createShadowTestConfig: Create a config_Shadow object

In choi-phd/TestDesign: Optimal Test Design Approach to Fixed and Adaptive Test Construction

Create a config_Shadow object

Description

createShadowTestConfig is a config function for creating a config_Shadow object for shadowtest assembly. Default values are used for any unspecified parameters/slots.

Usage

createShadowTestConfig(
  item_selection = NULL,
  content_balancing = NULL,
  MIP = NULL,
  MCMC = NULL,
  exclude_policy = NULL,
  refresh_policy = NULL,
  exposure_control = NULL,
  overlap_control = NULL,
  stopping_criterion = NULL,
  interim_theta = NULL,
  final_theta = NULL,
  theta_grid = seq(-4, 4, 0.1)
)

Arguments

item_selection	a named list containing item selection criteria. method the type of selection criteria. Accepts MFI, MPWI, FB, EB, GFI. (default = MFI) info_type the type of information. Accepts FISHER. (default = FISHER) initial_theta (optional) initial theta values to use. fixed_theta (optional) fixed theta values to use throughout all item positions. target_value (optional) the target value to use for method = 'GFI'.
content_balancing	a named list containing content balancing options. method the type of balancing method. Accepts NONE, STA. (default = STA)
MIP	a named list containing solver options. solver the type of solver. Accepts Rsymphony, highs, gurobi, lpSolve, Rglpk. (default = HIGHS) verbosity verbosity level of the solver. (default = -2) time_limit time limit in seconds. Used in solvers Rsymphony, gurobi, Rglpk. (default = 60) gap_limit search termination criterion. Gap limit in relative scale passed onto the solver. Used in solver gurobi. (default = .05) gap_limit_abs search termination criterion. Gap limit in absolute scale passed onto the solver. Used in solvers Rsymphony. (default = 0.05) obj_tol search termination criterion. The lower bound to use on the minimax deviation variable. Used when item_selection$method is GFI, and ignored otherwise. (default = 0.05) retry number of times to retry running the solver if the solver returns no solution. Some solvers incorrectly return no solution even when a solution exists. This is the number of attempts to verify that the problem is indeed infeasible in such cases. Set to 0 to not retry. (default = 5)
MCMC	a named list containing Markov-chain Monte Carlo configurations for obtaining posterior samples. burn_in the number of chains from the start to discard. (default = 100) post_burn_in the number of chains to use after discarding the first burn_in chains. (default = 500) thin thinning interval to apply. 1 represents no thinning. (default = 1) jump_factor the jump (scaling) factor for the proposal distribution. 1 represents no jumping. (default = 2.4)
exclude_policy	a named list containing the exclude policy for use with the exclude argument in Shadow. method the type of policy. Accepts HARD, SOFT. (default = HARD) M the Big M penalty to use on item information. Used in the SOFT method.
refresh_policy	a named list containing the refresh policy for when to obtain a new shadowtest. method the type of policy. Accepts ALWAYS, POSITION, INTERVAL, THRESHOLD, INTERVAL-THRESHOLD, STIMULUS, SET, PASSAGE. (default = ALWAYS) interval used in methods INTERVAL, INTERVAL-THRESHOLD. Set to 1 to refresh at each position, 2 to refresh at every two positions, and so on. (default = 1) threshold used in methods THRESHOLD, INTERVAL-THRESHOLD. The absolute change in between interim theta estimates to trigger the refresh. (default = 0.1) position used in methods POSITION. Item positions to trigger the refresh. (default = 1)
exposure_control	a named list containing exposure control settings. method the type of exposure control method. Accepts NONE, ELIGIBILITY, BIGM, BIGM-BAYESIAN. (default = ELIGIBILITY) M used in methods BIGM, BIGM-BAYESIAN. the Big M penalty to use on item information. max_exposure_rate target exposure rates for each segment. (default = rep(0.25, 7)) acceleration_factor the acceleration factor to apply. (default = 1) n_segment the number of theta segments to use. (default = 7) first_segment (optional) the theta segment assumed at the beginning of test for all participants. segment_cut theta segment cuts. (default = c(-Inf, seq(-2.5, 2.5, 1), Inf)) initial_eligibility_stats (optional) initial eligibility statistics to use. fading_factor the fading factor to apply. (default = .999) diagnostic_stats set to TRUE to generate segment-wise diagnostic statistics. (default = FALSE)
