lca_cor: Calculate item level and aggregate learning

In soodoku/guess: Adjust Estimates of Learning for Guessing

Description

guesstimate

Usage

lca_cor(transmatrix = NULL, nodk_priors = c(0.3, 0.1, 0.1, 0.25),
  dk_priors = c(0.3, 0.1, 0.2, 0.05, 0.1, 0.1, 0.05, 0.25))

Arguments

transmatrix	transition matrix returned from multi_transmat
nodk_priors	Optional. Vector of length 4. Priors for the parameters for model that fits data without Don't Knows
dk_priors	Optional. Vector of length 8. Priors for the parameters for model that fits data with Don't Knows

Value

list with two items: parameter estimates and estimates of learning

Examples

# Without DK
pre_test <- data.frame(item1 = c(1, 0, 0, 1, 0), item2 = c(1, NA, 0, 1, 0)) 
pst_test <- pre_test + cbind(c(0, 1, 1, 0, 0), c(0, 1, 0, 0, 1))
transmatrix <- multi_transmat(pre_test, pst_test)
res <- lca_cor(transmatrix)

soodoku/guess documentation built on Dec. 31, 2021, 5:19 p.m.