R/fitNormDistr.R

In snjy9182/smt-beta: Statistical analysis of single molecule trajectories

## fitNormDistr-methods
##
##
###############################################################################
##' @name fitNormDistr
##' @aliases fitNormDistr
##' @title fitNormDistr
##' @rdname fitNormDistr-methods
##' @docType methods
##' @description fit normal distributions to diffusion coefficient caclulated 
##' by Dcoef method and saves seed state as a attribute of the result
##'
##' @usage
##' fitNormDistr(dcoef,components=NULL,log.transform=FALSE,binwidth=NULL,
##' combine.plot=FALSE,output=FALSE, proportion=NULL, means=NULL,
##' sd=NULL, constrain=FALSE)
##' @param dcoef diffusion coefficient calculated from Dcoef().
##' @param components parameter specifying the number of components to fit. If 
##' NULL (default), a components analysis is done to determine the most likely 
##' components and this number is then used for subsequent analysis.
##' @param log.transform logical indicate if log10 transformation is needed, 
##' default F.
##' @param binwidth binwidth for the combined plot. If NULL (default), will 
##' automatic assign binwidth.
##' @param combine.plot Logical indicate if all the plot should be combined 
##' into one, with same scale (/same axises breaks), same color theme, and same
##'  bin size for comparison.
##' @param output logical indicate if output file should be generated.
##' @param proportion numeric vector with estimates of each component's 
##' proportion of the whole data.
##' @param means numeric vector with estimates of mean(mu) values for each 
##' component.
##' @param sd numeric vector with estimates of standard deviation(sigma)
##'  values for each component.
##' @param constrain logical indicate if mean and std deviation are set to the 
##' given value. This will not work for the unimodal distribution.
##' @details
##' Components analysis uses the likelihood ratio test (LRT) to assess the 
##' number of mixture components.
##' Bad Random seed generation may cause normalmixEM to crash. Using another 
##' seed will solve the issue.
##' 
##' Note: Ensure that a random number generator seed has been manually set! 
##' The seed is stored as an attribute of the returned object of fitNormDistr()
##'  and using the same seed makes results repeatable (see examples).
##' 
##' @return
##' \describe{
##' \item{proportions}{The proportions of mixing components.}
##' \item{mean}{The Means of the components.}
##' \item{sd}{The Standard Deviations (SD) of components if not log 
##' transformed; if log transformed, it is then interpreted as Coefficient of 
##' Variation (CV).}
##' \item{loglik}{The log likelihood, useful for compare different fitting 
##' result, the bigger the better fit.}
##' }
##' 
##' @examples
##' # compare folders
##' folder1=system.file("extdata","SWR1",package="sojourner")
##' folder2=system.file("extdata","HTZ1",package="sojourner")
##' trackll=compareFolder(folders=c(folder1,folder2), input=3)
##' MSD=msd(trackll=trackll)
##' dcoef=Dcoef(MSD,dt=6,plot=TRUE,output=FALSE)
##' 
##' # set unique seed (use any number)
##' set.seed(123)
##' 
##' # fit dcoef (function automatically saves seed state as an attribute of 
##' # the result)
##' a=fitNormDistr(dcoef,components=NULL,log.transform=FALSE,
##'                combine.plot=FALSE, output=FALSE)
##' 
##' # to repeat results of 'a', load seed attribute of a into RNG state
##' .Random.seed=attr(a,"seed")
##' # or, reset the seed with same unique number
##' # set.seed(123)
##' 
##' b=fitNormDistr(dcoef,components=NULL,log.transform=FALSE,
##' combine.plot=FALSE,output=FALSE)
##' 
##' # if 'a' and 'b' are the same
##' mapply(identical,a[[1]],b[[1]])
##' 
##' #try with log transformation
##' set.seed(234)
##' c=fitNormDistr(dcoef,components=2,log.transform=TRUE,combine.plot=FALSE,
##'                output=FALSE)
##' 
##' # trying with some parameters provided(this will be applied to all dcoef 
##' # results). 
##' # with constrain = FALSE, this will be used as the starting values for the 
##' # EM-algorithm
##' # normally we should deal with only one dataset when working with 
##' # constrains, since it will apply to all of them.
##' folder3=system.file("extdata","HSF", package="sojourner")
##' trackll=compareFolder(c(folder3),input=3)
##' MSD=msd(trackll=trackll)
##' dcoef=Dcoef(MSD,dt=6,plot=TRUE,output=FALSE)
##' 
##' # try with constrain=TRUE, the values will be forced to equal the provided 
##' # ones.
##' set.seed(345)
##' e=fitNormDistr(dcoef,means=c(0.3,0.5), constrain=TRUE)

