augment.irtree_fit: Augment data with information from an irtree_fit object
In ItemResponseTrees: IR-Tree Modeling in 'mirt', 'Mplus', or 'TAM'

Description Usage Arguments Details Value See Also Examples

Augment accepts a model object and a dataset and adds information about each observation in the dataset, namely, predicted values in the .fitted column. New columns always begin with a . prefix to avoid overwriting columns in the original dataset.

1 2	## S3 method for class 'irtree_fit' augment(x = NULL, data = NULL, se_fit = TRUE, method = "EAP", ...)

`x`	object of class `irtree_fit` as returned from `fit()`.
`data`	Optional data frame that is returned together with the predicted values. Argument is not needed since the data are contained in the fitted object.
`se_fit`	Logical indicating whether standard errors for the fitted values should be returned as well.
`method`	This is passed to `mirt::fscores()` or `TAM:::IRT.factor.scores()` (as argument `type`) if applicable.
`...`	Additional arguments passed to `mirt::fscores()` or `TAM:::IRT.factor.scores()` if applicable.

Note that argument method is used only for engines mirt and TAM.

Returns a tibble with one row for each observation and one (two) additional columns for each latent variable if se_fit = FALSE (if se_fit = TRUE). The names of the new columns start with .fit (and .se.fit).

generics::augment()

data("jackson")
df1 <- jackson[1:234, paste0("C", 1:5)]
irtree_create_template(df1)
m1 <- "
IRT:
t BY C1@1, C2@1, C3@1, C4@1, C5@1;
Class:
GRM"
fit1 <- fit(irtree_model(m1), data = df1)

tidy(fit1, par_type = "difficulty")

glance(fit1)

augment(fit1)