immer_proc_data: Processing Datasets and Creating Design Matrices for Rating...

In alexanderrobitzsch/immer: Item Response Models for Multiple Ratings

Processing Datasets and Creating Design Matrices for Rating Data

Description

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)

## Not run: 
#############################################################################
# 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

## End(Not run)

alexanderrobitzsch/immer documentation built on March 27, 2024, 5:48 a.m.