Given an estimated model compute the test information.
testinfo(
x,
Theta,
degrees = NULL,
group = NULL,
individual = FALSE,
which.items = 1:extract.mirt(x, "nitems")
)Arguments
- x
an object of class 'SingleGroupClass', or an object of class 'MultipleGroupClass' if a suitable
groupinput were supplied- Theta
a matrix of latent trait values
- degrees
a vector of angles in degrees that are between 0 and 90. Only applicable when the input object is multidimensional
- group
group argument to pass to
extract.groupfunction. Required when the input object is a multiple-group model- individual
logical; return a data.frame of information traceline for each item?
- which.items
an integer vector indicating which items to include in the expected information function. Default uses all possible items
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
dat <- expand.table(deAyala)
(mirt(dat, 1, '2PL', pars = 'values'))
#> group item class name parnum value lbound ubound est
#> 1 all Item.1 dich a1 1 0.8510000 -Inf Inf TRUE
#> 2 all Item.1 dich d 2 2.3841111 -Inf Inf TRUE
#> 3 all Item.1 dich g 3 0.0000000 0e+00 1 FALSE
#> 4 all Item.1 dich u 4 1.0000000 0e+00 1 FALSE
#> 5 all Item.2 dich a1 5 0.8510000 -Inf Inf TRUE
#> 6 all Item.2 dich d 6 0.7257898 -Inf Inf TRUE
#> 7 all Item.2 dich g 7 0.0000000 0e+00 1 FALSE
#> 8 all Item.2 dich u 8 1.0000000 0e+00 1 FALSE
#> 9 all Item.3 dich a1 9 0.8510000 -Inf Inf TRUE
#> 10 all Item.3 dich d 10 0.3265895 -Inf Inf TRUE
#> 11 all Item.3 dich g 11 0.0000000 0e+00 1 FALSE
#> 12 all Item.3 dich u 12 1.0000000 0e+00 1 FALSE
#> 13 all Item.4 dich a1 13 0.8510000 -Inf Inf TRUE
#> 14 all Item.4 dich d 14 -0.3618092 -Inf Inf TRUE
#> 15 all Item.4 dich g 15 0.0000000 0e+00 1 FALSE
#> 16 all Item.4 dich u 16 1.0000000 0e+00 1 FALSE
#> 17 all Item.5 dich a1 17 0.8510000 -Inf Inf TRUE
#> 18 all Item.5 dich d 18 -0.5626501 -Inf Inf TRUE
#> 19 all Item.5 dich g 19 0.0000000 0e+00 1 FALSE
#> 20 all Item.5 dich u 20 1.0000000 0e+00 1 FALSE
#> 21 all GROUP GroupPars MEAN_1 21 0.0000000 -Inf Inf FALSE
#> 22 all GROUP GroupPars COV_11 22 1.0000000 1e-04 Inf FALSE
#> prior.type prior_1 prior_2
#> 1 none NaN NaN
#> 2 none NaN NaN
#> 3 none NaN NaN
#> 4 none NaN NaN
#> 5 none NaN NaN
#> 6 none NaN NaN
#> 7 none NaN NaN
#> 8 none NaN NaN
#> 9 none NaN NaN
#> 10 none NaN NaN
#> 11 none NaN NaN
#> 12 none NaN NaN
#> 13 none NaN NaN
#> 14 none NaN NaN
#> 15 none NaN NaN
#> 16 none NaN NaN
#> 17 none NaN NaN
#> 18 none NaN NaN
#> 19 none NaN NaN
#> 20 none NaN NaN
#> 21 none NaN NaN
#> 22 none NaN NaN
mod <- mirt(dat, 1, '2PL', constrain = list(c(1,5,9,13,17)))
Theta <- matrix(seq(-4,4,.01))
tinfo <- testinfo(mod, Theta)
plot(Theta, tinfo, type = 'l')
# \donttest{
# compare information loss between two tests
tinfo_smaller <- testinfo(mod, Theta, which.items = 3:5)
# removed item informations
plot(Theta, iteminfo(extract.item(mod, 1), Theta), type = 'l')
plot(Theta, iteminfo(extract.item(mod, 2), Theta), type = 'l')
# most loss of info around -1 when removing items 1 and 2; expected given item info functions
plot(Theta, tinfo_smaller - tinfo, type = 'l')
# }