eap: Compute eap trait estimates for FMP and FUP models

Description Usage Arguments Value Author(s) Examples

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.

Author(s)

Niels Waller

Examples

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## 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)")
}                         


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