Nothing
pattL.fit<-function(obj, nitems,formel=~1,elim=~1,resptype="rating",
obj.names=NULL, undec=TRUE, ia=FALSE, NItest=FALSE, pr.it=FALSE)
{
call<-match.call()
ENV<-new.env()
ENV$pr.it<-pr.it
ENV$resptype<-"rating"
nobj<-nitems
opt<-options()
options("warn"=-1)
#######################################
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")
}
varnames<-colnames(dat)
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
}
# for ratings: at least two items not NA
idx<-apply(dat[,1:nobj],1,function(x) sum(!is.na(x))>1)
dat<-dat[idx,]
dat<-as.data.frame(dat[,1:nobj])
if(!is.null(covs)){
covs<-as.data.frame(covs[idx,])
colnames(covs)<-covnames
NAs<-which(!complete.cases(covs)) # check for NA
if (length(NAs)>0){
cat("\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]
if(NItest)
if(!any(is.na(dat)))
stop("Test for ignorable missing cannot be performed - no NA values!")
datrng<-range(dat,na.rm=TRUE)
# reduce datamatrix and genenerate and reduce patternmatrix
dat<-as.data.frame(diffsred(dat,nobj))
ENV$Y <- Lpatternmat(datrng,nobj) # pattern matrix reduced
# only global undecided
if(undec)
ENV$U <- apply(ENV$Y,1,function(x) sum(x==0))
ENV$undec<-undec
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$label.intpars
npars.ia<-nobj*(nobj-1)*(nobj-2)/2
} else {
ilabels<-NULL
npars.ia<-0
}
X<- ENV$Y %*% pcdesign(nobj)
X<- -X[,-nobj] # basic design matrix
cList<-splitCovs(dat,covs,formel,elim,ENV) # split data according to subject covariates
partsList<-gen.partsList(nobj,cList,ENV) # generate list for all subj covariate x miss values groups
rm(cList)
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
}
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