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--- a +++ b/data-raw/lmmSpline-method.R @@ -0,0 +1,534 @@ +# Jasmin Straube, Queensland Facility of Advanced Bioinformatics +# Part of this script was borrowed from the lm function from the Stats package the lme function of the nlme package +# and functions from the lmeSpline, reshape and gdata packages +# +# This program is free software; you can redistribute it and/or +# modify it under the terms of the GNU Moleculesral Public License +# as published by the Free Software Foundation; either version 2 +# of the License, or (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Moleculesral Public License for more details. +# +# You should have received a copy of the GNU Moleculesral Public License +# along with this program; if not, write to the Free Software +# Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. + + +#' Data-driven linear mixed effect model spline modelling +#' +#' Function that models a linear or limear mixed model depending on the best fit. Alternatively, the function can return THE derivation information of the fitted models +#' for the fixed (original) times points and a chosen \code{basis}. +#' +#' @import methods +#' @importFrom nlme lme lmeControl pdIdent pdDiag +#' @importFrom parallel parLapply detectCores makeCluster clusterExport stopCluster +#' @importFrom gdata drop.levels +#' @importFrom lmeSplines smspline approx.Z +#' @importFrom reshape2 melt dcast +#' @importFrom stats lm predict.lm predict anova quantile na.exclude +#' @usage lmmSpline(data, time, sampleID, timePredict, deri, basis, knots, keepModels,numCores) +#' @param data \code{data.frame} or \code{matrix} containing the samples as rows and features as columns +#' @param time \code{numeric} vector containing the sample time point information. +#' @param sampleID \code{character}, \code{numeric} or \code{factor} vector containing information about the unique identity of each sample +#' @param timePredict \code{numeric} vector containing the time points to be predicted. By default set to the original time points observed in the experiment. +#' @param deri \code{logical} value. If \code{TRUE} returns the predicted derivative information on the observed time points.By default set to \code{FALSE}. +#' @param basis \code{character} string. What type of basis to use, matching one of \code{"cubic"}, \code{"p-spline"} or \code{"cubic p-spline"}. The \code{"cubic"} basis (\code{default}) is the cubic smoothing spline as defined by Verbyla \emph{et al.} 1999, the \code{"p-spline"} is the truncated p-spline basis as defined by Durban \emph{et al.} 2005. +#' @param knots Alternatively an \code{integer}, the number of knots used for the \code{"p-spline"} or \code{"cubic p-spline"} basis calculation. Otherwise calculated as proposed by Ruppert 2002. Not used for the "cubic" smoothing spline basis as it used the inner design points. +#' @param keepModels alternative \code{logical} value if you want to keep the model output. Default value is FALSE +#' @param numCores Alternative \code{numeric} value indicating the number of CPU cores to be used. Default value is automatically estimated. +#' @details +#' The first model (\code{modelsUsed}=0) assumes the response is a straight line not affected by individual variation. +#' +#' Let \eqn{y_{ij}(t_{ij})} be the expression of a feature for individual (or biological replicate) \eqn{i} at time \eqn{t_{ij}}, where \eqn{i=1,2,...,n}, \eqn{j=1,2,...,m_i}, \eqn{n} is the sample size and \eqn{m_i} is the number of observations for individual \eqn{i} for the given feature. +#' We fit a simple linear regression of expression \eqn{y_{ij}(t_{ij})} on time \eqn{t_{ij}}. +#' The intercept \eqn{\beta_0} and slope \eqn{\beta_1} are estimated via ordinary least squares: +#' \eqn{y_{ij}(t_{ij})= \beta_0 + \beta_1 t_{ij} + \epsilon_{ij}}, where \eqn{\epsilon_{ij} ~ N(0,\sigma^2_{\epsilon}).