' @param BPPARAM A \linkS4class{BiocParallelParam} object indicating the type
#' of parallelisation. See examples.
#' @param seed set a number here if you want the function to give reproducible outputs.
#' Not recommended during exploratory analysis. Note if RNGseed is set in 'BPPARAM', this will be overwritten by 'seed'.
#' @return Depending on the type of analysis performed and the input arguments,
#' a list that may contain:
#'
#' \item{error.rate}{returns the prediction error for each \code{test.keepX} on
#' each component, averaged across all repeats and subsampling folds. Standard
#' deviation is also output. All error rates are also available as a list.}
#' \item{choice.keepX}{returns the number of variables selected (optimal keepX)
#' on each component.} \item{choice.ncomp}{For supervised models; returns the
#' optimal number of components for the model for each prediction distance
#' using one-sided t-tests that test for a significant difference in the mean
#' error rate (gain in prediction) when components are added to the model. See
#' more details in Rohart et al 2017 Suppl. For more than one block, an optimal
#' ncomp is returned for each prediction framework.}
#' \item{error.rate.class}{returns the error rate for each level of \code{Y}
#' and for each component computed with the optimal keepX}
#'
#' \item{predict}{Prediction values for each sample, each \code{test.keepX},
#' each comp and each repeat. Only if light.output=FALSE}
#' \item{class}{Predicted class for each sample, each \code{test.keepX}, each
#' comp and each repeat. Only if light.output=FALSE}
#'
#' \item{auc}{AUC mean and standard deviation if the number of categories in
#' \code{Y} is greater than 2, see details above. Only if auc = TRUE}
#'
#' \item{cor.value}{only if multilevel analysis with 2 factors: correlation
#' between latent variables.}
#' @author Florian Rohart, Francois Bartolo, Kim-Anh Lê Cao, Al J Abadi
#' @seealso \code{\link{tune.rcc}}, \code{\link{tune.mint.splsda}},
#' \code{\link{tune.pca}}, \code{\link{tune.splsda}},
#' \code{\link{tune.splslevel}} and http://www.mixOmics.org for more details.
#' @references
#' Singh A., Shannon C., Gautier B., Rohart F., Vacher M., Tebbutt S.
#' and Lê Cao K.A. (2019), DIABLO: an integrative approach for identifying key
#' molecular drivers from multi-omics assays, Bioinformatics,
#' Volume 35, Issue 17, 1 September 2019, Pages 3055–3062.
#'
#' mixOmics article:
#'
#' Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: an R package for 'omics
#' feature selection and multiple data integration. PLoS Comput Biol 13(11):
#' e1005752
#'
#' MINT:
#'
#' Rohart F, Eslami A, Matigian, N, Bougeard S, Lê Cao K-A (2017). MINT: A
#' multivariate integrative approach to identify a reproducible biomarker
#' signature across multiple experiments and platforms. BMC Bioinformatics
#' 18:128.
#'
#' PLS and PLS citeria for PLS regression: Tenenhaus, M. (1998). \emph{La
#' regression PLS: theorie et pratique}. Paris: Editions Technic.
#'
#' Chavent, Marie and Patouille, Brigitte (2003). Calcul des coefficients de
#' regression et du PRESS en regression PLS1. \emph{Modulad n}, \bold{30} 1-11.
#' (this is the formula we use to calculate the Q2 in perf.pls and perf.spls)
#'
#' Mevik, B.-H., Cederkvist, H. R. (2004). Mean Squared Error of Prediction
#' (MSEP) Estimates for Principal Component Regression (PCR) and Partial Least
#' Squares Regression (PLSR). \emph{Journal of Chemometrics} \bold{18}(9),
#' 422-429.
#'
#' sparse PLS regression mode:
#'
#' Lê Cao, K. A., Rossouw D., Robert-Granie, C. and Besse, P. (2008). A sparse
#' PLS for variable selection when integrating Omics data. \emph{Statistical
#' Applications in Genetics and Molecular Biology} \bold{7}, article 35.
#'
#' One-sided t-tests (suppl material):
#'
#' Rohart F, Mason EA, Matigian N, Mosbergen R, Korn O, Chen T, Butcher S,
#' Patel J, Atkinson K, Khosrotehrani K, Fisk NM, Lê Cao K-A&, Wells CA&
#' (2016). A Molecular Classification of Human Mesenchymal Stromal Cells. PeerJ
#' 4:e1845.
