lsasim (version 2.1.6)

response_gen: Generation of item response data using a rotated block design

Description

Creates a data frame of discrete item responses based on.

Usage

response_gen(
  subject,
  item,
  theta,
  a_par = NULL,
  b_par,
  c_par = NULL,
  d_par = NULL,
  item_no = NULL,
  ogive = "Logistic"
)

Arguments

subject

integer vector of test taker IDs.

item

integer vector of item IDs.

theta

numeric vector of latent test taker abilities.

a_par

numeric vector of item a parameters for each item.

b_par

numeric vector of item b parameters for each item.

c_par

numeric vector of item c parameters for each item.

d_par

list of numeric vectors of item threshold parameters for each item.

item_no

vector of item numbers the correspond the item parameters

ogive

can be "Normal" or "Logistic".

Details

subject and item must be equal lengths.

Generalized partial credit models (!is.null(d_par)) uses threshold parameterization.

Examples

set.seed(1234)
s_id <- c(1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, 4,
          4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7,
          7, 7, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10,
          10, 11, 11, 11, 11, 11, 11, 12,12, 12, 12, 12, 12, 12, 13, 13, 13, 13,
          13, 13, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 16,16, 16, 16,
          16, 16, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 19, 19,
          19, 19, 19, 19,19, 20, 20, 20, 20, 20, 20, 20)
i_id<- c(1, 4, 7, 10, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 1, 4, 7, 10, 3, 6, 9, 1, 4,
         7, 10, 3, 6, 9, 1, 4, 7, 10, 3, 6, 9, 2, 5, 8, 3, 6, 9, 1, 4, 7, 10, 2,
         5, 8, 2, 5, 8, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 1, 4, 7, 10, 3, 6, 9, 2,
         5, 8, 3, 6, 9, 1, 4, 7, 10, 3, 6, 9, 2, 5, 8, 3, 6, 9, 2, 5, 8, 3, 6, 9,
         2, 5, 8, 3, 6, 9, 2, 5, 8, 3, 6, 9, 1, 4, 7, 10, 2, 5, 8, 1, 4, 7, 10,
         2, 5, 8, 1, 4, 7, 10, 2, 5, 8, 1, 4, 7, 10, 3, 6, 9)
bb <- c(-1.72, -1.85, 0.98, 0.07, 1.00, 0.13, -0.43, -0.29, 0.86, 1.26)
aa <- c(1.28, 0.78, 0.98, 1.21, 0.83, 1.01, 0.92, 0.76, 0.88, 1.11)
cc <- rep(0, 10)
dd <- list(c(0, 0, -0.13, 0, -0.19, 0, 0, 0, 0, 0),
           c(0, 0,  0.13, 0,  0.19, 0, 0, 0, 0, 0))
response_gen(subject = s_id, item = i_id, theta = rnorm(20, 0, 1),
             b_par = bb, a_par = aa, c_par = cc, d_par = dd)