} +#' The second model (\code{modelsUsed}=1) is nonlinear where the straight line in regression replaced with a curve modelled using here for example a spline truncated line basis (\code{basis}="p-spline") as proposed Durban \emph{et al.} 2005: +#' +#' \deqn{y_{ij}(t_{ij})= f(t_{ij}) +\epsilon_{ij},} +#' +#' where \eqn{\epsilon_{ij}~ N(0,\sigma_{\epsilon}^2).} +#' +#' The penalized spline is represented by \eqn{f}, which depends on a set of knot positions \eqn{\kappa_1,...,\kappa_K} in the range of \eqn{{t_{ij}}}, some unknown coefficients \eqn{u_k}, an intercept \eqn{\beta_0} and a slope \eqn{\beta_1}. The first term in the above equation can therefore be expanded as: +#' \deqn{f(t_{ij})= \beta_0+ \beta_1t_{ij}+\sum\limits_{k=1}^{K}u_k(t_{ij}-\kappa_k)_+,} +#' with \eqn{(t_{ij}-\kappa_k)_+=t_{ij}-\kappa_k}, if \eqn{t_{ij}-\kappa_k > 0, 0} otherwise. +#' +#' The choice of the number of knots \eqn{K} and their positions influences the flexibility of the curve. +#' If the argument \code{knots}=missing, we use a method proposed by Ruppert 2002 to estimate the number of knots given the measured number of time points \eqn{T}, so that the knots \eqn{\kappa_1 \ldots \kappa_K} are placed at quantiles of the time interval of interest: +#' +#' \deqn{K= max(5,min(floor(\frac{T}{4}) , 40)).} +#' +#' In order to account for individual variation, our third model (\code{modelsUsed}=2) adds a subject-specific random effect \eqn{U_i} to the mean response \eqn{f(t_{ij})}. +#' Assuming \eqn{f(t_{ij})} to be a fixed (yet unknown) population curve, \eqn{U_i} is treated as a random realization of an underlying Gaussian process with zero-mean and variance \eqn{\sigma_U^2} and is independent from the random error \eqn{\epsilon_{ij}}: +#' +#' \deqn{y_{ij}(t_{ij}) = f(t_{ij}) + U_i + \epsilon_{ij}} +#' +#' with \eqn{U_{i} ~ N(0,\sigma_U^2)} and \eqn{\epsilon_{ij} ~ N(0,\sigma_{\epsilon}^2)}. + +#' In the equation above, the individual curves are expected to be parallel to the mean curve as we assume the individual expression curves to be constant over time. +#' A simple extension to this model is to assume individual deviations are straight lines. The fourth model (\code{modelsUsed}=3) therefore fits individual-specific random intercepts \eqn{a_{i0}} and slopes \eqn{a_{i1}}: +#' +#' \deqn{y_{ij}(t_{ij}) = f(t_{ij}) + a_{i0} + a_{i1}t_{ij} + \epsilon_{ij}} +#' +#' with \eqn{\epsilon_{ij} ~ N(0,\sigma_\epsilon^2)} and \eqn{(a_{i0},a_{i1})^T} ~ \eqn{ N(0,\Sigma).} +#' We assume independence between the random intercept and slope. +#' @return lmmSpline returns an object of class \code{lmmspline} containing the following components: +#' \itemize{ +#' \item{predSpline}{\code{data.frame} containing predicted values based on linear model object or linear mixed effect model object.} +#' \item{modelsUsed}{\code{numeric} vector indicating the model used to fit the data. 0 = linear model, 1=linear mixed effect model spline (LMMS) with defined basis ('cubic' by default) 2 = LMMS taking subject-specific random intercept, 3 = LMMS with subject specific intercept and slope.} +#' \item{model}{\code{list} of models used to model time profiles.} +#' \item{derivative}{\code{logical} value indicating if the predicted values are the derivative information.} +#' } +#' @references Durban, M., Harezlak, J., Wand, M. P., & Carroll, R. J. (2005). \emph{Simple fitting of subject-specific curves for longitudinal data.} Stat. Med., 24(8), 1153-67. +#' @references Ruppert, D. (2002). \emph{Selecting the number of knots for penalized splines.} J. Comp. Graph. Stat. 11, 735-757 +#' @references Verbyla, A. P., Cullis, B. R., & Kenward, M. G. (1999). \emph{The analysis of designed experiments and longitudinal data by using smoothing splines.