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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gof.R
\name{gof}
\alias{gof}
\alias{gof.lvmfit}
\alias{moments}
\alias{moments.lvm}
\alias{information}
\alias{information.lvmfit}
\alias{score}
\alias{score.lvmfit}
\alias{logLik.lvmfit}
\title{Extract model summaries and GOF statistics for model object}
\usage{
gof(object, ...)
\method{gof}{lvmfit}(object, chisq=FALSE, level=0.90, rmsea.threshold=0.05,all=FALSE,...)
moments(x,...)
\method{moments}{lvm}(x, p, debug=FALSE, conditional=FALSE, data=NULL, latent=FALSE, ...)
\method{logLik}{lvmfit}(object, p=coef(object),
data=model.frame(object),
model=object$estimator,
weights=Weights(object),
data2=object$data$data2,
...)
\method{score}{lvmfit}(x, data=model.frame(x), p=pars(x), model=x$estimator,
weights=Weights(x), data2=x$data$data2, ...)
\method{information}{lvmfit}(x,p=pars(x),n=x$data$n,data=model.frame(x),
model=x$estimator,weights=Weights(x), data2=x$data$data2, ...)
}
\arguments{
\item{object}{Model object}
\item{\dots}{Additional arguments to be passed to the low level functions}
\item{x}{Model object}
\item{p}{Parameter vector used to calculate statistics}
\item{data}{Data.frame to use}
\item{latent}{If TRUE predictions of latent variables are included in output}
\item{data2}{Optional second data.frame (only for censored observations)}
\item{weights}{Optional weight matrix}
\item{n}{Number of observations}
\item{conditional}{If TRUE the conditional moments given the covariates are
calculated. Otherwise the joint moments are calculated}
\item{model}{String defining estimator, e.g. "gaussian" (see
\code{estimate})}
\item{debug}{Debugging only}
\item{chisq}{Boolean indicating whether to calculate chi-squared
goodness-of-fit (always TRUE for estimator='gaussian')}
\item{level}{Level of confidence limits for RMSEA}
\item{rmsea.threshold}{Which probability to calculate, Pr(RMSEA<rmsea.treshold)}
\item{all}{Calculate all (ad hoc) FIT indices: TLI, CFI, NFI, SRMR, ...}
}
\value{
A \code{htest}-object.
}
\description{
Calculates various GOF statistics for model object including global
chi-squared test statistic and AIC. Extract model-specific mean and variance
structure, residuals and various predicitions.
}
\examples{
m <- lvm(list(y~v1+v2+v3+v4,c(v1,v2,v3,v4)~x))
set.seed(1)
dd <- sim(m,1000)
e <- estimate(m, dd)
gof(e,all=TRUE,rmsea.threshold=0.05,level=0.9)
set.seed(1)
m <- lvm(list(c(y1,y2,y3)~u,y1~x)); latent(m) <- ~u
regression(m,c(y2,y3)~u) <- "b"
d <- sim(m,1000)
e <- estimate(m,d)
rsq(e)
##'
rr <- rsq(e,TRUE)
rr
estimate(rr,contrast=rbind(c(1,-1,0),c(1,0,-1),c(0,1,-1)))
}
\author{
Klaus K. Holst
}
\keyword{methods}
\keyword{models}
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