R: Extract model summaries and GOF statistics for model object

gof {lava} | R Documentation

## Extract model summaries and GOF statistics for model 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.

### 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, latent=FALSE, ...)

    ## S3 method for class 'lvmfit'
    logLik(object, p=coef(object),
                          data=model.frame(object),
                          model=object$estimator,
                          weights=Weights(object),
                          data2=object$data$data2,
                              ...)

    ## S3 method for class 'lvmfit'
    score(x, data=model.frame(x), p=pars(x), model=x$estimator,
                       weights=Weights(x), data2=x$data$data2, ...)

    ## S3 method for class 'lvmfit'
    information(x,p=pars(x),n=x$data$n,data=model.frame(x),
                       model=x$estimator,weights=Weights(x), data2=x$data$data2, ...)

### Arguments

`object` | Model object
`...` | Additional arguments to be passed to the low level functions
`x` | Model object
`p` | Parameter vector used to calculate statistics
`data` | Data.frame to use
`latent` | If TRUE predictions of latent variables are included in output
`data2` | Optional second data.frame (only for censored observations)
`weights` | 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, ...

### 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)))

[Package lava version 1.6.1 Index]