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ItemResponseTrees

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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.

<img src="tools/ecn-model.png" width="80%" style="border:0px;display: block; margin-left: auto; margin-right: auto;" />

Installation

You can install the released version of ItemResponseTrees from CRAN with:

install.packages("ItemResponseTrees")

And the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("hplieninger/ItemResponseTrees")

Example

The IR-tree model depicted above can be fit as follows. For more details, see the vignette and ?irtree_model.

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)