ML.methods: Methods for sem Objects Fit Using the 'objectiveML',...
In sem3: Structural Equation Models

Description Usage Arguments Author(s) References See Also

These functions are for objects fit by sem using the objectiveML (multivariate-normal full-information maximum-likelihood) objectiveGLS (generalized least squares), and objectiveML (multigroup multivariate-normal FIML) objective functions.

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

## S3 method for class 'objectiveML'
logLik(object, ...)
## S3 method for class 'objectiveML'
deviance(object, ...)
## S3 method for class 'msemObjectiveML'
deviance(object, ...) 
## S3 method for class 'objectiveML'
AIC(object, ..., k)
## S3 method for class 'msemObjectiveML'
AIC(object, ..., k)
## S3 method for class 'objectiveML'
AICc(object, ...)
## S3 method for class 'msemObjectiveML'
AICc(object, ...)
## S3 method for class 'objectiveML'
BIC(object, ...)
## S3 method for class 'msemObjectiveML'
BIC(object, ...)
## S3 method for class 'objectiveML'
CAIC(object, ...)

## S3 method for class 'objectiveML'
print(x, ...)
## S3 method for class 'objectiveGLS'
print(x, ...)
## S3 method for class 'msemObjectiveML'
print(x, ...)
## S3 method for class 'msemObjectiveGLS'
print(x, ...)
## S3 method for class 'objectiveML'
summary(object, digits=5, conf.level=.90, robust=FALSE, analytic.se=object$t <= 500, ...)
## S3 method for class 'objectiveGLS'
summary(object, digits=5, conf.level=.90, robust=FALSE, ...)
## S3 method for class 'msemObjectiveML'
summary(object, digits=5, conf.level=.90, robust=FALSE, analytic.se=object$t <= 500, ...)
## S3 method for class 'msemObjectiveGLS'
summary(object, digits=5, conf.level=.90, robust=FALSE, ...)

`object, model.2, x`	an object inheriting from class `objectiveML`, `objectiveGLS`, or `msemObjectiveML`.
`robust`	if `TRUE`, compute robust standard errors or test.
`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` is there are no more than 100 parameters in the model and `FALSE` otherwise.