# 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)}

}