#' Wrapper function for kernel multitask regression
#'
#' Wrapper function to perform kernel multitask regression with \code{cv.kmr} that trains a model on training set and then predicts on test set for multiple tasks.
#'
#' @param patientsKernelTrain Precomputed kernel Gram matrix of \code{n} training patients, of dimension \code{n x n}.
#' @param patientsKernelTest Precomputed kernel Gram matrix of \code{m} test patients crossing \code{n} training patients, of dimension \code{m x n}.
#' @param response Matrix of observed toxicity values, of dimension \code{n x t}, for the \code{n} training patients responding to \code{t} drugs.
#' @param drugsKernel Kernel Gram matrix of the \code{t} drugs, of dimension \code{t x t}.
#' @param lambdas Sequence of lambdas that must be tested to fit a cross-validated KMR model. Default is exp(-15:25).
#' @param nfolds Number of folds for cross-validation. Default is 5.
#' @param nrepeats Number of times the k-fold cross-validation is performed. Default is 1.
#'
#' @return A matrix of predicted toxicity values, of dimension \code{m x t}, for the \code{m} test patients responding to the \code{t} drugs.
#'
#' @export
#'
#' @note Multitask prediction is made, for which task relationships are encoded in \code{drugsKernel}.
#'
#' @references
#' Bernard, E., Jiao, Y., Scornet, E., Stoven, V., Walter, T., and Vert, J.-P. (2017). "Kernel multitask regression for toxicogenetics." \href{https://doi.org/10.1101/171298}{bioRxiv-171298}.
#'
#' @seealso \code{\link[kmr]{cv.kmr}}
#'
#' @importFrom kmr cv.kmr
#'
predictorKMR <- function(patientsKernelTrain,
patientsKernelTest,
response,
drugsKernel,
lambdas = exp(-15:25),
nfolds = 5,
nrepeats = 1)
{
patientsKernelTrain <- as.matrix(patientsKernelTrain)
patientsKernelTest <- as.matrix(patientsKernelTest)
# Train a mkr model
cvobj <- kmr::cv.kmr(x = patientsKernelTrain, y = response, kx_type = "precomputed",
kt_type = "precomputed", kt_option = list(kt = drugsKernel),
lambda = lambdas, nfolds = nfolds, nrepeats = nrepeats)
# Make predictions
pred <- predict(cvobj, patientsKernelTest)
pred <- data.frame(pred)
dimnames(pred) <- list(rownames(patientsKernelTest), colnames(response))
return(pred)
}
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