inst/examples/example-fit.R

In ItemResponseTrees: IR-Tree Modeling in 'mirt', 'Mplus', or 'TAM'

\donttest{
# Running these examples may take a while

data("jackson")
df1 <- jackson[1:456, paste0("C", 1:5)]
df2 <- jackson[1:456, c(paste0("C", 1:5), paste0("E", 1:5))]

irtree_create_template(df1)

# Graded Response Model ---------------------------------------------------

m1 <- "
IRT:
t BY C1@1, C2@1, C3@1, C4@1, C5@1;

Class:
GRM
"

model1 <- irtree_model(m1)

fit1 <- fit(model1, data = df1)

glance(fit1)
tidy(fit1, par_type = "difficulty")
augment(fit1)

# IR-Tree Models ----------------------------------------------------------

##### IR-tree model for 1 target trait #####

m2 <- "
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 C1@1, C2@1, C3@1, C4@1, C5@1;
e BY C1@1, C2@1, C3@1, C4@1, C5@1;
m BY C1@1, C2@1, C3@1, C4@1, C5@1;

Class:
Tree
"

model2 <- irtree_model(m2)

# See ?mirt::mirt for details on method argument
fit2 <- fit(model2, data = df1, control = control_mirt(method = "MHRM"))

##### IR-tree model for 2 target traits #####

m3 <- "
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:
t1 BY C1@1, C2@1, C3@1, C4@1, C5@1;
t2 BY E1@1, E2@1, E3@1, E4@1, E5@1;
e  BY C1@1, C2@1, C3@1, C4@1, C5@1, E1@1, E2@1, E3@1, E4@1, E5@1;
m  BY C1@1, C2@1, C3@1, C4@1, C5@1, E1@1, E2@1, E3@1, E4@1, E5@1;

Class:
Tree

Constraints:
t = t1 | t2
"

model3 <- irtree_model(m3)

fit3 <- fit(model3, data = df2, control = control_mirt(method = "MHRM"))

##### IR-tree model constrained to Steps Model #####

m4 <- "
Equations:
1 = (1-a1)
2 = a1*(1-a2)
3 = a1*a2*(1-a3)
4 = a1*a2*a3*(1-a4)
5 = a1*a2*a3*a4

IRT:
a1 BY C1@1, C2@1, C3@1, C4@1, C5@1;
a2 BY C1@1, C2@1, C3@1, C4@1, C5@1;
a3 BY C1@1, C2@1, C3@1, C4@1, C5@1;
a4 BY C1@1, C2@1, C3@1, C4@1, C5@1;

Class:
Tree

Constraints:
a1 = a2
a1 = a3
a1 = a4
"

model4 <- irtree_model(m4)

fit4 <- fit(model4, data = df1)

# Partial Credit Model ----------------------------------------------------

##### Ordinary PCM #####

m5 <- "
IRT:
t BY C1@1, C2@1, C3@1, C4@1, C5@1;

Weights:
t = c(0, 1, 2, 3, 4)

Class:
PCM
"

model5 <- irtree_model(m5)

fit5 <- fit(model5, data = df1 - 1, engine = "tam")

##### Multidimensional PCM with constraints #####

m6 <- "
IRT:
t1 BY C1@1, C2@1, C3@1, C4@1, C5@1;
t2 BY E1@1, E2@1, E3@1, E4@1, E5@1;
e  BY C1@1, C2@1, C3@1, C4@1, C5@1, E1@1, E2@1, E3@1, E4@1, E5@1;
m  BY C1@1, C2@1, C3@1, C4@1, C5@1, E1@1, E2@1, E3@1, E4@1, E5@1;

Weights:
t = c(0, 1, 2, 3, 4)
e = c(1, 0, 0, 0, 1)
m = c(0, 0, 1, 0, 0)

Class:
PCM

Constraints:
t = t1 | t2
"

model6 <- irtree_model(m6)

fit6 <- fit(model6, data = df2 - 1, engine = "tam",
            control = control_tam(control = list(snodes = 1234)))
}