mirt (version 1.42)

anova-method: Compare nested models with likelihood-based statistics

Description

Compare nested models using likelihood ratio test (X2), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Sample-Size Adjusted BIC (SABIC), and Hannan-Quinn (HQ) Criterion. When given a sequence of objects, anova tests the models against one another in the order specified. Note that the object inputs should be ordered in terms of most constrained model to least constrained.

Usage

# S4 method for SingleGroupClass
anova(
  object,
  object2,
  ...,
  bounded = FALSE,
  mix = 0.5,
  frame = 1,
  verbose = FALSE
)

Value

a data.frame/mirt_df object

Arguments

object

an object of class SingleGroupClass, MultipleGroupClass, or MixedClass, reflecting the most constrained model fitted

object2

a second model estimated from any of the mirt package estimation methods

...

additional less constrained model objects to be compared sequentially to the previous model

bounded

logical; are the two models comparing a bounded parameter (e.g., comparing a single 2PL and 3PL model with 1 df)? If TRUE then a 50:50 mix of chi-squared distributions is used to obtain the p-value

mix

proportion of chi-squared mixtures. Default is 0.5

frame

(internal parameter not for standard use)

verbose

(deprecated argument)

References

Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. tools:::Rd_expr_doi("10.18637/jss.v048.i06")

Examples

if (FALSE) {
x <- mirt(Science, 1)
x2 <- mirt(Science, 2)
anova(x, x2)

# compare three models sequentially (X2 not always meaningful)
x3 <- mirt(Science, 1, 'gpcm')
x4 <- mirt(Science, 1, 'nominal')
anova(x, x2, x3, x4)

# in isolation
anova(x)

# with priors on first model
model <- "Theta = 1-4
          PRIOR = (1-4, a1, lnorm, 0, 10)"
xp <- mirt(Science, model)
anova(xp, x2)
anova(xp)

# bounded parameter
dat <- expand.table(LSAT7)
mod <- mirt(dat, 1)
mod2 <- mirt(dat, 1, itemtype = c(rep('2PL', 4), '3PL'))
anova(mod, mod2) #unbounded test
anova(mod, mod2, bounded = TRUE) #bounded

# priors
model <- 'F = 1-5
          PRIOR = (5, g, norm, -1, 1)'
mod1b <- mirt(dat, model, itemtype = c(rep('2PL', 4), '3PL'))
anova(mod1b)

model2 <- 'F = 1-5
          PRIOR = (1-5, g, norm, -1, 1)'
mod2b <- mirt(dat, model2, itemtype = '3PL')
anova(mod1b, mod2b)

}