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ML.methods

Methods for sem Objects Fit Using the objectiveML, objectiveGLS, objectiveFIML, msemObjectiveML, and msemObjectiveGLS Objective Functions

Description

These functions are for objects fit by sem using the objectiveML (multivariate-normal full-information maximum-likelihood), link{objectiveFIML} (multivariate-normal full-information maximum-likihood in the presence of missing data), objectiveGLS (generalized least squares), and msemObjectiveML (multigroup multivariate-normal FIML) objective functions.

Usage

## S3 method for class 'objectiveML'
anova(object, model.2, robust=FALSE, ...)
## S3 method for class 'objectiveFIML'
anova(object, model.2, ...)

## S3 method for class 'objectiveML'
logLik(object, ...)
## S3 method for class 'objectiveFIML'
logLik(object, saturated=FALSE, 
    intercept="Intercept", iterlim=1000, ...)
## S3 method for class 'objectiveML'
deviance(object, ...)
## S3 method for class 'objectiveFIML'
deviance(object, saturated.logLik, ...)
## S3 method for class 'msemObjectiveML'
deviance(object, ...) 
## S3 method for class 'objectiveML'
AIC(object, ..., k)
## S3 method for class 'objectiveFIML'
AIC(object, saturated.logLik, ..., k)
## S3 method for class 'msemObjectiveML'
AIC(object, ..., k)
## S3 method for class 'objectiveML'
AICc(object, ...)
## S3 method for class 'objectiveFIML'
AICc(object, saturated.logLik, ...)
## S3 method for class 'msemObjectiveML'
AICc(object, ...)
## S3 method for class 'objectiveML'
BIC(object, ...)
## S3 method for class 'objectiveFIML'
BIC(object, saturated.logLik, ...)
## S3 method for class 'msemObjectiveML'
BIC(object, ...)
## S3 method for class 'objectiveML'
CAIC(object, ...)
## S3 method for class 'objectiveFIML'
CAIC(object, saturated.logLik, ...)

## S3 method for class 'objectiveML'
print(x, ...)
## S3 method for class 'objectiveGLS'
print(x, ...)
## S3 method for class 'objectiveFIML'
print(x, saturated=FALSE, ...)
## S3 method for class 'msemObjectiveML'
print(x, ...)
## S3 method for class 'msemObjectiveGLS'
print(x, ...)

## S3 method for class 'objectiveML'
summary(object, digits=getOption("digits"), 
    conf.level=.90, robust=FALSE, analytic.se=object$t <= 500, 
    fit.indices=c("GFI", "AGFI", "RMSEA", "NFI", "NNFI", "CFI", "RNI",
      "IFI", "SRMR", "AIC", "AICc", "BIC", "CAIC"), ...)
## S3 method for class 'objectiveFIML'
summary(object, digits=getOption("digits"), conf.level=.90,
    fit.indices=c("AIC", "AICc", "BIC", "CAIC"),
    saturated=FALSE, intercept="Intercept", saturated.logLik, ...)
## S3 method for class 'objectiveGLS'
summary(object, digits=getOption("digits"), conf.level=.90, 
    fit.indices=c("GFI", "AGFI", "RMSEA", "NFI", "NNFI", "CFI", "RNI", "IFI", "SRMR"),
    ...)
## S3 method for class 'msemObjectiveML'
summary(object, digits=getOption("digits"), 
    conf.level=.90, robust=FALSE, 
    analytic.se=object$t <= 500,
    fit.indices=c("GFI", "AGFI", "RMSEA", "NFI", "NNFI", "CFI", "RNI", 
      "IFI", "SRMR", "AIC", "AICc", "BIC"), ...)
## S3 method for class 'msemObjectiveGLS'
summary(object, digits=getOption("digits"), 
    conf.level=.90,
    fit.indices=c("GFI", "AGFI", "RMSEA", "NFI", "NNFI", 
      "CFI", "RNI", "IFI", "SRMR"), ...)

Arguments

`object, model.2, x`	an object inheriting from class `objectiveML`, `objectiveGLS`, `objectiveFIML`, `msemObjectiveML`, or `msemObjectiveGLS`.
`robust`	if `TRUE`, compute robust standard errors or test.
`fit.indices`	a character vector of “fit indices” to report; the allowable values are those given in Usage above, and vary by the objective function. If the argument isn't given then the fit indices reported are taken from the R `fit.indices` option; if this option isn't set, then only the AIC and BIC are reported for models fit with `objectiveML`, `objectiveFIML`, or `msemObjectiveML`, and no fit indices are reported for models fit with `objectiveGLS` or `msemObjectiveGLS`.
`k, ...`	ignored.
`digits`	digits to be printed.
`conf.level`	level for confidence interval for the RMSEA index (default is .9).
`analytic.se`	use analytic (as opposed to numeric) coefficient standard errors; default is `TRUE` where analytic standard errors are available if there are no more than 100 parameters in the model and `FALSE` otherwise.
`saturated`	if `TRUE` (the default is `FALSE`); compute the log-likelihood (and statistics that depend on it) for the saturated model when the objective function is FIML in the presence of missing data. This can be computationally costly.
`intercept`	the name of the intercept regressor in the raw data, to be used in calculating the saturated log-likelihood for the FIML estimator; the default is `"Intercept"`.
`saturated.logLik`	the log-likelihood for the saturated model, as returned by `logLik` with `saturated=TRUE`; if absent, this will be computed and the computation can be time-consuming.
`iterlim`	iteration limit used by the `nlm` optimizer to compute the saturated log-likelihood for the FIML estimator with missing data; defaults to `1000`.

Author(s)

John Fox jfox@mcmaster.ca and Jarrett Byrnes

References

See sem.