test-13-grsmIRT.R
In mirt: Multidimensional Item Response Theory

expect_class <- function(x, class) expect_true(inherits(x, class))

test_that('grsmIRT', {
# Response data : num of response categories = 5

    R <-
      structure(c(4, 3, 3, 2, 2, 3, 2, 3, 4, 4, 3, 3, 2, 3, 2, 5, 4,
                  2, 2, 4, 2, 1, 4, 3, 4, 1, 4, 4, 4, 4, 4, 2, 1, 4, 2, 2, 1, 1,
                  3, 5, 4, 2, 2, 2, 3, 3, 5, 2, 2, 2, 2, 2, 5, 4, 4, 4, 2, 4, 3,
                  4, 4, 2, 3, 4, 4, 3, 4, 4, 4, 2, 4, 5, 4, 4, 1, 4, 3, 5, 4, 3,
                  3, 3, 4, 2, 2, 4, 5, 5, 2, 3, 2, 4, 1, 5, 4, 5, 1, 2, 3, 2, 5,
                  3, 3, 3, 4, 4, 2, 3, 4, 4, 4, 2, 3, 3, 4, 2, 4, 3, 3, 2, 4, 5,
                  1, 3, 1, 4, 4, 4, 4, 2, 1, 3, 4, 2, 1, 3, 5, 5, 2, 4, 1, 4, 2,
                  4, 4, 2, 1, 4, 3, 2, 4, 3, 4, 4, 4, 3, 2, 2, 4, 3, 3, 2, 4, 3,
                  4, 3, 4, 4, 3, 2, 4, 5, 2, 3, 1, 5, 4, 2, 4, 1, 2, 3, 3, 3, 1,
                  4, 5, 5, 2, 4, 2, 5, 2, 4, 4, 2, 1, 4, 3, 1, 5, 4, 1, 3, 5, 2,
                  2, 2, 2, 4, 3, 2, 3, 3, 4, 1, 4, 4, 4, 3, 4, 4, 2, 3, 1, 5, 2,
                  5, 4, 2, 2, 2, 2, 3, 1, 4, 4, 4, 2, NA, 1, 4, 2, 2, 3, 1, 1,
                  4, 3, 2), .Dim = c(50L, 5L),
                .Dimnames = list(c("3", "4", "5",
                                   "6", "7", "10", "11", "12", "17", "18", "19", "25", "26", "28",
                                   "29", "33", "34", "35", "39", "40", "42", "44", "45", "46", "47",
                                   "48", "50", "53", "56", "61", "63", "66", "67", "68", "71", "72",
                                   "73", "77", "80", "81", "82", "83", "84", "85", "87", "88", "89",
                                   "90", "91", "92"), c("TA1-1", "TA1-2", "TA1-3", "TA1-4", "TA1-5"
                                   )))

    coef2mat = function(cf) {
      stopifnot(is.list(cf))
      do.call(rbind, cf[1:(length(cf)-1)])
    }

    Qncat = function(R) {
      stopifnot(is.matrix(R))
      max(apply(R, 2, function(x) length(unique(x[!is.na(x)]))))
    }

    n.item <- ncol(R); ncat <- Qncat(R)

    fit.grsmIRT <- mirt(R, 1, itemtype="grsmIRT", verbose = FALSE)
    cfs <- unname(coef(fit.grsmIRT, as.data.frame=TRUE)[,1])
    expect_equal(cfs, c(0.9504355,1.400673,0.246286,-0.4280762,-1.777097,0,1.937205,1.400673,0.246286,-0.4280762,-1.777097,0.4881555,4.595605,1.400673,0.246286,-0.4280762,-1.777097,0.1717269,3.491201,1.400673,0.246286,-0.4280762,-1.777097,0.2391751,2.120059,1.400673,0.246286,-0.4280762,-1.777097,-0.01121605,0,1), tolerance = 1e-3)

    R[R==5] = 4 # when the num. of response categories is 4
    fit.grsmIRT <- mirt(R, 1, itemtype="grsmIRT", verbose=FALSE)
    cfs <- unname(coef(fit.grsmIRT, as.data.frame=TRUE)[,1])
    expect_equal(cfs, c(0.8199136,1.582405,0.3846789,-0.3105926,0,1.829724,1.582405,0.3846789,-0.3105926,0.4238192,4.399354,1.582405,0.3846789,-0.3105926,-0.001894622,3.623942,1.582405,0.3846789,-0.3105926,0.05004533,2.216279,1.582405,0.3846789,-0.3105926,-0.1542053,0,1), tolerance = 1e-3)


    R[R==1] = 2 # when the num. of response categories is 3
    n.item <- ncol(R); ncat <- Qncat(R)
    fit.grsmIRT <- mirt(R, 1, itemtype="grsmIRT", verbose = FALSE)
    cfs <- unname(coef(fit.grsmIRT, as.data.frame=TRUE)[,1])
    expect_equal(cfs, c(0.5536239,0.05664485,-0.6902105,0,1.653999,0.05664485,-0.6902105,0.7890457,8.838826,0.05664485,-0.6902105,0.4258847,3.555495,0.05664485,-0.6902105,0.4568785,1.972311,0.05664485,-0.6902105,0.1996144,0,1), tolerance = 1e-3)
})