simulateFisherInfo {catSurv}

Calculates Fisher Information under different adaptive battery specifications

Description

Takes in a a Cat object, a set of respondents, and their corresponding theta values, and calculates the amount of information given an adaptive battery.

Usage

simulateFisherInfo(catObjs = list(), theta, responses)

Arguments

catObjs

A list of Cat objects of the same class.

theta

A vector of numerics representing the true value of theta.

responses

A dataframe of answer profiles corresponding to the true values of theta.

Details

The function takes a Cat object, theta, and response profiles. The user defines the selection type, estimation type, etc. so that the questions can be applied adaptively These adaptive profiles are then used to calculate the total inforamtion gained for a respondent for all answered items, conditioned on theta.

Value

The function simulateFisherInfo returns a dataframe where each Cat object corresponds to a column and each respondent corresponds to a row.

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

See Also

Cat-class, fisherTestInfo, selectItem

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(grm_MAP, grm_EAP)

# Results
fisher_inf_results <- simulateFisherInfo(catObjs = grmList,
                              theta = rep(c(-1, 0, 1),
                              each = 10),
                              responses = respondents)