eap: Compute eap trait estimates for FMP and FUP models

In fungible: Psychometric Functions from the Waller Lab

Compute eap trait estimates for FMP and FUP models

Description

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

Usage

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.

Author(s)

Niels Waller

Examples

## 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 March 31, 2023, 5:47 p.m.