kcirt.fitEE: Least Squares k-Cube Thurstonian IRT Fitting In kcirt: k-Cube Thurstonian IRT Models

Description

k-Cube Thurstonian IRT Fitting using a least-squares expectation-expectation algorithm.

Usage

 `1` ```kcirt.fitEE(model, mxHatLambda, maxIter = 40, lambda.ridge = 0.3, Seta.ridge=0.01) ```

Arguments

 `model` A kcirt model. A named list of `class` 'kcube.irt.model'. `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.

Details

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.

Value

A kcirt model. A named list of `class` 'kcube.irt.model'.

Author(s)

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) ```