Predicting class membership in mixture IRT for new data

[leahfeuerstahler]

There seems to be a bug when trying to predict group membership based on a mixture IRT model with user-provided data. A reproducible example is given below.

library(mirt)

below copied from multipleGroup() documentation

set.seed(12345) nitems <- 20 a1 <- matrix(.75, ncol=1, nrow=nitems) a2 <- matrix(1.25, ncol=1, nrow=nitems) d1 <- matrix(rnorm(nitems,0,1),ncol=1) d2 <- matrix(rnorm(nitems,0,1),ncol=1) itemtype <- rep('2PL', nrow(a1)) N1 <- 500 N2 <- N1*2 # second class twice as large

dataset1 <- simdata(a1, d1, N1, itemtype) dataset2 <- simdata(a2, d2, N2, itemtype) dat <- rbind(dataset1, dataset2)

models <- 'F1 = 1-20 CONSTRAIN = (1-20, a1)' mod_mix <- multipleGroup(dat, models, dentype = 'mixture-2', GenRandomPars = TRUE)

no problem classifying scores for calibration data

head(fscores(mod_mix, method = "classify"))

fails for classifying scores for user-provided data

head(fscores(mod_mix, response.pattern = dataset1, method = "classify"))

Error in object@ParObjects$pars[[extract.mirt(object, "nitems") + 1L]] :

subscript out of bounds

[philchalmers]

Hi Leah,

Thanks for the report; a logical flag was missing in the fscores() function and was being treating as a multi-group rather than mixture model. I now get the following output instead:

> head(fscores(mod_mix, response.pattern = dataset1[1,], method = "classify"))
        CLASS_1   CLASS_2
[1,] 0.04826089 0.9517391

and

> head(fscores(mod_mix, response.pattern = dataset1, method = "classify"))
        CLASS_1   CLASS_2
[1,] 0.04826089 0.9517391
[2,] 0.74229805 0.2577019
[3,] 0.02496487 0.9750351
[4,] 0.02929065 0.9707094
[5,] 0.07220029 0.9277997
[6,] 0.42417022 0.5758298

[leahfeuerstahler]

Thanks for the quick response! Much appreciated, as always.

On Mon, Oct 25, 2021 at 1:46 PM Phil Chalmers @.***> wrote:

Hi Leah,

Thanks for the report; a logical flag was missing in the fscores() function and was being treating as a multi-group rather than mixture model. I now get the following output instead:

head(fscores(mod_mix, response.pattern = dataset1[1,], method = "classify")) CLASS_1 CLASS_2 [1,] 0.04826089 0.9517391

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-- Leah Feuerstahler, PhD Assistant Professor of Psychology Fordham University 441 E Fordham Road Bronx, NY 10458 (718) 817-3788