I am curious whether there is an intuitive way in
ltm package in
R to display person's estimated latent trait after fitting the data to an IRT model.
install.packages("ltm"), one can quickly run a two-parameter IRT model with the built-in
WIRS data, which is a 6-item test with 1,005 persons:
library(ltm) data <- WIRS two_pl <- ltm(data ~ z1)
coef(two_pl) displays all the item parameters:
Dffclt Dscrmn Item 1 3.4011395 0.1534064 Item 2 -0.9421221 0.3676923 Item 3 0.8093853 1.7179970 Item 4 1.3689278 1.0101043 Item 5 0.4762685 2.0324137 Item 6 1.6804632 1.3745785
two_pl$coefficients one can also display
z1. I am not familiar with meaning of the former as there is none in the typical formation of 2PL equation, but
z1 is the discrimination parameter of items.
(Intercept) z1 Item 1 -0.5217566 0.1534064 Item 2 0.3464111 0.3676923 Item 3 -1.3905215 1.7179970 Item 4 -1.3827598 1.0101043 Item 5 -0.9679746 2.0324137 Item 6 -2.3099285 1.3745785
My question: is it possible to find the estimated
theta parameters for each person in
I have checked the documentation, while they have both
person.fit, there is no indication that one can pull out the data of person's latent level trait. In the
WIRS example, it should be a vector/list with 1,005 elements, but I am still not able to find anything like it after fitting the model.