Compare nested models with likelihood-based statistics
Compare nested models using likelihood ratio test (X2), Akaike Information Criterion (AIC), sample size adjusted AIC (AICc), Bayesian Information Criterion (BIC), Sample-Size Adjusted BIC (SABIC), and Hannan-Quinn (HQ) Criterion.
## S4 method for signature 'SingleGroupClass' anova(object, object2, bounded = FALSE, mix = 0.5, verbose = TRUE)
object |
an object of class |
object2 |
a second model estimated from any of the mirt package estimation methods |
bounded |
logical; are the two models comparing a bounded parameter (e.g., comparing a single
2PL and 3PL model with 1 df)? If |
mix |
proportion of chi-squared mixtures. Default is 0.5 |
verbose |
logical; print additional information to console? |
a data.frame/mirt_df object
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
## Not run:
x <- mirt(Science, 1)
x2 <- mirt(Science, 2)
anova(x, x2)
# in isolation
anova(x)
# 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)
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