eRm (version 0.9.0)

person.parameter: Estimation of Person Parameters

Description

Maximum likelihood estimation of the person parameters.

Usage

## S3 method for class 'eRm':
person.parameter(object, se = TRUE, splineInt = TRUE)
## S3 method for class 'ppar':
summary(object, ...)
## S3 method for class 'ppar':
print(x, ...)
## S3 method for class 'ppar':
plot(x, xlab = "Person Raw Scores", ylab = "Person Parameters (Theta)", main = NULL, ...)

Arguments

object

Object of class eRm.

se

If TRUE, the standard errors are estimated.

splineInt

TRUE if the missing person raw scores (0 and full raw score included) are interpolated using a cubic spline regression.

x

Object of class ppar.

xlab

Label of the x-axis.

ylab

Label of the y-axis.

main

Title of the plot.

...

Further arguments to be passed to or from other methods. They are ignored in this function.

Value

• The function person.parameter returns an object of class ppar containing:
• loglik
  Log-likelihood.
• npar
  Number of parameters.
• niter
  Number of iterations.
• thetapar
  Person parameter estimates.
• se.theta
  Standard errors of the person parameters.

Details

If the data set contains missing values, person parameters are estimated for each missing value subgroup.

References

Fischer, G. H., and Molenaar, I. (1995). Rasch Models - Foundations, Recent Developements, and Applications. Springer. Mair, P., and Hatzinger, R. (2007). Extended Rasch modeling: The eRm package for the application of IRT models in R. Journal of Statistical Software, 20(9), 1-20. Mair, P., and Hatzinger, R. (2007). CML based estimation of extended Rasch models with the eRm package in R. Psychology Science, 49, 26-43.

See Also

itemfit.ppar,personfit.ppar

Examples

#Person parameter estimation of a rating scale model
data(rsmdat)
res <- RSM(rsmdat)
pres <- person.parameter(res)
print(pres)
summary(pres)
plot(pres)

#Person parameter estimation for a Rasch model with missing values and
#witout spline interpolation.
data(raschdat1)
raschNA <- raschdat1
raschNA[1:40,1] <- NA                      #first item not presented
raschNA[41:100,2] <- NA                    #second item not presented
res <- RM(raschNA, se = FALSE)             #Rasch model without standard errors
pres <- person.parameter(res, splineInt = FALSE)
print(pres)                                #person parameteres round to 5 digits
summary(pres)