gof: Extract model summaries and GOF statistics for model object

In lava: Latent Variable Models

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.

Usage

gof(object, ...)

## S3 method for class 'lvmfit'
gof(object, chisq=FALSE, level=0.90, rmsea.threshold=0.05,all=FALSE,...)

moments(x,...)

## S3 method for class 'lvm'
moments(x, p, debug=FALSE, conditional=FALSE, data=NULL, ...)

## S3 method for class 'lvmfit'
logLik(object, p=coef(object),
                      data=model.frame(object),
                      model=object$estimator,
                      weight=Weight(object),
                      weight2=object$data$weight2,
                          ...)

## S3 method for class 'lvmfit'
score(x, data=model.frame(x), p=pars(x), model=x$estimator,
                   weight=Weight(x), weight2=x$data$weight2, ...)

## S3 method for class 'lvmfit'
information(x,p=pars(x),n=x$data$n,data=model.frame(x),
                   model=x$estimator,weight=Weight(x), weight2=x$data$weight2, ...)

Arguments

object	Model object
x	Model object
p	Parameter vector used to calculate statistics
data	Data.frame to use
weight2	Optional second data.frame (only for censored observations)
weight	Optional weight matrix
n	Number of observations
conditional	If TRUE the conditional moments given the covariates are calculated. Otherwise the joint moments are calculated
model	String defining estimator, e.g. "gaussian" (see estimate)
debug	Debugging only
chisq	Boolean indicating whether to calculate chi-squared goodness-of-fit (always TRUE for estimator='gaussian')
level	Level of confidence limits for RMSEA
rmsea.threshold	Which probability to calculate, Pr(RMSEA<rmsea.treshold)
all	Calculate all (ad hoc) FIT indices: TLI, CFI, NFI, SRMR, ...
...	Additional arguments to be passed to the low level functions

Value

A htest-object.

Author(s)

Klaus K. Holst

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)))

lava documentation built on May 2, 2019, 4:49 p.m.