#' @keywords regression multivariate
#' @export
#' @example ./examples/tune-examples.R
tune <-
function (method = c("pls", "spls", "plsda", "splsda", "block.plsda", "block.splsda", "mint.plsda", "mint.splsda", "rcc", "pca", "spca"),
# Input data
X,
Y,
# Sparse params
test.keepX = c(5, 10, 15),
test.keepY = NULL,
already.tested.X,
already.tested.Y,
# Number of components
ncomp,
## Params related to model building
V, # PCA
center = TRUE, # PCA
grid1 = seq(0.001, 1, length = 5), # CCA
grid2 = seq(0.001, 1, length = 5), # CCA
mode = c("regression", "canonical", "invariant", "classic"), # PLS
indY, # block PLSDA
weighted = TRUE, # block PLSDA
design, # block PLSDA
study, # MINT PLSDA
tol = 1e-09, # PLSDA, block PLSDA, mint PLSDA, PCA
scale = TRUE, # PLS, PLSDA, block PLSDA, mint PLSDA, PCA
logratio = c('none','CLR'), # PLS, PLSDA, PCA
near.zero.var = FALSE, # PLS, PLSDA, block PLSDA, mint PLSDA
max.iter = 100, # PLS, PLSDA, block PLSDA, mint PLSDA, PCA
multilevel = NULL, # PLS, PLSDA, PCA
## Params related to cross-validation
validation = "Mfold",
nrepeat = 1,
folds = 10,
signif.threshold = 0.01,
# How to measure accuracy - only for DA methods
dist = "max.dist",
measure = ifelse(method == "spls", "cor", "BER"),
auc = FALSE,
# Running params
seed = NULL,
BPPARAM = SerialParam(),
progressBar = FALSE,
# Output params
light.output = TRUE
)
{
## ----------- checks ----------- ##
method = match.arg(method)
mode <- match.arg(mode)
# hardcode scheme and init
scheme = "horst"
init = "svd.single"
## ----------- PCA ----------- ##
if (method == "pca") {
message("Calling 'tune.pca'")
if (missing(ncomp))
ncomp = NULL
result = tune.pca(
# model building params
X = X, ncomp = ncomp,
center = center, scale = scale, max.iter = max.iter, tol = tol)
## ----------- sPCA ----------- ##
} else if (method == "spca") {
message("Calling 'tune.spca'")
if (missing(ncomp))
ncomp = 2
#TODO use match.call() arg matching for these function calls
result = tune.spca(
# model building params
X = X, ncomp = ncomp,
center = center, scale = scale,
# sparsity params
test.keepX = test.keepX,
# CV params
nrepeat = nrepeat, folds = folds,
# running params
BPPARAM = BPPARAM, seed = seed)
## ----------- CCA ----------- ##
} else if (method == "rcc") {
message("Calling 'tune.rcc'")
result = tune.rcc(
# model building params
X = X, Y = Y,
# sparsity params
grid1 = grid1, grid2 = grid2,
# CV params
validation = validation, folds = folds,
# running params
BPPARAM = BPPARAM, seed = seed)
## ----------- PLS ----------- ##
} else if (method == "pls") {
message("Calling 'tune.pls'")
result = tune.pls(
# model building params
X = X, Y = Y, ncomp = ncomp, mode = mode,
scale = scale, logratio = logratio, tol = tol,
max.iter = max.iter, near.zero.var = near.zero.var,
# CV params
validation = validation, nrepeat = nrepeat, folds = folds,
# PA params
measure = measure,
# running params
progressBar = progressBar,
BPPARAM = BPPARAM, seed = seed)