} Appl.Statist, 18(3), 269-311. +#' @references Straube J., Gorse A.-D., Huang B.E., Le Cao K.-A. (2015). \emph{A linear mixed model spline framework for analyzing time course 'omics' data} PLOSONE, 10(8), e0134540. +# @seealso \code{\link[lmms]{summary.lmmspline}}, \code{\link[lmms]{plot.lmmspline}}, \code{\link[lmms]{predict.lmmspline}}, \code{\link[lmms]{deriv.lmmspline}} +#' @examples +#' \dontrun{ +#' data(kidneySimTimeGroup) +#' # running for samples in group 1 +#' G1 <- which(kidneySimTimeGroup$group=="G1") +#' testLMMSpline<- lmmSpline(data=kidneySimTimeGroup$data[G1,],time=kidneySimTimeGroup$time[G1], +#' sampleID=kidneySimTimeGroup$sampleID[G1]) +#' summary(testLMMSpline) +#' DerivTestLMMSplineTG<- lmmSpline(data=as.data.frame(kidneySimTimeGroup$data[G1,]), +#' time=kidneySimTimeGroup$time[G1],sampleID=kidneySimTimeGroup$sampleID[G1], +#' deri=TRUE,basis="p-spline") +#' summary(DerivTestLMMSplineTG)} +# setGeneric('lmmSpline',function(data,time,sampleID,timePredict,deri,basis,knots,keepModels,numCores){standardGeneric('lmmSpline')}) +# setClassUnion("matrixOrFrame",c('matrix','data.frame')) +# setClassUnion("missingOrnumeric", c("missing", "numeric")) +# setClassUnion("missingOrcharacter", c("missing", "character")) +# setClassUnion("missingOrlogical", c("missing", "logical")) +# setClassUnion("factorOrcharacterOrnumeric", c("factor", "character","numeric")) +# # @rdname lmmSpline-methods +# # @aliases lmmSpline,matrixOrFrame,numeric,factorOrcharacterOrnumeric, +# # missingOrlogical,missingOrcharacter,missingOrnumeric,missingOrlogical,missingOrnumeric-method +# # @exportMethod lmmSpline +# +# setMethod('lmmSpline',c(data="matrixOrFrame",time="numeric",sampleID="factorOrcharacterOrnumeric",timePredict="missingOrnumeric", deri="missingOrlogical", basis="missingOrcharacter",knots="missingOrnumeric",keepModels="missingOrlogical",numCores="missingOrnumeric"), function(data,time,sampleID,timePredict,deri,basis,knots,keepModels,numCores){ +# +# lmmSplinePara(data=data,time=time,sampleID=sampleID,timePredict=timePredict,deri=deri,basis=basis, knots=knots,keepModels=keepModels,numCores=numCores) +# }) +# @name lmmSpline + +#' @docType methods +#' @rdname lmmSpline-methods +#' @importFrom parallel detectCores parLapply clusterExport +#' @export +lmmSpline <- function(data, time, sampleID, timePredict, deri, basis, knots,keepModels, numCores){ + + if(missing(keepModels)) + keepModels <- F + if(missing(timePredict)) + timePredict <- sort(unique(time)) + if(missing(basis)) + basis <- "cubic" + + if(missing(deri)){ + deri <- FALSE + }else{ + deri <- deri + } + + basis.collection <- c("cubic","p-spline","cubic p-spline") + if(!basis%in% basis.collection) + stop(cat("Chosen basis is not available. Choose:", paste(basis.collection,collapse=', '))) + if(diff(range(c(length(sampleID),length(time),nrow(data))))>0) + stop("Size of the input vectors rep, time and nrow(data) are not equal") + if(missing(knots)& (basis=="p-spline"|basis=='cubic p-spline')) + warning("The number of knots is automatically estimated") + if(deri & basis=='cubic') + stop('To calculate the derivative choose either "p-spline" or "cubic p-spline" as basis') + + options(show.error.messages = TRUE) + + i <- NULL + fits <- NULL + error <- NULL + + if(missing(numCores)){ + num.Cores <- detectCores() + }else{ + num.Cores <- detectCores() + if(num.Cores<numCores){ + warning(paste('The number of cores is bigger than the number of detected cores. Using the number of detected cores',num.Cores,'instead.')) + }else{ + num.Cores <- numCores + } + + } + Molecule <- '' + + derivLme <- function(fit){ + #random slopes + + if(class(fit)=='lm'){ + beta.hat <- rep(fit$coefficients[2],length(unique(fit$model$time))) + return(beta.hat) + + }else