R/pattRrep.fit.R

In prefmod: Utilities to Fit Paired Comparison Models for Preferences

pattRrep.fit<-function(obj, nitems,tpoints=1,formel=~1,elim=~1,resptype="rankingT",
         obj.names=NULL, ia=FALSE, iaT=FALSE, NItest=FALSE, pr.it=FALSE)
{

    call<-match.call()
    if (ia)
        cat("Warning:\n\tDependencies do not make sense for rankings! \n")
    if (tpoints<2)
        stop("no of timepoints incorrectly specified! if tpoints==1 use pattR.fit")

    ENV<-new.env()
    ENV$pr.it<-pr.it

    ENV$resptype<-"ratingT"

    nobj<-nitems  * tpoints
    ENV$Rnobj<-nobj   # number of ranked objects
    opt<-options()
    options("warn"=-1)

    ENV$nitems<-nitems
    ENV$tpoints<-tpoints

#### check data file
  if(is.character(obj)){                    ## datafilename supplied
        datafile    <-  obj
        if(file.access(datafile, mode=0) == 0){
           dat<-as.matrix(read.table(datafile,header=TRUE))  # datafile
        } else {
           stop("\ninput data file does not exist!\n")
        }
   } else if(is.data.frame(obj)){            ## data frame supplied
        dat<-as.matrix(obj)                                  # dataframe
        dat<-apply(dat,2,as.numeric)
   } else {
        stop("first argument must be either datafilename or dataframe")
   }

#### formula and variables
   formel.names<-attr(terms(as.formula(formel)),"term.labels")
   elim.names<-attr(terms(as.formula(elim)),"term.labels")
   covnames<-unique(c(formel.names,elim.names))

   varnames<-colnames(dat)

### variables rightmost columns in data
   if (ncol(dat)>nobj) {
        #covnames<-varnames[(nobj+1):ncol(dat)]
        #covs<-as.data.frame(dat[,(nobj+1):ncol(dat)])
        ## instead of the above rh 2011-05-13
        formel.names<-attr(terms(as.formula(formel)),"term.labels")
        formel.names<-unique(unlist(strsplit(formel.names,":"))) # 2011-08-31 remove interaction terms
        elim.names<-attr(terms(as.formula(elim)),"term.labels")
        elim.names<-unique(unlist(strsplit(elim.names,":")))     # 2011-08-31 remove interaction terms
        covnames<-unique(c(formel.names,elim.names))
        covs<-as.data.frame(dat[,covnames])
   } else {
        covs<-NULL
   }

   # check for proper ranks
   dups<-apply(dat[,1:(tpoints*nitems)],1,function(x) max(table(x))>tpoints)
   nodups<-!dups

   if (sum(dups)>0){
        norankslines<-(1:nrow(dat))[dups]
        cat("Warning:\n\timproper ranks in lines", norankslines, " - removed from data\n")
        dat<-dat[nodups,]
        if(!is.null(covs)) covs<-covs[nodups,,drop=FALSE]
   }


   # transform into PCs
   dat<-ifelse(is.na(dat),as.integer(99999),dat) # if removed with partial rankings: only comparisons between chosen
                                                 # then reverse results in -lambda
    ## reduce datamatrix
    # for each timepoint
    #dat.t<-NULL
    dat.t<-as.data.frame(diffsred(dat[,1:nitems],nitems))
    if(tpoints>1){
       for (t in 2:tpoints){
          from<-nitems*(t-1)+1
          to<-from+nitems-1
          dat.t<-cbind(dat.t,as.data.frame(diffsred(dat[,from:to],nitems)))
       }
    }
    ## replace 0 with NA
####browser()
    dat.t<-lapply(dat.t, function(x) ifelse(x==0,NA,x))
    ###dat<-dat.t
    pc.dat<-data.frame(dat.t)
    rm(dat.t)


   # check for NAs in subject covariates
   if(!is.null(covs)){
        covs<-as.data.frame(covs)
        colnames(covs)<-covnames
        NAs<-which(!complete.cases(covs))                  # check for NA
        if (length(NAs)>0){
             cat("Warning:\n\tsubject covariates: NAs in lines",NAs," - removed from data\n")
             notNAs<-which(complete.cases(covs))
             dat<-dat[notNAs,]
             # covs<-covs[notNAs,] ## replaced 20-08-09
             covs<-covs[notNAs,,drop=FALSE]
        }
   }

