Compare nested models with likelihood-based statistics

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 class 'SingleGroupClass'
anova(
  object,
  object2,
  ...,
  bounded = FALSE,
  mix = 0.5,
  frame = 1,
  verbose = FALSE
)

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)

Value

a data.frame/mirt_df object

References

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

Examples


if (FALSE) { # \dontrun{
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)

} # }