##' @export fitNormDistr
###############################################################################
# fit normal distribution to diffusion coefficient
# 
# TO DO: combine.plot=TRUE requires additional testing

#function that deals with one-component normal distribution fitting
.singlecompFit=function(data, mean=NULL, sd=NULL, constrain=FALSE){
    if (constrain == TRUE){warning(
        "single component fitting cannot be constrained.")}
    #    if(!is.null(mean) & !is.null(sd)){stop("Please provide only one of 
    #    the mean or sd constraints.")}
    #    fixlist = list(c(mean=mean, sd=sd))
    #    fit.info = fitdist(data,"norm", fix.arg=list(mean=mean))
    #} else{fit.info = fitdist(data=data, distr="norm")}
    #This dummy matrix is used to make it work out with the gg.mixEM function 
    #in Plotting.Helpers.R
    fit.info = fitdist(data=data, distr="norm")
    dummy.posterior = matrix()
    colnames(dummy.posterior) = c("comp1")
    #a list with components similar to that of mixEM so that it can be used in 
    # gg.mixEM
    fit.output = list(x=as.vector(data), lambda = c(1), 
                      mu = fit.info$estimate[1], sigma = fit.info$sd[1],
                        loglik = fit.info$loglik, restarts=0, ft="normalmixEM",
                      posterior= dummy.posterior)
    return(list(fit.output))
}

#uses mclust package's mclustBootstrapLRT() function to estimate the number of 
# components in distribution
.getCompNum=function(data){
    cat("\ncomponents analysis\n")
    components.test=mclust::mclustBootstrapLRT(data,model = "V",verbose=TRUE)
    print(components.test)
    components.int=length(components.test$p.value)
    cat("\n\nmost likely components",components.int,
        "at significant level 0.05\n\n")
    return(components.int)
}

.getSummaryResult=function(result, log.transform){
    out=list(
        data.frame(proportion=result$lambda),

        #why returning it back from log?
        #data.frame(mean=if(log.transform) 10^(result$mu)
        #           else result$mu),
        data.frame(mean=result$mu),
        
        data.frame(sd=result$sigma),

        log.lik=result$loglik
    )
    
    out=list(out)
    out=t(do.call(cbind.data.frame,out))
    colnames(out) = NULL
    return(out)
}

.fitNormDistr=function(dcoef,components=NULL,log.transform=FALSE,binwidth=NULL,
                       combine.plot=FALSE,output=FALSE,proportion=NULL,
                       means=NULL,sd=NULL,constrain=FALSE){
    cat(paste("\nIMPORTANT: Ensure a seed has been manually set!",
              "See help docs for more info.\n"))
    name=names(dcoef)
    len=length(dcoef)
    
    #parameters that help initial settings.
    params = list(proportion, means, sd)

    mixmdl.lst=list()
    length(mixmdl.lst)=len
    names(mixmdl.lst)=name

    # store the standard error, currently only for components more than 2
    
    ## This part deals with input related errors
    #collection of parameter vector lengths
    param.len = lapply(params, length)
    param.len=param.len[param.len!=0]
    if(!is.null(components) & (any(param.len != components))){
        stop(paste("Invalid parameter size: number of components",
                   "and parameter vector length does not match"))
    }

    for (i in seq_len(len)){
        data=dcoef[[name[i]]][,"slope"]

        # log transformation
        if (log.transform == TRUE) {
            data=log10(data)
            data=data[!is.na(data)]
        }
        
        components.int=0
        #components test
        if (is.null(components)&length(param.len) == 0){
            components.int = .getCompNum(data)
        }else{
            if(length(param.len) == 0){
                components.int=components
            }else if(!all(as.vector(param.len) == param.len[[1]])){
                stop("Parameter vector lengths do not match")
            }
            if(is.null(components)){
                components.int=param.len[[1]]
            } else{components.int=components}
        }
        
