Shadow is a test assembly function for performing adaptive test assembly based on the generalized shadow-test framework.
Shadow(
config,
constraints = NULL,
true_theta = NULL,
data = NULL,
prior = NULL,
prior_par = NULL,
exclude = NULL,
include_items_for_estimation = NULL,
force_solver = FALSE,
session = NULL,
seed = NULL
)
# S4 method for config_Shadow
Shadow(
config,
constraints = NULL,
true_theta = NULL,
data = NULL,
prior = NULL,
prior_par = NULL,
exclude = NULL,
include_items_for_estimation = NULL,
force_solver = FALSE,
session = NULL,
seed = NULL
)a config_Shadow object. Use createShadowTestConfig for this.
a constraints object representing test specifications. Use loadConstraints for this.
(optional) true theta values to use in simulation. Either true_theta or data must be supplied.
(optional) a matrix containing item response data to use in simulation. Either true_theta or data must be supplied.
(optional) density at each config@theta_grid to use as prior.
Must be a length-nq vector or a nj * nq matrix.
This overrides prior_dist and prior_par in the config.
prior and prior_par cannot be used simultaneously.
(optional) normal distribution parameters c(mean, sd) to use as prior.
Must be a length-nq vector or a nj * nq matrix.
This overrides prior_dist and prior_par in the config.
prior and prior_par cannot be used simultaneously.
(optional) a list containing item names in $i and set names in $s to exclude from selection for each participant. The length of the list must be equal to the number of participants.
(optional) an examinee-wise list containing:
administered_item_pool items to include in theta estimation as item_pool object.
administered_item_resp item responses to include in theta estimation.
if TRUE, do not check whether the solver is one of recommended solvers for complex problems (set-based assembly, partitioning). (default = FALSE)
(optional) used to communicate with Shiny app TestDesign.
(optional) used to perform data generation internally.
Shadow returns an output_Shadow_all object containing assembly results.
van der Linden, W. J., Reese, L. M. (1998). A model for optimal constrained adaptive testing. Applied Psychological Measurement, 22, 259-270.
van der Linden, W. J. (1998). Optimal assembly of psychological and educational tests. Applied Psychological Measurement, 22, 195-211.
van der Linden, W. J. (2000). Optimal assembly of tests with item sets. Applied Psychological Measurement, 24, 225-240.
van der Linden, W. J. (2005). Linear models for optimal test design. Springer Science & Business Media.
config <- createShadowTestConfig()
true_theta <- rnorm(1)
solution <- Shadow(config, constraints_science, true_theta)
#> Loading required namespace: progress
solution@output
#> [[1]]
#> Simulee index : 1
#> True theta : -1.400044
#> Final theta estimate : -1.338556
#> Final SE estimate : 0.2672964
#> stage stimulus_index item_index item_resp item_ncat interim_theta interim_se
#> 1 1 NA 567 0 3 -0.5924506 0.7490406
#> 2 2 NA 795 0 3 -1.3194239 0.6560083
#> 3 3 NA 847 0 3 -1.8032921 0.5822011
#> 4 4 NA 89 2 3 -1.2185459 0.4648906
#> 5 5 NA 940 1 3 -1.2142178 0.4259300
#> 6 6 NA 587 2 3 -0.9486594 0.4007839
#> 7 7 NA 900 0 3 -1.0774608 0.3851033
#> 8 8 NA 42 0 2 -1.1279861 0.3748951
#> 9 9 NA 92 0 2 -1.1661351 0.3687200
#> 10 10 NA 4 1 2 -1.1268611 0.3631582
#> 11 11 NA 56 1 3 -1.1036555 0.3351523
#> 12 12 NA 290 0 4 -1.1764425 0.3257606
#> 13 13 NA 428 0 2 -1.2592226 0.3196785
#> 14 14 NA 538 0 2 -1.3201765 0.3152927
#> 15 15 NA 615 0 2 -1.3911677 0.3114897
#> 16 16 NA 11 0 3 -1.4636527 0.3096513
#> 17 17 NA 600 0 2 -1.5237047 0.3069239
#> 18 18 NA 420 1 2 -1.4950430 0.3004250
#> 19 19 NA 421 0 2 -1.5201125 0.2993451
#> 20 20 NA 3 1 2 -1.4840899 0.2982755
#> 21 21 NA 263 0 3 -1.5372597 0.2926565
#> 22 22 NA 865 1 3 -1.4705398 0.2852602
#> 23 23 NA 946 1 3 -1.3813534 0.2772106
#> 24 24 NA 549 0 2 -1.4153764 0.2764295
#> 25 25 NA 680 0 3 -1.4307520 0.2748275
#> 26 26 NA 629 1 2 -1.4002018 0.2724934
#> 27 27 NA 36 0 2 -1.4212331 0.2718303
#> 28 28 NA 679 1 2 -1.3924608 0.2695204
#> 29 29 NA 352 1 2 -1.3594577 0.2681620
#> 30 30 NA 194 1 2 -1.3385562 0.2672964
#> theta_segment
#> 1 4
#> 2 3
#> 3 3
#> 4 2
#> 5 3
#> 6 3
#> 7 3
#> 8 3
#> 9 3
#> 10 3
#> 11 3
#> 12 3
#> 13 3
#> 14 3
#> 15 3
#> 16 3
#> 17 3
#> 18 2
#> 19 3
#> 20 2
#> 21 3
#> 22 2
#> 23 3
#> 24 3
#> 25 3
#> 26 3
#> 27 3
#> 28 3
#> 29 3
#> 30 3
#>
#>