lagrange: Lagrange test for freeing parameters

In xzhaopsy/MIRT: Multidimensional Item Response Theory

Description

Lagrange (i.e., score) test to test whether parameters should be freed from a more constrained baseline model.

Usage

lagrange(mod, parnum, SE.type = "Oakes", type = "central", ...)

Arguments

mod	an estimated model
parnum	a vector, or list of vectors, containing one or more parameter locations/sets of locations to be tested. See objects returned from mod2values for the locations
SE.type	type of information matrix estimator to use. See mirt for further details
type	type of numerical algorithm passed to numerical_deriv to obtain the gradient terms
...	additional arguments to pass to mirt

Author(s)

Phil Chalmers [e-mail]

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

See Also

wald

Examples

## Not run: 
dat <- expand.table(LSAT7)
mod <- mirt(dat, 1, 'Rasch')
(values <- mod2values(mod))

#test all fixed slopes individually
parnum <- values$parnum[values$name == 'a1']
lagrange(mod, parnum)

# compare to LR test for first two slopes
mod2 <- mirt(dat, 'F = 1-5
                   FREE = (1, a1)', 'Rasch')
coef(mod2, simplify=TRUE)$items
anova(mod, mod2)

mod2 <- mirt(dat, 'F = 1-5
                   FREE = (2, a1)', 'Rasch')
coef(mod2, simplify=TRUE)$items
anova(mod, mod2)

mod2 <- mirt(dat, 'F = 1-5
                   FREE = (3, a1)', 'Rasch')
coef(mod2, simplify=TRUE)$items
anova(mod, mod2)

# test slopes first two slopes and last three slopes jointly
lagrange(mod, list(parnum[1:2], parnum[3:5]))

# test all 5 slopes and first + last jointly
lagrange(mod, list(parnum[1:5], parnum[c(1, 5)]))


## End(Not run)

xzhaopsy/MIRT documentation built on May 29, 2019, 12:42 p.m.