        
        if (components.int == 1){ #The single component is a special case here 
            # since it cannot be a mixEM class object
            if(constrain==TRUE){
                oneComp = .singlecompFit(data=data, mean=means, sd=sd, 
                                         constrain=TRUE)
            } else {oneComp=.singlecompFit(data=data)}
            fit.output=oneComp[[1]]
            plot.mixEM=gg.mixEM(fit.output,binwidth=binwidth,reorder=FALSE)
            mixmdl=fit.output
            mixmdl.lst[[i]] = fit.output
        } else{
            if(constrain == TRUE) {
                mixmdl=normalmixEM(data,k=components.int,maxit=1e4,
                                   epsilon=1e-10,lambda=proportion,
                                   sd.constr=sd,
                                   mean.constr=means)
            } else {
                mixmdl=normalmixEM(data,k=components.int,maxit=1e4,
                                   epsilon=1e-10,lambda=proportion,mu=means,
                                   sigma=sd)
            }
            # reorder also for plotting and for output result
            # this is recordered on mean value
            mixmdl=reorderEM(mixmdl)
            print(summary(mixmdl))
            plot.mixEM=gg.mixEM(mixmdl,binwidth=binwidth,reorder=TRUE)
            # approximate standard error using parametic bootstrap
            cat("\napproximating standard error by parametic bootstrap...\n\n")
            # file="/dev/null" # this only for mac
            mixmdl.lst[[i]]=mixmdl
        }
        suppressMessages(plot(plot.mixEM))
    }

        result.lst=list()
        length(result.lst)=len
        names(result.lst)=name

        for (i in seq_len(len)){
            result=.getSummaryResult(mixmdl.lst[[i]], log.transform)
            result.lst[[i]]=result
        }

    print(result.lst)

    if (combine.plot == TRUE){

        # same scale, same binwidth, same breaks
        ss=same.scale(mixmdl.lst)
        
        # auto binwidth, smaller of the two
        if (is.null(binwidth)) {
            binwidth.vec=vapply(mixmdl.lst,function(mdl){
                auto.binwidth(mdl$x)
            },double(1))

            binwidth=min(binwidth.vec)
            cat("\ncombined binwidth =",binwidth,"\n")
        }

        if(components.int == 1){reorder=FALSE}
        else{reorder=TRUE}
        plot.lst=lapply(mixmdl.lst,function(x){

            p=gg.mixEM(x,binwidth=binwidth,reorder=reorder)+
                # this only plot polygon but not histogram when ylim is added
                # +xlim(ss$scale.x)+ylim(ss$scale.y)
                coord_cartesian(xlim = ss$scale.x,ylim=ss$scale.y)+
                # makes integer breaks at the maxium n
                scale_x_continuous(breaks=scales::pretty_breaks(n=10))
            return(p)
        })

        # plot
        do.call(gridExtra::grid.arrange,plot.lst)
        # save
        cmb.plot=gridExtra::marrangeGrob(plot.lst, nrow=2, ncol=1)
    }

    # output
    if (output == TRUE){
        result.df=do.call(rbind.data.frame,result.lst)
        logTrans=""
        if(log.transform){logTrans=".logtrans"}
        fileName=paste("FitNormDistr-",
                    .timeStamp(name[1]),logTrans,"___.csv",sep="")
        cat("\nOutput FitNormDistr.\n")
        write.csv(file=fileName,result.df)
        
        # output plot
        if (combine.plot == TRUE){

            fileName=paste("FitNormDistr-combinePlot-",
                            .timeStamp(name[1]),logTrans,"___.pdf",sep="")
            cat("\nOutput FitNormDistr plot.\n")

            # gridExtra::marrangeGrob non-interactive use, multipage pdf
            # width=NULL,height=NULL default to graphic device size
            ggsave(filename=fileName,cmb.plot,width=8,height=8)
        }
    }
    # use invisible() so the user would not be overwhelmed by the numbers
    # at the same time programmers can assign the value and use it
    return(invisible(mixmdl.lst))
}


fitNormDistr=function(dcoef,components=NULL,log.transform=FALSE,binwidth=NULL,
                      combine.plot=FALSE,output=FALSE, proportion=NULL, 
                      means=NULL, sd=NULL, constrain=FALSE){
    
    # collects current seed (recommended that a unique seed is set beforehand, 
    # e.g. set.seed(123))
    my_seed=.Random.seed
    
    result = .fitNormDistr(dcoef=dcoef,components=components,
                           log.transform=log.transform,binwidth=binwidth,
                           combine.plot=combine.plot,output=output,
                            proportion=proportion,means=means,sd=sd,
                           constrain=constrain)
    
    # saves seed as attribute of result
    attr(result,"seed")=my_seed
    
    return(result)
}


# Comment:
# the calculation of means for the bootstrapped sample seems to be directly
# using fitted posterior distributions, rather than fitted 100 times.


# TODO: 
# constrain for the single component case should be implemented.