MRCV (version 0.2-0)

anova.genloglin: Perform MRCV Model Comparison Tests

Description

The anova.genloglin method function offers second-order Rao-Scott and bootstrap adjusted model comparison and goodness-of-fit (Pearson and LRT) statistics appropriate for evaluating models estimated by the genloglin function.

Usage

## S3 method for class 'genloglin':
anova(object, model.HA = "saturated", type = "all", gof = TRUE, 
    print.status = TRUE, ...)

Arguments

object

An object of class 'genloglin' produced by the genloglin function.

model.HA

For the two MRCV case, a character string specifying one of the following models to be compared to the null model (where the null model should be nested within the alternative model): "homogeneous" (the homogeneous association model), "

type

A character string specifying one of the following approaches for performing adjusted model comparison tests: "boot" specifies a bootstrapping procedure; "rs2" specifies a Rao-Scott second-order adjustment; "all" spe

gof

A logical value indicating whether goodness-of-fit statistics should be calculated in addition to model comparison statistics. For model.HA = "saturated", model comparison statistics and goodness-of-fit statistics are identical, so only one

print.status

A logical value indicating whether bootstrap progress updates should be provided.

...

Additional arguments passed to or from other methods.

Value

• --- A list containing at least the following objects: original.arg and test.statistics. original.arg is a list containing the following objects:
  • model:
  {The original model specified in the call to the genloglin function.}
• model.HA:
• {The alternative model specified for the model.HA argument.}
• gof:
• {The original value supplied to the gof argument.}

code

rs.results

itemize

• lrt.gof.rs:

item

• lrt:
• lrt.gof:
• B.discard:
• p.chisq.boot:
• p.lrt.boot:
• p.lrt.gof.boot:
• df:
• p.value:
• df:
• p.value:
• df:
• p.value:
• df:
• p.value:

Details

The Rao-Scott approach applies a second-order adjustment to the model comparison statistic and its sampling distribution. Formulas are provided in Appendix A of Bilder and Loughin (2007). The bootstrap approach empirically estimates the sampling distribution of the model comparison statistic. Gange's (1995) method for generating correlated binary data is used for taking resamples under the null hypothesis. Bootstrap results are available only when boot = TRUE in the call to the genloglin function.

References

Bilder, C. and Loughin, T. (2007) Modeling association between two or more categorical variables that allow for multiple category choices. Communications in Statistics--Theory and Methods, 36, 433--451. Gange, S. (1995) Generating multivariate categorical variates using the iterative proportional fitting algorithm. The American Statistician, 49, 134--138.

Examples

## For examples see help(genloglin).