Description Usage Arguments Details Value Author(s) See Also Examples
k-Cube Thurstonian IRT Fitting using a least-squares expectation-expectation algorithm.
1 | kcirt.fitEE(model, mxHatLambda, maxIter = 40, lambda.ridge = 0.3, Seta.ridge=0.01)
|
model |
A kcirt model. A named list of |
mxHatLambda |
An initial guess for the Hyperparameters. |
maxIter |
Maximum number of iterations. |
lambda.ridge |
Non-negative real-valued scalar. Amount of Ridge shrinkage on hatLambda crossproduct for LS stages. |
Seta.ridge |
Non-negative real-valued scalar. Amount of Ridge shrinkage on SEta crossproduct for LS stages. |
This function can be thought of as an expectation-expectation procedure. The starting Hyperparameters, mxHatLambda
, are used to predict mxEta
(this prediction is commonly called mxHatEta
is this package), and so on, back and forth. The procedure stops when either the L2 cost first bottoms out, or maxIter
is met.
In many cases, this function alone produces excellent-performing estimates/predictions. The user may pass the returned model to kcirt.fitMSS
for further refinement.
A kcirt model. A named list of class
'kcube.irt.model'.
Dave Zes, Korn/Ferry International
See Also kcirt.fitMSS
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | constructMap.ls <- list(
c(1,1,2,2),
c(1,1,3,3),
c(2,2,3,3),
c(1,1,2,2),
c(1,1,3,3),
c(2,2,3,3)
)
qTypes <- rep("R", length(constructMap.ls))
mod <- kcirt.model(constructMap.ls=constructMap.ls, qTypes=qTypes, mxLambda=NULL)
N <- 300
set.seed(99999)
mod <- kcirt.sim(model=mod, N=N)
ikcirt.df1(mod, "self")
mxHatLambda <- mod$mxLambda - matrix( rnorm( sum(mod$ns)^2, 0, 0.3 ), sum(mod$ns), sum(mod$ns) )
mod2 <- kcirt.fitEE(model=mod, mxHatLambda=mxHatLambda, maxIter=40)
|
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