endm | R Documentation |
Knowledge structures and 200 artificial responses to four problems are used to illustrate parameter estimation in Heller and Wickelmaier (2013).
data(endm)
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.
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")}
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")
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