library(eRm)
# list of 3 is returned (item combinations, fit rasch model and ppar)
data(ADL)
testthat::test_that("test_personsItems",{
testthat::expect_equal(length(exhaustiveRasch::test_personsItems(
items=1:5, dset=ADL, na.rm=TRUE, modelType="RM",
estimation_param= estimation_control(est="psychotools"))),
expected=3)})
# list of 3 is returned (item combinations, fit rasch model and ppar)
data(ADL)
firstrun <- exhaustiveRasch::test_personsItems(
items=1:5, dset=ADL, na.rm=TRUE, modelType="RM",
estimation_param= estimation_control(est="eRm"))
testthat::test_that("test_personsItems with pre-fit model
in the 'items' parameter",{
testthat::expect_equal(length(exhaustiveRasch::test_personsItems(
items=firstrun, dset=ADL, na.rm=TRUE, modelType="RM",
estimation_param= estimation_control(est="eRm"))),
expected=3)})
# empty list is returned
data(ADL)
testthat::test_that("test_personsItems",{
testthat::expect_equal(length(exhaustiveRasch::test_personsItems(
items=c(1,2,3,4,8), dset=ADL, na.rm=TRUE, modelType="RM",
estimation_param= estimation_control(est="pairwise"))),
expected=0)})
# list of 3 is returned (item combinations, fit rasch model and ppar)
data(ADL)
testthat::test_that("test_personsItems with gap_prop",{
testthat::expect_equal(length(exhaustiveRasch::test_personsItems(
items=1:5, dset=ADL, na.rm=TRUE, modelType="RM", gap_prop = 0.6,
estimation_param= estimation_control(est="eRm"))),
expected=3)})
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