# multilevel??
## ----------- sPLS +/- multilevel ----------- ##
} else if (method == "spls") {
if(missing(multilevel))
{
message("Calling 'tune.spls'")
result = tune.spls(
# model building params
X = X, Y = Y, ncomp = ncomp, mode = mode,
scale = scale, logratio = logratio, tol = tol,
max.iter = max.iter, near.zero.var = near.zero.var,
# sparsity params
test.keepX = test.keepX, test.keepY = test.keepY,
# CV params
validation = validation, nrepeat = nrepeat, folds = folds,
# PA params
measure = measure,
# running params
progressBar = progressBar,
BPPARAM = BPPARAM, seed = seed)
} else {
message("Calling 'tune.splslevel' with method = 'spls'")
if (missing(ncomp))
ncomp = 1
if (missing(already.tested.X))
already.tested.X = NULL
if (missing(already.tested.Y))
already.tested.Y = NULL
result = tune.splslevel(
# model building params
X = X, Y = Y, ncomp = ncomp, mode = mode,
# multilevel params
multilevel = multilevel,
# sparsity params
test.keepX = test.keepX, test.keepY = test.keepY,
already.tested.X = already.tested.X, already.tested.Y = already.tested.Y,
# running params
BPPARAM = BPPARAM, seed = seed)
}
## ----------- plsda ----------- ##
} else if (method == "plsda") {
message("Calling 'tune.plsda'")
if (missing(ncomp))
ncomp = 1
result = tune.plsda(
# model building params
X = X, Y = Y, ncomp = ncomp, multilevel = multilevel,
scale = scale, logratio = logratio, tol = tol,
max.iter = max.iter, near.zero.var = near.zero.var,
# CV params
validation = validation, folds = folds, nrepeat = nrepeat,
# PA params
dist = dist, auc = auc,
# running params
progressBar = progressBar, light.output = light.output,
BPPARAM = BPPARAM, seed = seed)
## ----------- splsda ----------- ##
} else if (method == "splsda") {
message("Calling 'tune.splsda'")
if (missing(ncomp))
ncomp = 1
result = tune.splsda(
# model building params
X = X, Y = Y, ncomp = ncomp, multilevel = multilevel,
scale = scale, logratio = logratio, tol = tol,
max.iter = max.iter, near.zero.var = near.zero.var,
# sparsity params
test.keepX = test.keepX, already.tested.X = already.tested.X,
# CV params
validation = validation, folds = folds, nrepeat = nrepeat,
# PA params
dist = dist, measure = measure, auc = auc,
# running params
progressBar = progressBar, light.output = light.output,
BPPARAM = BPPARAM, seed = seed)
## ----------- block.plsda ----------- ##
} else if (method == "block.plsda") {
message("Calling 'tune.block.plsda'")
result = tune.block.plsda(
# model building params
X = X, Y = Y, indY = indY, ncomp = ncomp, design = design,
tol = tol, scale = scale, max.iter = max.iter, near.zero.var = near.zero.var,
# CV params
validation = validation, folds = folds, nrepeat = nrepeat, signif.threshold = signif.threshold,
# PA params
dist = dist, weighted = weighted,
# running params
progressBar = progressBar, light.output = light.output,
BPPARAM = BPPARAM, seed = seed)
## ----------- block.splsda ----------- ##
} else if (method == "block.splsda") {
message("Calling 'tune.block.splsda'")
result = tune.block.splsda(
# model building params
X = X, Y = Y, indY = indY, ncomp = ncomp, design = design,
tol = tol, scale = scale, max.iter = max.iter, near.zero.var = near.zero.var,
# sparsity params
test.keepX = test.keepX, already.tested.X = already.tested.X,
# CV params
validation = validation, folds = folds, nrepeat = nrepeat, signif.threshold = signif.threshold,
# PA params
dist = dist, weighted = weighted, measure = measure,
# running params
progressBar = progressBar, light.output = light.output,
BPPARAM = BPPARAM, seed = seed)
## ----------- mint.plsda ----------- ##
} else if (method == "mint.plsda") {
message("Calling 'tune.mint.plsda' with Leave-One-Group-Out Cross Validation (nrepeat = 1)")
if (missing(ncomp))
ncomp = 1
result = tune.mint.plsda(
# model building params
X = X, Y = Y, ncomp = ncomp, study = study,
scale = scale, tol = tol, max.iter = max.iter, near.zero.var = near.zero.var,
# PA params
dist = dist, auc = auc,
# running params - no need for seed or BPPARAM due to LOO CV
progressBar = progressBar,light.output = light.output)
## ----------- mint.splsda ----------- ##
} else if (method == "mint.splsda") {
message("Calling 'tune.mint.splsda' with Leave-One-Group-Out Cross Validation (nrepeat = 1)")
if (missing(ncomp))
ncomp = 1
result = tune.mint.splsda(
# model building params
X = X, Y = Y, ncomp = ncomp, study = study,
scale = scale, tol = tol, max.iter = max.iter, near.zero.var = near.zero.var,
# sparsity params
test.keepX = test.keepX, already.tested.X = already.tested.X,
# PA params
dist = dist, measure = measure, auc = auc,
# running params - no need for seed or BPPARAM due to LOO CV
progressBar = progressBar,light.output = light.output)
}
## ----------- end ----------- ##
result$call = match.call()
return(result)
}
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