if(class(fit)=='lme'){ + u <- unlist(fit$coefficients$random$all) + beta.hat <- fit$coefficients$fixed[2] + Zt <- fit$data$Zt[!duplicated(fit$data$Zt),]>0 + deriv.all <- beta.hat + rowSums(Zt%*%t(u)) + return(deriv.all) + } + } + + #penalized cubic + + derivLmeCubic <- function(fit){ + #random slopes + if(class(fit)=='lm'){ + beta.hat <- rep(fit$coefficients[2],length(unique(fit$model$time))) + return(beta.hat) + + }else if(class(fit)=='lme'){ + u <- unlist(fit$coefficients$random$all) + beta.hat <- fit$coefficients$fixed[2] + PZ <- fit$data$Zt[!duplicated(fit$data$Zt),] + PZ <-PZ^(1/3) + deriv.all <- beta.hat + rowSums((PZ*PZ)%*%(t(u)*3)) + return(deriv.all) + } + + } + + if(missing(knots)) + knots <-NULL + nMolecules <- NULL + nMolecules <- ncol(data) + + + lme <- nlme::lme + cl <- makeCluster(num.Cores,"SOCK") + clusterExport(cl, list('data','lm','try','class','unique','anova','drop.levels','pdDiag','pdIdent','time','sampleID','melt','dcast','predict','derivLme','knots','derivLmeCubic','lme','keepModels','basis','data','other.reshape'),envir=environment()) + + models <-list() + + + new.data <- parLapply(cl,1:nMolecules,fun = function(i){ + # new.data <- list() + # for(i in 1:nMolecules){ + + expr <- data[,i] + + dataM <- as.data.frame(other.reshape(Rep=sampleID,Time=time,Data=unlist(expr))) + dataM$all = rep(1, nrow(dataM)) + dataM$time = as.numeric(as.character(dataM$Time)) + dataM$Expr = as.numeric(as.character(dataM$Expr)) + + + #### CUBIC SPLINE BASIS #### + if(basis=="cubic"){ + dataM$Zt <- lmeSplines::smspline(~ time, data=dataM) + knots <- sort(unique(time))[2:(length(unique(time))-1)] + } + #### PENALIZED SPLINE BASIS##### + if(basis%in%c("p-spline","cubic p-spline")){ + + if(is.null(knots)){ + K <- max(6,min(floor(length(unique(dataM$time))/4),40)) + }else{ + K <- max(knots,6) + } + knots <- quantile(unique(dataM$time),seq(0,1,length=K+2))[-c(1,K+2)] + if(min(knots)<=min(dataM$time) | max(knots)>=max(dataM$time)) + stop(cat('Make sure the knots are within the time range',range(dataM$time)[1],'to',range(dataM$time)[2])) + PZ <- outer(dataM$time,knots,"-") + if(basis=="cubic p-spline") + PZ <- PZ^3 + PZ <- PZ *(PZ>0) + dataM$Zt <- PZ + + } + + + + if(deri){ + pred.spline = rep(NA,length(timePredict)) + }else{ + pred.spline =rep(NA,length(timePredict)) + pred.df <- data.frame(all=rep(1,length(timePredict)), time=timePredict) + pred.df$Zt = lmeSplines::approx.Z(dataM$Zt, dataM$time, timePredict) + + } + + + + #library(nlme) + fit0 <- NULL + fit0 <- try(lm(Expr ~ time, data=dataM )) + if(class(fit0) == 'try-error') { + models <- list() + error <- i + pred.spline <- rep(NA,length(timePredict)) + fits <- NA + }else{ + fit1 <- NULL + fit1 <- try(lme(Expr ~ time, data=dataM, random=list(all=pdIdent(~Zt - 1)), + na.action=na.exclude, control=lmeControl(opt = "optim"))) + pvalue <-1 + if(class(fit1) != 'try-error') { + + pvalue <- anova(fit1, fit0)$'p-value'[2] + + } + + if(pvalue <= 0.05){ + + fit2 <- NULL + fit2 <- try(lme(Expr ~ time, data=dataM, + random=list(all=pdIdent(~Zt - 1), Rep=pdIdent(~1)), + na.action=na.exclude, control=lmeControl(opt = "optim"))) + + if(class(fit2) != 'try-error') { # to prevent errors stopping the loop + + pvalue = anova(fit1, fit2)$'p-value'[2] + }else{ + pvalue=1 + } + + if(pvalue <= 0.05){ + fit3 <-NULL + fit3 <- try(lme(Expr ~ time, data=dataM, + random=list(all=pdIdent(~Zt - 1), Rep=pdDiag(~time)), + na.action=na.exclude, control=lmeControl(opt = "optim"))) + + if(class(fit3) != 'try-error') { # to prevent errors stopping the loop + pvalue = anova(fit2, fit3)$'p-value'[2] + + }else{ + pvalue=1 + } + if(pvalue <= 0.05){ + fits <- 3 + models<- fit3 + if(deri){ + if(basis=='p-spline') + pred.spline = derivLme(fit3) + if(basis=='cubic p-spline') + pred.spline = derivLmeCubic(fit3) + + }else{ + pred.spline = predict(fit3, newdata=pred.df, level=1, na.action=na.exclude) + } + }else{ # choose simpler model: fit2 + fits <- 2 + models <- fit2 + if(deri){ + if(basis=='p-spline') + pred.spline = derivLme(fit2) + if(basis=='cubic p-spline') + pred.spline = derivLmeCubic(fit2) + }else{ + pred.spline = predict(fit2, newdata=pred.df, level=1, na.action=na.exclude) + } + } + + }else{ + models <- fit1 + fits <- 1 + if(deri){ + if(basis=='p-spline') + pred.spline = derivLme(fit1) + if(basis=='cubic p-spline') + pred.spline = derivLmeCubic(fit1) + }else{ + pred.spline = predict(fit1, newdata=pred.df, level=1, na.action=na.exclude) + } + } + }else{ + + models <- fit0 + fits <-0 + if(deri){ + pred.spline = rep(fit0$coefficients[2],length(unique(dataM$time))) + }else{ + + pred.spline = predict(fit0, newdata=pred.df, level=1, na.action=na.exclude) + } + } + } + if(!keepModels) + keepModels <- list() + + return(list(pred.spl=pred.spline,fit=fits,models=models,error=error,knots=knots)) + #new.data[[i]] <- list(pred.spl=pred.spline,fit=fits,models=models,error=error,knots=knots) + + }) + #} + stopCluster(cl) + knots <- sort(unique(as.vector((sapply(new.data,'[[','knots'))))) + pred.spl <- matrix(sapply(new.data,'[[','pred.spl'),nrow=nMolecules,byrow=T) + fits <- unlist(sapply(new.data,'[[','fit')) + error <- unlist(sapply(new.data,'[[','error')) + models <-list() + if(keepModels){ + models <- sapply(new.data,'[[','models') + + if(is.matrix(models)) + models <- sapply(new.data,'[','models') + } + + pred.spl = as.data.frame(pred.spl) + MolNames <- as.character(unlist(colnames(data))) + + if(is.null(MolNames)| sum(is.na(MolNames))>0) + MolNames <- 1:nrow(pred.spl) + if(nrow(pred.spl)==length(MolNames)) + rownames(pred.spl)<-MolNames + if(ncol(pred.spl)==length(timePredict)) + colnames(pred.spl) <- timePredict + error2 <- "All features were modelled" + if(length(error)>0){ + warning('The following features could not be fitted ',paste(MolNames[error],' ',sep='\n')) + error2 <- '' + error2 <- MolNames[error] + + } + + l <-new('lmmspline',predSpline=pred.spl,modelsUsed=fits,basis=basis,knots=knots,errorMolecules=error2,models=models, derivative=deri) + return(l) + +} + + +other.reshape <- function(Rep, Time, Data){ + lme.data<-NULL + lme.data <- data.frame(Time=Time,Rep=Rep,as.matrix(Data)) + lme.data$Time = factor(drop.levels(lme.data$Time)) + lme.data$Rep = factor(drop.levels(lme.data$Rep)) + melt.lme.data <-NULL + melt.lme.data <- melt(lme.data) + cast.lme.data <- NULL + cast.lme.data <- dcast(melt.lme.data, variable+ Rep ~ Time) + melt.lme.data2 <- NULL + melt.lme.data2 <- melt(data.frame(cast.lme.data)) + names(melt.lme.data2) <- c("Molecule", "Rep", "Time", "Expr") + melt.lme.data2$Time <- factor(gsub("^X", "", as.character(melt.lme.data2$Time))) + return(as.data.frame(melt.lme.data2)) +} + + + +#' \code{lmms} class a S4 superclass to extend \code{lmmspline} and \code{lmmsde} class. +#' +#' \code{lmms} class is a superclass for classes \code{lmmspline} and \code{lmmsde}. These classes inherit common slots. +#' +#' @slot basis An object of class \code{character} describing the basis used for modelling. +#' @slot knots An object of class \code{numeric}, describing the boundaries of the splines. If not defined or if basis='cubic' knots are automatically estimated using Ruppert 2002 or are the design points when using 'cubic'. +#' @slot errorMolecules Vector of class \code{character}, describing the molecules that could not be modelled. +#' +#' @name lmms-class +#' @rdname lmms-class +#' @exportClass lmms + + +setClass('lmms',slots=c(basis="character", knots="numeric",errorMolecules="character")) + +#' \code{lmmspline} class a S4 class that extends \code{lmms} class. +#' +#' \code{lmmspline} class