simulateThetas function

Estimates theta under different adaptive battery specifications

Takes in response profiles from multiple respondents and multiple Cat object (i.e., adaptive battery) specifications and returns a set of theta estimates

simulateThetas(catObjs = list(), responses, return_adaptive = FALSE)

Arguments

• catObjs: A list of Cat objects of the same model with different adaptive battery specifications
• responses: A matrix of response profiles
• return_adaptive: Boolean indicating if user wants dataframe containing only answers chosen via the adaptive design for each Cat object in catObjs list.

Returns

The function simulateThetas returns a dataframe where each Cat object corresponds to a column and each respondent corresponds to a row if return_adaptive is FALSE, the default. Optionally, simulateThetas returns a list containing that dataframe plus dataframes for the answer profiles simulated via each adaptive design if return_adaptive is TRUE.

Details

The function takes multiple Cat objects, stored in a list, and generates an estimation for theta.

Examples

# Load Cat object
data(grm_cat)
   
# Simulate respondents
respondents <- plyr::adply(.data = matrix(c(-1, 0, 1)),
                           .margins = 1,
                           .id = NULL,
                           .fun = simulateRespondents, cat = grm_cat, n = 10)
 
# A stopping rule (here, a common one) is required
grm_cat@lengthThreshold <- 3

# Specify different adaptive inventory procedures
grm_MAP <- grm_EAP <- grm_cat
grm_MAP@estimation <- "MAP"
grm_EAP@estimation <- "EAP"

# List of Cat objects 
grmList <- list(cat1 = grm_MAP, cat2 = grm_EAP)

# Results
theta_est_results <- simulateThetas(catObjs = grmList, responses = respondents)

See Also

Cat-class, apply, selectItem

Author(s)

Haley Acevedo, Ryden Butler, Josh W. Cutler, Matt Malis, Jacob M. Montgomery, Tom Wilkinson, Erin Rossiter, Min Hee Seo, Alex Weil, Jaerin Kim, Dominique Lockett