endm {pks} | 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. 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")