inherits from class \code{lmms} and extends it with three further slots: \code{predSpline}, \code{modelsUsed}, \code{models}. The class is returned when applying \code{\link{lmmSpline}} method. +#' +#' @slot predSpline A \code{data.frame} returning the fitted values for the time points of interest. +#' @slot models A \code{list} of class \code{\link{lm}} or \code{\link{lme}} containing the models for every molecule +#' @slot modelsUsed A \code{list} of class \code{lm} or \code{lme}, containing the models used to model the particular feature of interest. +#' @slot derivative A \code{logical} value indicating if the derivative was calculated. +#' +#' +#' @name lmmspline-class +#' @rdname lmmspline-class +#' @exportClass lmmspline + +setClass("lmmspline",slots= c(predSpline="data.frame", modelsUsed="numeric",models="list",derivative='logical'),contains='lmms') + + +#' Predicts fitted values of an \code{lmmspline} Object +#' +#' Predicts the fitted values of an \code{lmmspline} object for time points of interest. +#' +#' @importFrom parallel parLapply +#' @importFrom lmeSplines approx.Z +#' @param object an object inheriting from class \code{lmmspline}. +#' @param timePredict an optional \code{numeric} vector. Vector of time points to predict fitted values. If \code{missing} uses design points. +#' @param numCores alternative \code{numeric} value indicating the number of CPU cores to be used for parallelization. By default estimated automatically. +#' @param ... ignored. +#' @return \code{matrix} containing predicted values for the requested time points from argument \code{timePredict}. +#' @examples +#' \dontrun{ +#' data(kidneySimTimeGroup) +#' G1 <- which(kidneySimTimeGroup$group=="G1") +#' testLMMSpline<- lmmSpline(data=kidneySimTimeGroup$data[G1,], +#' time=kidneySimTimeGroup$time[G1], +#' sampleID=kidneySimTimeGroup$sampleID[G1],keepModels=T) +#' mat.predict <- predict(testLMMSpline, timePredict=c(seq(1,4, by=0.5)))} + +#' @export +predict.lmmspline<- function(object, timePredict, numCores, ...){ + + if(missing(timePredict)){ + return(object@pred.spline) + }else{ + + + + models <- object@models + + if(length(models)==0) + stop('You will need to keep the models to predict time points.') + cl <-sapply(models,class) + i <- which(cl=="lme")[1] + if(length(i)>0){ + lme.model <- models[[i]] + t <- na.omit(lme.model$data$time) + + pred.spline <- rep(NA,length(timePredict)) + pred.df <- data.frame(all=rep(1,length(timePredict)), time=timePredict) + pred.df$Zt = approx.Z(lme.model$data$Zt, lme.model$data$time, timePredict) + + }else{ + lme.model <- models[[i]] + i <- which(cl=="lm")[1] + t <- na.omit(lme.model$model$time) + pred.df <- data.frame(x=timePredict) + } + if(min(timePredict)<min(t) | max(timePredict)>max(t)) + stop(cat('Can only predict values within the time range',range(t)[1],'to',range(t)[2])) + + + if(missing(numCores)){ + num.Cores <- detectCores() + }else{ + num.Cores <- detectCores() + if(num.Cores<numCores){ + warning(paste('The number of cores is bigger than the number of detected cores. Using the number of detected cores',num.Cores,'instead.')) + }else{ + num.Cores <- numCores + } + + } + lme <- nlme::lme + cl <- makeCluster(num.Cores,"SOCK") + clusterExport(cl, list('models','pred.spline','pred.df','predict'),envir=environment()) + + new.data <- parLapply(cl,1:length(models),fun = function(i){ + # library(nlme) + cl <- class(models[[i]]) + pred.spline <- switch(cl, + lm=predict.lm(models[[i]], newdata=pred.df, level=1, na.action=na.exclude), + lme=predict(models[[i]], newdata=pred.df, level=1, na.action=na.exclude) + ) + return(pred.spline) + + }) + + stopCluster(cl) + pred.spl <- matrix(unlist(new.data),nrow=length(models),ncol=length(timePredict),byrow=T) + return(pred.spl)} +} \ No newline at end of file