Description Usage Arguments Value Examples
Calculate the predicted response distribution given the posterior median/mean of the item parameters and a matrix theta
of person parameters.
1 2 3 4 |
fit_sum |
List. A summary of theta and beta parameters as returned from |
theta |
Matrix. A matrix of person parameters for which the predictions should be made. |
betas |
Jx4 matrix with item parameters on four response dimensions
(middle, extreme, acquiescence, relevant trait defined by
|
beta_ARS_extreme |
only for |
measure |
Character vector that indicates whether the mean (default) or the median of the posterior distribution should be plotted. |
S |
number of latent processes to be measured |
N |
number of persons |
J |
number of items |
revItem |
vector of length J specifying reversed items (1=reversed, 0=regular) |
traitItem |
vector of length J specifying the underlying traits (e.g., indexed from 1...5). Standard: only a single trait is measured by all items. If the Big5 are measured, might be something like c(1,1,1,2,2,2,...,5,5,5,5) |
fitModel |
Character. Either |
Function returns a data frame in long format with predicted response probability for each person-item combination.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## Not run:
# generate data
N <- 20
J <- 10
betas <- cbind(rnorm(J, .5), rnorm(J, .5), rnorm(J, 1.5), rnorm(J, 0))
dat <- generate_irtree_ext(N = N, J = J, betas = betas, beta_ARS_extreme = .5)
# fit model
res1 <- fit_irtree(dat$X, revItem = dat$revItem, M = 200, warmup = 200)
res2 <- summarize_irtree_fit(res1)
res3 <- tidyup_irtree_fit(res2)
# expected frequencies
boeck_predict(res3)
## End(Not run)
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