Item response tree (IR-tree) models like the one depicted below are a class of item response theory (IRT) models that assume that the responses to polytomous items can best be explained by multiple psychological processes (e.g., Böckenholt, 2012; Plieninger, 2020). The package ItemResponseTrees allows to fit such IR-tree models in mirt, TAM, and Mplus (via MplusAutomation).
The package automates some of the hassle of IR-tree modeling by means of a consistent syntax. This allows new users to quickly adopt this model class, and this allows experienced users to fit many complex models effortlessly.
You can install the released version of ItemResponseTrees from CRAN with:
And the development version from GitHub with:
# install.packages("remotes") remotes::install_github("hplieninger/ItemResponseTrees")
The IR-tree model depicted above can be fit as follows. For more
details, see the
library("ItemResponseTrees") m1 <- " Equations: 1 = (1-m)*(1-t)*e 2 = (1-m)*(1-t)*(1-e) 3 = m 4 = (1-m)*t*(1-e) 5 = (1-m)*t*e IRT: t BY E1, E2, E3, E4, E5, E6, E7, E8, E9; e BY E1@1, E2@1, E3@1, E4@1, E5@1, E6@1, E7@1, E8@1, E9@1; m BY E1@1, E2@1, E3@1, E4@1, E5@1, E6@1, E7@1, E8@1, E9@1; Class: Tree " model1 <- irtree_model(m1) fit1 <- fit(model1, data = jackson[, paste0("E", 1:9)]) glance( fit1) tidy( fit1, par_type = "difficulty") augment(fit1)
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