| boot.LR {mirt} | R Documentation |
Given two fitted models, compute a parametric bootstrap test to determine whether
the less restrictive models fits significantly better than the more restricted model.
Note that this hypothesis test also works when prior parameter distributions are included for
either model. Function can be run in parallel after using a stuitable mirtCluster
definition.
boot.LR(mod, mod2, R = 1000)
mod |
an estimated model object |
mod2 |
an estimated model object |
R |
number of parametric bootstraps to use. |
a p-value evaluating whether the more restrictive model fits significantly worse than the less restrictive model
Phil Chalmers rphilip.chalmers@gmail.com
## Not run: #standard dat <- expand.table(LSAT7) mod1 <- mirt(dat, 1) mod2 <- mirt(dat, 1, '3PL') # standard LR test anova(mod1, mod2) # boostrap LR test (run in parallel to save time) mirtCluster() boot.LR(mod1, mod2, R=200) ## End(Not run)