Processing Datasets and Creating Design Matrices for Rating Data

The function immer_proc_data processes datasets containing rating data into a dataset into a long format of pseudoitems (item \(\times\) raters).

The function immer_create_design_matrix_formula creates a design matrix for a processed dataset and a provided formula.

Usage

immer_proc_data(dat, pid=NULL, rater=NULL, weights=NULL, maxK=NULL)

immer_create_design_matrix_formula( itemtable, formulaA )

Arguments

dat: Datasets with integer item responses
pid: Vector with person identifiers
rater: Vector with rater identifiers
weights: Vector with sampling weights
maxK: Optional vector with maximum category per item
itemtable: Processed item table. The table must include the column item (an integer item identifier) and maxK (maximum number of categories per item). Optional columns are rater (an integer rater identifier),item_name and rater_name.
formulaA: An R formula. The facets item, step and rater are treated as numeric. However, numeric transformation can be applied for the step parameter by using the arguments item_num, step_num or rater_num in formulaA.

Value

The output of immer_proc_data is a list with several entries (selection)

dat2: Dataset containing pseudoitems
dat2.resp: Dataset containing response indicators for pseudoitems
dat2.NA: Dataset containing pseudoitems and missing responses coded as NA
dat: Original dataset
person.index: Person identifiers
rater.index: Rater identifiers
VV: Number of items
N: Number of persons
RR: Number of raters
dat2.ind.resp: Array containing indicators of pseudoitems and categories
ND: Number of person-rater interactions
itemtable: Information about processed data

The output of immer_create_design_matrix_formula is a list with several entries (selection)

A: design matrix
itemtable2: Processed item table

Examples

#############################################################################
# EXAMPLE 1: Processing rating data
#############################################################################

data(data.immer01a, package="immer")
dat <- data.immer01a

res <- immer::immer_proc_data( dat=dat[,paste0("k",1:5)], pid=dat$idstud,
             rater=dat$rater)
str(res, max.level=1)

if (FALSE) {
#############################################################################
# EXAMPLE 2: Creating several design matrices for rating data
#############################################################################

data(data.ratings1, package="sirt")
dat <- data.ratings1
resp <- dat[,-c(1,2)]
#- redefine the second and third item such that the maximum category score is 2
for (vv in c(2,3)){
    resp[ resp[,vv] >=2,vv ] <- 2
}

#--- process data
res0 <- immer::immer_proc_data( dat=resp, pid=dat$idstud,  rater=dat$rater)

#--- rating scale model
des1 <- immer::immer_create_design_matrix_formula( itemtable=res0$itemtable,
                formulaA=~ item + step )
des1$des

#--- partial scale model
des2 <- immer::immer_create_design_matrix_formula( itemtable=res0$itemtable,
                formulaA=~ item + item:step )
des2$des

#--- multi-facets Rasch model
des3 <- immer::immer_create_design_matrix_formula( itemtable=res0$itemtable,
                formulaA=~ item + item:step + rater )
des3$des

#--- polytomous model with quadratic step effects
des4 <- immer::immer_create_design_matrix_formula( itemtable=res0$itemtable,
                formulaA=~ item + item:I(step_num^2) )
des4$des
}