eRm (version 1.0-1)

person.parameter: Estimation of Person Parameters

Description

Maximum likelihood estimation of the person parameters with spline interpolation for non-observed and 0/full responses. Extraction of information criteria such as AIC, BIC, and cAIC based on unconditional log-likelihood.

Usage

# S3 method for eRm
person.parameter(object)
# S3 method for ppar
summary(object, ...)
# S3 method for ppar
print(x, ...)
# S3 method for ppar
plot(x, xlab = "Person Raw Scores",
   ylab = "Person Parameters (Theta)", main = NULL, ...)
# S3 method for ppar
coef(object, extrapolated = TRUE, ...)
# S3 method for ppar
logLik(object, ...)
# S3 method for ppar
confint(object, parm, level = 0.95, ...)

Arguments

object

Object of class 'eRm' in person.parameter and object of class ppar in IC.

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.

extrapolated

either returns extrapolated values for raw scores 0 and k or sets them NA

parm

Parameter specification (ignored).

level

Alpha-level.

Value

The function person.parameter returns an object of class ppar containing:

loglik

Log-likelihood of the collapsed data (for faster estimation persons with the same raw score are collapsed).

npar

Number of parameters.

niter

Number of iterations.

thetapar

Person parameter estimates.

se.theta

Standard errors of the person parameters.

hessian

Hessian matrix.

theta.table

Matrix with person parameters (ordered according to original data) including NA pattern group.

pers.ex

Indices with persons excluded due to 0/full raw score

X.ex

Data matrix with persons excluded

gmemb

NA group membership vector (0/full persons excluded)

The function coef returns a vector of the person parameter estimates for each person (i.e., the first column of theta.table).

The function logLik returns an object of class loglik.ppar containing:

loglik

Log-likelihood of the collapsed data (see above).

df

Degrees of freedom.

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

# NOT RUN {
#Person parameter estimation of a rating scale model
res <- RSM(rsmdat)
pres <- person.parameter(res)
pres
summary(pres)
plot(pres)

#Person parameter estimation for a Rasch model with missing values
res <- RM(raschdat2, se = FALSE) #Rasch model without standard errors
pres <- person.parameter(res)
pres                             #person parameters
summary(pres)
logLik(pres)                     #log-likelihood of person parameter estimation
# }