overlap_control	a named list containing overlap control settings. method the type of overlap control method. Accepts NONE, ELIGIBILITY, BIGM, BIGM-BAYESIAN. (default = NONE) M used in methods BIGM, BIGM-BAYESIAN. the Big M penalty to use on item information. max_overlap_rate target overlap rate. (default = 0.20)
stopping_criterion	a named list containing stopping criterion. method the type of stopping criterion. Accepts FIXED. (default = FIXED) test_length test length. min_ni the maximum number of items to administer. max_ni the minimum number of items to administer. se_threshold standard error threshold. Item administration is stopped when theta estimate standard error becomes lower than this value.
interim_theta	a named list containing interim theta estimation options. method the type of estimation. Accepts EAP, MLE, MLEF, EB, FB, CARRYOVER. (default = EAP) shrinkage_correction set TRUE to apply shrinkage correction. Used when method is EAP. (default = FALSE) prior_dist the type of prior distribution. Accepts NORMAL, UNIFORM. (default = NORMAL) prior_par distribution parameters for prior_dist. (default = c(0, 1)) bound_ML theta bound in c(lower_bound, upper_bound) format. Used when method is MLE. (default = -4, 4) truncate_ML set TRUE to truncate ML estimate within bound_ML. (default = FALSE) max_iter maximum number of Newton-Raphson iterations. Used when method is MLE. (default = 50) crit convergence criterion. Used when method is MLE. (default = 1e-03) max_change maximum change in ML estimates between iterations. Changes exceeding this value is clipped to this value. Used when method is MLE. (default = 1.0) use_step_size set TRUE to use step_size. Used when method is MLE or MLEF. (default = FALSE) step_size upper bound to impose on the absolute change in initial theta and estimated theta. Absolute changes exceeding this value will be capped to step_size. Used when method is MLE or MLEF. (default = 0.5) do_Fisher set TRUE to use Fisher's method of scoring. Used when method is MLE. (default = TRUE) fence_slope slope parameter to use for method = 'MLEF'. This must have two values in total, for the lower and upper bound item respectively. Use one value to use the same value for both bounds. (default = 5) fence_difficulty difficulty parameters to use for method = 'MLEF'. This must have two values in total, for the lower and upper bound item respectively. (default = c(-5, 5)) hand_scored_attribute (optional) the item attribute name for whether each item is hand-scored or not. The attribute should have TRUE (hand-scored) and FALSE (machine-scored) values. If a hand-scored item is administered to an examinee, the previous interim theta (or the starting theta if this occurs for the first item) is reused without updating the estimate.
final_theta	a named list containing final theta estimation options. method the type of estimation. Accepts EAP, MLE, MLEF, EB, FB, CARRYOVER. (default = EAP) shrinkage_correction set TRUE to apply shrinkage correction. Used when method is EAP. (default = FALSE) prior_dist the type of prior distribution. Accepts NORMAL, UNIFORM. (default = NORMAL) prior_par distribution parameters for prior_dist. (default = c(0, 1)) bound_ML theta bound in c(lower_bound, upper_bound) format. Used when method is MLE. (default = -4, 4) truncate_ML set TRUE to truncate ML estimate within bound_ML. (default = FALSE) max_iter maximum number of Newton-Raphson iterations. Used when method is MLE. (default = 50) crit convergence criterion. Used when method is MLE. (default = 1e-03) max_change maximum change in ML estimates between iterations. Changes exceeding this value is clipped to this value. Used when method is MLE. (default = 1.0) use_step_size set TRUE to use step_size. Used when method is MLE or MLEF. (default = FALSE) step_size upper bound to impose on the absolute change in initial theta and estimated theta. Absolute changes exceeding this value will be capped to step_size. Used when method is MLE or MLEF. (default = 0.5) do_Fisher set TRUE to use Fisher's method of scoring. Used when method is MLE. (default = TRUE) fence_slope slope parameter to use for method = 'MLEF'. This must have two values in total, for the lower and upper bound item respectively. Use one value to use the same value for both bounds. (default = 5) fence_difficulty difficulty parameters to use for method = 'MLEF'. This must have two values in total, for the lower and upper bound item respectively. (default = c(-5, 5))
theta_grid	the theta grid to use as quadrature points.

Examples

cfg1 <- createShadowTestConfig(refresh_policy = list(
  method = "STIMULUS"
))
cfg2 <- createShadowTestConfig(refresh_policy = list(
  method = "POSITION",
  position = c(1, 5, 9)
))

choi-phd/TestDesign documentation built on Oct. 1, 2024, 2:37 a.m.