R: Methods for sem Objects Fit Using the 'objectiveML',...

ML.methods {sem} | R Documentation

## 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`.

### See Also

`sem`, `objective.functions`, `modIndices.objectiveML`

[Package sem version 3.1-13 Index]