# eap: Compute eap trait estimates for FMP and FUP models In fungible: Psychometric Functions from the Waller Lab

## Description

Compute eap trait estimates for items fit by filtered monotonic polynomial IRT models.

## Usage

 `1` ```eap(data, bParams, NQuad = 21, priorVar = 2, mintheta = -4, maxtheta = 4) ```

## Arguments

 `data` N(subjects)-by-p(items) matrix of 0/1 item response data. `bParams` A p-by-9 matrix of FMP or FUP item parameters and model designations. Columns 1 - 8 hold the (possibly zero valued) polynomial coefficients; column 9 holds the value of `k`. `NQuad` Number of quadrature points used to calculate the eap estimates. `priorVar` Variance of the normal prior for the eap estimates. The prior mean equals 0. `mintheta, maxtheta` NQuad quadrature points will be evenly spaced between `mintheta` and `maxtheta`

## Value

 `eap trait estimates.`

Niels Waller

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52``` ```## this example demonstrates how to calculate ## eap trait estimates for a scale composed of items ## that have been fit to FMP models of different ## degree NSubjects <- 2000 ## Assume that ## items 1 - 5 fit a k=0 model, ## items 6 - 10 fit a k=1 model, and ## items 11 - 15 fit a k=2 model. itmParameters <- matrix(c( # b0 b1 b2 b3 b4 b5, b6, b7, k -1.05, 1.63, 0.00, 0.00, 0.00, 0, 0, 0, 0, #1 -1.97, 1.75, 0.00, 0.00, 0.00, 0, 0, 0, 0, #2 -1.77, 1.82, 0.00, 0.00, 0.00, 0, 0, 0, 0, #3 -4.76, 2.67, 0.00, 0.00, 0.00, 0, 0, 0, 0, #4 -2.15, 1.93, 0.00, 0.00, 0.00, 0, 0, 0, 0, #5 -1.25, 1.17, -0.25, 0.12, 0.00, 0, 0, 0, 1, #6 1.65, 0.01, 0.02, 0.03, 0.00, 0, 0, 0, 1, #7 -2.99, 1.64, 0.17, 0.03, 0.00, 0, 0, 0, 1, #8 -3.22, 2.40, -0.12, 0.10, 0.00, 0, 0, 0, 1, #9 -0.75, 1.09, -0.39, 0.31, 0.00, 0, 0, 0, 1, #10 -1.21, 9.07, 1.20,-0.01,-0.01, 0.01, 0, 0, 2, #11 -1.92, 1.55, -0.17, 0.50,-0.01, 0.01, 0, 0, 2, #12 -1.76, 1.29, -0.13, 1.60,-0.01, 0.01, 0, 0, 2, #13 -2.32, 1.40, 0.55, 0.05,-0.01, 0.01, 0, 0, 2, #14 -1.24, 2.48, -0.65, 0.60,-0.01, 0.01, 0, 0, 2),#15 15, 9, byrow=TRUE) # generate data using the above item parameters ex1.data<-genFMPData(NSubj = NSubjects, bParams = itmParameters, seed = 345)\$data ## calculate eap estimates for mixed models thetaEAP<-eap(data = ex1.data, bParams = itmParameters, NQuad = 25, priorVar = 2, mintheta = -4, maxtheta = 4) ## compare eap estimates with initial theta surrogates if(FALSE){ #set to TRUE to see plot thetaInit <- svdNorm(ex1.data) plot(thetaInit,thetaEAP, xlim = c(-3.5,3.5), ylim = c(-3.5,3.5), xlab = "Initial theta surrogates", ylab = "EAP trait estimates (Mixed models)") } ```

fungible documentation built on Sept. 29, 2021, 1:06 a.m.