fitI2L {lazy.irt} R Documentation

## Approximate Conversion of LRT to IRT Using Logit Transformation

### Description

Approximate Conversion of LRT to IRT Using Logit Transformation

### Usage

```fitI2L(V, print = 0, plot = 0, title = NULL)
```

### Arguments

 `V` item x class probability matrix `print` = 1 to print the estimated IRT item parameters = 2 to print the irf. `plot` = 1 to plot the main result = 2 to plot irf of each item. `title` title string

### Details

The LS criterion in terms of the logit:
` ssq( logit(t(V)) - logit(irf(theta|item parameters) )`
will be minimized by PCA with respect to theta and item parameters.

### Value

A list of:
theta The estimated theta value for each latent rank
param IRT item parameter data.frame
rmse The rmse stat.

### Examples

```#
#### In the following examples, maxiter is set to 20 which is
#### not large enough to obtain convergence.
####
#
#
#
set.seed(1701)

param <- paramB1[c(1:3,7:9,13:15),]
thmin <- -2; thmax <- 2; npoint <- 5
N <- 1000

# discrete theta
# theta0 <- seq(thmin,thmax,length=npoint)
theta0 <- c(-2, -1, 0, 2, 3)
theta <- unlist(lapply( theta0, rep, round(N/npoint) ))
res2 <- gendataIRT( 1, paramB1, theta=theta, compress=1 )
Uc <- as.data.frame(res2\$U)
ncat <- res2\$ncat
type <- res2\$type

# lrt parameters
nclass <- 5
resm1 <- uLRT( Uc, nclass=nclass, estrho=1, monotone=1, alpha=20
, maxiter=20, plot=1, print=1 )
V1 <- resm1\$V[,seq(2,2*resm1\$nitems,2)]

# conversion
res <- fitI2L( t(V1), print=1, plot=1 )
plot(theta0, res\$theta,type="b", main="original theta vs recovered theta")

```

[Package lazy.irt version 0.1.3 Index]