Nothing
FitARp <-
function(z,p,lag.max="default",MLEQ=FALSE)
{
stopifnot(length(z)>0, length(z)>length(p), length(p)>0)
is.wholenumber <-
function(x, tol = .Machine$double.eps^0.5) abs(x - round(x)) < tol
stopifnot(is.wholenumber(p), p>0)
n<-length(z)
if (lag.max=="default")
MaxLag <- min(300, ceiling(length(z)/5))
else
MaxLag <- lag.max
pvec <- sort(p)
pvec<-pvec[pvec>0]
if (length(pvec)==0)
pvec<-0
PMAX<-max(pvec)
if (length(p)==1 && PMAX!=0)
pvec <- 1:p
SubsetQ <- length(pvec)<PMAX
if (PMAX == 0) SubsetQ<-FALSE
mz <- mean(z)
y <- z
#get parameter estimates
if (MLEQ){
ans<-GetFitARpMLE(y,pvec)
FitMethod<-"MLE"
MeanMLEQ<-TRUE
}
else {
ans<-GetFitARpLS(y,pvec)
FitMethod<-"LS"
MeanMLEQ<-FALSE
}
phiHat<-ans$phiHat
res<-BackcastResidualsAR(y-mz, phiHat)
fits<-y-res
sigsq<-sum(res^2)/n
racf<-(acf(res, plot=FALSE, lag.max=MaxLag)$acf)[-1]
#covariance matrix via inverse Fisher information matrix
#sd of racf
if (SubsetQ){
varNames<-paste("phi(",pvec,")",sep="")
covHat<-solve(InformationMatrixARp(phiHat,pvec))/n
dimnames(covHat)<-list(varNames,varNames)
sdRacf<-sqrt(diag(VarianceRacfARp(phiHat,pvec,MaxLag,n)))
}
else {
if (PMAX>0) {
varNames<-paste("phi(",1:PMAX,")",sep="")
covHat<-SiddiquiMatrix(phiHat)/n
dimnames(covHat)<-list(varNames,varNames)
sdRacf<-sqrt(diag(VarianceRacfAR(phiHat,MaxLag,n)))
}
else {
varNames<-character(0)
covHat<-numeric(0)
sdRacf<-rep(1/sqrt(n),MaxLag)
}
}
if (SubsetQ) {
ModelTitle<-deparse(as.numeric(pvec),width.cutoff=180)
ModelTitle<-paste("ARp",substr(ModelTitle,2,nchar(ModelTitle)),sep="")
ModelTitle<-gsub(" ", "", ModelTitle)
}
else
ModelTitle<-paste("AR(",p,")",sep="")
#
LBQ<-LjungBoxTest(res, lag.max=MaxLag, k=length(pvec))
RacfMatrix<-matrix(c(racf,sdRacf),ncol=2)
dimnames(RacfMatrix)<-list(1:MaxLag, c("ra", "Sd(ra)"))
zetaHat<-ARToPacf(phiHat)
#
if (!MLEQ) { #for LS, report usual LS covariance matrix
covHat<-ans$covHat
covHat <- covHat[-1,-1,drop=FALSE] #remove intercept
dimnames(covHat)<-list(varNames,varNames)
}
ans<-list(loglikelihood=ans$loglikelihood,phiHat=phiHat,sigsqHat=sigsq,muHat=mz,covHat=covHat,zetaHat=zetaHat,
RacfMatrix=RacfMatrix,LjungBoxQ=LBQ,res=res,fits=fits,SubsetQ=SubsetQ,pvec=pvec,FitMethod=FitMethod,
MeanMLE=MeanMLEQ, ARModel="ARp", tsp=tsp(z),
call=match.call(),DataTitle=attr(z,"title"),ModelTitle=ModelTitle,z=z)
class(ans)<-"FitAR"
ans
}
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