Description Usage Arguments Value References See Also Examples
This function takes posterior predictive probabilities (from
post_prob_irtree
), and compares—for several discrepency
measures—observations and predictions both descriptively and via posterior
predicitive p-values.
1 2 |
prob |
Numeric array of dimension R x N x J x 5 (for N persons, J items with 5 categories, and R iterations). |
statistics |
Character vector of length >= 1 specifying the discrepency measures to use. |
fit |
a fitted object from |
X |
Numeric matrix of dimension N x J containing the observed item responses.
|
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) |
Returns an object of class ppc
Levy, R. (2011). Posterior predictive model checking for conjunctive multidimensionality in item response theory. Journal of Educational and Behavioral Statistics, 36, 672-694. doi:10.3102/1076998611410213
Li, T., Xie, C., & Jiao, H. (2017). Assessing fit of alternative unidimensional polytomous IRT models using posterior predictive model checking. Psychological Methods, 22, 397-408. doi:10.1037/met0000082
Sinharay, S., Johnson, M. S., & Stern, H. S. (2006). Posterior predictive assessment of item response theory models. Applied Psychological Measurement, 30, 298-321. doi:10.1177/0146621605285517
Zhu, X., & Stone, C. A. (2012). Bayesian comparison of alternative graded response models for performance assessment applications. Educational and Psychological Measurement, 72, 774-799. doi:10.1177/0013164411434638
print.ppc
for summarizing the results and
ppc_resp_irtree
for summarizing posterior predictive response
frequencies.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ## Not run:
J <- 10
betas <- cbind(rnorm(J, .5), rnorm(J, .5), rnorm(J, 1.5), rnorm(J, 0))
dat <- generate_irtree_ext(N = 20, J = J, betas = betas, beta_ARS_extreme = .5)
# fit model
res1 <- fit_irtree(dat$X, revItem = dat$revItem, M = 200)
res2 <- summarize_irtree_fit(res1)
# posterior predictive checking
res3 <- post_prob_irtree(res2)
res4 <- ppc_irtree(prob = res3, fit = res1)
res4
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.