R/IRT.anova.R

In CDM: Cognitive Diagnosis Modeling

## File Name: IRT.anova.R
## File Version: 0.09

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# general ANOVA function
IRT.anova <- function( object, ... )
{
    cl <- match.call()
    cl2 <- paste(cl)[-1]
    if (length(list(object, ...)) !=2){
        stop("anova method can only be applied for comparison of two models.\n")
    }
    objects <- list(object, ...)
    model1a <- objects[[1]]
    model2a <- objects[[2]]
    model1 <- IRT.IC( model1a )
    model2 <- IRT.IC( model2a )

    dfr1 <- data.frame( "Model"=cl2[1],
            "loglike"=model1["loglike"],
            "Deviance"=-2*model1["loglike"] )
    dfr1$Npars <- model1["Npars"]
    dfr1$AIC <- model1["AIC"]
    dfr1$BIC <- model1["BIC"]
    dfr2 <- data.frame( "Model"=cl2[2],
            "loglike"=model2["loglike"],
            "Deviance"=-2*model2["loglike"] )
    dfr2$Npars <- model2["Npars"]
    dfr2$AIC <- model2["AIC"]
    dfr2$BIC <- model2["BIC"]

    dfr <- rbind( dfr1, dfr2 )
    dfr <- dfr[ order( dfr$Npars ), ]
    dfr$Chisq <- NA
    dfr$df <- NA
    dfr$p <- NA
    dfr[1, "Chisq"] <- dfr[1,"Deviance"] - dfr[2,"Deviance"]
    dfr[1, "df"] <- abs( dfr[1,"Npars"] - dfr[2,"Npars"] )
    dfr[1, "p" ] <- round( 1 - stats::pchisq( dfr[1,"Chisq"], df=dfr[1,"df"] ), 5 )
    for ( vv in 2:( ncol(dfr))){
        dfr[,vv] <- round( dfr[,vv], 5 )
    }
    rownames(dfr) <- NULL
    print(dfr)
    invisible(dfr)
}
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