    ## option for obj.names added
    if (is.null(obj.names))
      ENV$obj.names<-varnames[1:nobj]
    else
      ENV$obj.names<-obj.names[1:nobj]
#######################################  end data, inits

## design matrix
    ENV$Y <- ifelse(Rpatternmat(nitems)>0,1,-1) # pattern matrix

   # "recursively" expand patternmatrix for timepoints
    np<-nrow(ENV$Y)                     # no patterns in Y
    npp<-np
    YL<-ENV$Y
    for (t in 1:(tpoints-1)){
      YL<-do.call("rbind", lapply(1:np, function(i) YL) ) # stacks YL np times
      YR<-expand.mat(ENV$Y,rep(npp,np))                       # repeats each line of Y np^t times
      YL<-cbind(YL,YR)
      npp<-npp*np
    }
    ENV$Y<-YL
    rm(YR,YL) # tidy up
    if(NItest)
      if(!any(is.na(pc.dat)))
         stop("Test for ignorable missing cannot be performed - no NA values!")


    # no undecided with rankings
    ENV$undec<-FALSE

    ENV$NItest<-NItest
    if(ENV$NItest) {
          if(formel!="~1" || elim != "~1"){
          covs<-NULL
          formel<-~1
          elim<-~1
          cat("\ncurrently no covariates fitted if NItest==TRUE !!\n")
       }
    }

    ENV$ia<-ia
    if (ia) {
       depL<-dependencies(nobj,ENV$Y)
       ENV$XI<-depL$d
       ilabels<-depL$label.intpars
       npars.ia<-nobj*(nobj-1)*(nobj-2)/2
    } else {
       ilabels<-NULL
       npars.ia<-0
    }

    ncomp<-choose(nitems,2)
    X<- -(ENV$Y[,1:ncomp] %*% pcdesign(nitems))[,-nitems]
    for (t in 2:tpoints){
       from<-ncomp*(t-1)+1
       to<-from+ncomp-1
       X<-cbind(X,-(ENV$Y[,from:to] %*% pcdesign(nitems))[,-nitems]  )
    }


    # dependence parameters between timepoints (AR(1))
    ENV$iaT<-iaT
    if (iaT) {
       npars.iaT<-ncomp*(tpoints-1)
       ENV$XIT<-do.call("cbind", lapply(1:npars.iaT,function(i) ENV$Y[,i]*ENV$Y[,i+ncomp]))
       ENV$iTlabels<-paste(paste("Comp",1:ncomp,sep=""),
                       paste("IT",rep(1:(tpoints-1),rep(ncomp,tpoints-1)),rep(2:(tpoints),rep(ncomp,tpoints-1)),sep=""),
                    sep=":")
    } else {
       ENV$iTlabels<-NULL
       npars.iaT<-0
    }



    #X<- ENV$Y %*% pcdesign(nobj)
    #X<- -X[,-nobj]                          # basic design matrix

    cList<-splitCovs(pc.dat,covs,formel,elim,ENV)     # split data according to subject covariates
    partsList<-gen.partsListR(nitems,cList,ENV)     # generate list for all subj covariate x miss values groups
    rm(cList)

    npar <- tpoints*(nitems-1) * ENV$ncovpar + ENV$undec*tpoints + npars.ia + npars.iaT
    if (ENV$NItest) npar<-tpoints*(nitems-1)*2 + ENV$undec*tpoints + npars.ia + npars.iaT

    #npar <- (nobj-1) * ENV$ncovpar + ENV$undec + npars.ia
    #if (ENV$NItest) npar<-(nobj-1)*2 + ENV$undec + npars.ia

    lambda<-rep(0,npar)
    ENV$iter<-0

    ## MAIN FITTING ROUTINE
    result<-nlm(loglik,lambda,X,nobj,partsList,ENV,hessian=TRUE,
        iterlim=1000)

    options(opt)

    ENV$nobj<-nobj
    ENV$ilabels<-ilabels

    envList<-mget(ls(ENV),envir=ENV)
    outputobj<-list(coefficients=result$estimate,
                    ll=ENV$ll,
                    fl=ENV$fl,
                    call=call,
                    result=result,
                    envList=envList,
                    partsList=partsList)
    class(outputobj) <- c("pattMod")                         #class: pattern model
    outputobj

}