R/SingleGroup-methods.R
anova-method.RdCompare 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.
# S4 method for SingleGroupClass
anova(
object,
object2,
...,
bounded = FALSE,
mix = 0.5,
frame = 1,
verbose = FALSE
)an object of class SingleGroupClass,
MultipleGroupClass, or MixedClass, reflecting the most constrained model fitted
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
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
proportion of chi-squared mixtures. Default is 0.5
(internal parameter not for standard use)
(deprecated argument)
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
# \donttest{
x <- mirt(Science, 1)
x2 <- mirt(Science, 2)
anova(x, x2)
#> AIC SABIC HQ BIC logLik X2 df p
#> x 3249.739 3262.512 3274.922 3313.279 -1608.870
#> x2 3241.938 3257.106 3271.843 3317.392 -1601.969 13.801 3 0.003
# compare three models sequentially (X2 not always meaningful)
x3 <- mirt(Science, 1, 'gpcm')
x4 <- mirt(Science, 1, 'nominal')
anova(x, x2, x3, x4)
#> AIC SABIC HQ BIC logLik X2 df p
#> x 3249.739 3262.512 3274.922 3313.279 -1608.870
#> x2 3241.938 3257.106 3271.843 3317.392 -1601.969 13.801 3 0.003
#> x3 3257.366 3270.139 3282.549 3320.906 -1612.683 -21.428 -3 NaN
#> x4 3264.910 3284.069 3302.684 3360.220 -1608.455 8.456 8 0.39
# in isolation
anova(x)
#> AIC SABIC HQ BIC logLik
#> x 3249.739 3262.512 3274.922 3313.279 -1608.87
# with priors on first model
model <- "Theta = 1-4
PRIOR = (1-4, a1, lnorm, 0, 10)"
xp <- mirt(Science, model)
anova(xp, x2)
#> AIC SABIC HQ BIC logLik logPost df
#> xp 3249.829 3262.602 3275.012 3313.369 -1608.915 -1622.881 NA
#> x2 3241.938 3257.106 3271.843 3317.392 -1601.969 -1601.969 3
anova(xp)
#> AIC SABIC HQ BIC logLik logPost
#> xp 3249.829 3262.602 3275.012 3313.369 -1608.915 -1622.881
# 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
#> AIC SABIC HQ BIC logLik X2 df p
#> mod 5337.610 5354.927 5356.263 5386.688 -2658.805
#> mod2 5339.587 5358.636 5360.106 5393.573 -2658.794 0.023 1 0.88
anova(mod, mod2, bounded = TRUE) #bounded
#> AIC SABIC HQ BIC logLik X2 df p
#> mod 5337.610 5354.927 5356.263 5386.688 -2658.805
#> mod2 5339.587 5358.636 5360.106 5393.573 -2658.794 0.023 1 0.44
# priors
model <- 'F = 1-5
PRIOR = (5, g, norm, -1, 1)'
mod1b <- mirt(dat, model, itemtype = c(rep('2PL', 4), '3PL'))
anova(mod1b)
#> AIC SABIC HQ BIC logLik logPost
#> mod1b 5339.571 5358.62 5360.089 5393.557 -2658.786 -2659.705
model2 <- 'F = 1-5
PRIOR = (1-5, g, norm, -1, 1)'
mod2b <- mirt(dat, model2, itemtype = '3PL')
anova(mod1b, mod2b)
#> AIC SABIC HQ BIC logLik logPost df
#> mod1b 5339.571 5358.620 5360.089 5393.557 -2658.786 -2659.705 NA
#> mod2b 5348.306 5374.282 5376.286 5421.923 -2659.153 -2664.008 4
# }