endm: Responses and Knowledge Structures from Heller and...

In pks: Probabilistic Knowledge Structures

Responses and Knowledge Structures from Heller and Wickelmaier (2013)

Description

Knowledge structures and 200 artificial responses to four problems are used to illustrate parameter estimation in Heller and Wickelmaier (2013).

Usage

data(endm)

Format

A list consisting of three components:

K

a state-by-problem indicator matrix representing the true knowledge structure that underlies the model that generated the data.

K2

a slightly misspecified knowledge structure.

N.R

a named numeric vector. The names denote response patterns, the values denote their frequencies.

Source

Heller, J., & Wickelmaier, F. (2013). Minimum discrepancy estimation in probabilistic knowledge structures. Electronic Notes in Discrete Mathematics, 42, 49–56. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.endm.2013.05.145")}

Examples

data(endm)
endm$K    # true knowledge structure
endm$K2   # misspecified knowledge structure
endm$N.R  # response patterns

## Generate data from BLIM based on K
blim0 <- list(
     P.K = setNames(c(.1, .15, .15, .2, .2, .1, .1), as.pattern(endm$K)),
    beta = rep(.1, 4),
     eta = rep(.1, 4),
       K = endm$K,
  ntotal = 200)
class(blim0) <- "blim"
simulate(blim0)

## Fit BLIM based on K2
blim1 <- blim(endm$K2, endm$N.R, "MD")

pks documentation built on May 5, 2023, 3:08 p.m.