sirt (version 0.31-20)

latent.regression.em.raschtype: Latent Regression Model for the Generalized Logistic Item Response Model

Description

This function estimates a unidimensional latent regression model if a likelihood is specified or parameters from the generalized item response model (Stukel, 1988) is given as in input. Item parameters are treated as fixed in the estimation.

Usage

latent.regression.em.raschtype(data=NULL, f.yi.qk=NULL , X , 
    weights=rep(1, nrow(X)), beta.init=rep(0,ncol(X)), 
    sigma.init=1, b=rep(0,ncol(X)), a=rep(1,length(b)), 
    c=rep(0, length(b)), d=rep(1, length(b)), alpha1=0, alpha2=0, 
    max.parchange=1e-04, theta.list=seq(-5, 5, len=20), 
    maxiter=300 )

Arguments

data

An $N$ times $I$ data frame of dichotomous item responses. If no data frame is supplied, then a user can input the individual likelihood f.yi.qk.

f.yi.qk

An optional matrix which contains the individual likelihood. This matrix is produced by rasch.mml2 or rasch.copula2. The use of this argument

X

An $N$ times $K$ matrix of $K$ covariates in the latent regression model. Note that the intercept (i.e. a vector of ones) must be included in X.

weights

Student weights (optional).

beta.init

Initial regression coefficients (optional).

sigma.init

Initial residual standard deviation (optional).

b

Item difficulties (optional). They must only be provided if the likelihood f.yi.qk is not given as an input.

a

Item discriminations (optional).

c

Guessing parameter (lower asymptotes) (optional).

d

One minus slipping parameter (upper asymptotes) (optional).

alpha1

Upper tail parameter $\alpha_1$ in the generalized logistic item response model. Default is 0.

alpha2

Lower tail parameter $\alpha_2$ parameter in the generalized logistic item response model. Default is 0.

max.parchange

Maximum change in regression parameters

theta.list

Grid of person ability where theta is evaluated

maxiter

Maximum number of iterations

Value

• A list with following entries
• iterations
  Number of iterations needed
• maxiter
  Maximal number of iterations
• max.parchange
  Maximum change in parameter estimates
• coef
  Coefficients
• summary.coef
  Summary of regression coefficients
• sigma
  Estimate of residual standard deviation
• vcov.simple
  Covariance parameters of estimated parameters (simplified version)
• vcov.latent
  Covariance parameters of estimated parameters which accounts for latent ability
• post
  Individual posterior distribution
• EAP
  Individual EAP estimates
• SE.EAP
  Standard error estimates of EAP
• explvar
  Explained variance in latent regression
• totalvar
  Total variance in latent regression
• rsquared
  Explained variance $R^2$ in latent regression

Details

In the output Regression Parameters the fraction of missing information (fmi) is reported which is the increase of variance in regression parameter estimates due to the fact the ability is a latent variable. The effective sample size pseudoN.latent corresponds to a sample size when the ability would be available with a reliability of one.

References

Adams, R. & Wu. M. (2007). The mixed-coefficients multinomial logit model: A generalized form of the Rasch model. In M. von Davier & C. H. Carstensen: Multivariate and mixture distribution Rasch models: Extensions and applications (pp. 57-76). New York: Springer. Stukel, T. A. (1988). Generalized logistic models. Journal of the American Statistical Association, 83, 426-431.

See Also

See also plausible.value.imputation.raschtype for plausible value imputation of generalized logistic item type models.

Examples

############################################################
###  EXAMPLE 1: PISA Reading
############################################################

data( data.pisaRead)
dat <- data.pisaRead$data
items <- grep("R" , colnames(dat))
# define matrix of covariates
X <- cbind( 1 , dat[ , c("female","hisei","migra" ) ] )

#***
# Model 1: Latent regression model in the Rasch model
# estimate Rasch model
mod1 <- rasch.mml2( dat[,items] )
# latent regression model
lm1 <- latent.regression.em.raschtype( data=dat[,items ],
            X = X , b = mod1$item$b )

#***
# Model 2: Latent regression with generalized link function
# estimate alpha parameters for link function
mod2 <- rasch.mml2( dat[,items] , est.alpha=TRUE)
# use model estimated likelihood for latent regression model
lm2 <- latent.regression.em.raschtype( f.yi.qk=mod2$f.yi.qk , 
            X = X , theta.list=mod2$theta.k)

#***
# Model 3: Latent regression model based on Rasch copula model
testlets <- paste( data.pisaRead$item$testlet)
itemclusters <- match( testlets , unique(testlets) )
# estimate Rasch copula model
mod3 <- rasch.copula2( dat[,items] , itemcluster=itemclusters )
# use model estimated likelihood for latent regression model
lm3 <- latent.regression.em.raschtype( f.yi.qk=mod3$f.yi.qk , 
           X = X , theta.list=mod3$theta.k) 

     
############################################################
### SIMULATED EXAMPLE 2
set.seed(899)
############################################################

I <- 21     # number of items
b <- seq(-2,2, len=I)   # item difficulties
n <- 2000       # number of students

# simulate theta and covariates
theta <- rnorm( n )
x <- .7 * theta + rnorm( n , .5 )
y <- .2 * x+ .3*theta + rnorm( n , .4 )
dfr <- data.frame( theta , 1 , x , y )

# simulate Rasch model
dat1 <- sim.raschtype( theta = theta , b = b )

# estimate latent regression
mod <- latent.regression.em.raschtype( data = dat1 , X  = dfr[,-1] , b = b )
## Regression Parameters
## 
##        est se.simple     se        t p   beta    fmi N.simple pseudoN.latent
## X1 -0.2554    0.0208 0.0248 -10.2853 0 0.0000 0.2972     2000       1411.322
## x   0.4113    0.0161 0.0193  21.3037 0 0.4956 0.3052     2000       1411.322
## y   0.1715    0.0179 0.0213   8.0438 0 0.1860 0.2972     2000       1411.322
## 
## Residual Variance  = 0.685 
## Explained Variance = 0.3639 
## Total Variance     = 1.049 
##                 R2 = 0.3469 

# compare with linear model (based on true scores)
summary( lm( theta  ~ x + y , data = dfr ) )
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.27821    0.01984  -14.02   <2e-16 ***
## x            0.40747    0.01534   26.56   <2e-16 ***
## y            0.18189    0.01704   10.67   <2e-16 ***
## ---
## 
## Residual standard error: 0.789 on 1997 degrees of freedom
## Multiple R-squared: 0.3713,     Adjusted R-squared: 0.3707 

#***********
# define guessing parameters (lower asymptotes) and 
# upper asymptotes ( 1 minus slipping parameters)
cI <- rep(.2, I)        # all items get a guessing parameter of .2
cI[ c(7,9) ] <- .25     # 7th and 9th get a guessing parameter of .25
dI <- rep( .95 , I )	# upper asymptote of .95
dI[ c(7,11) ] <- 1		# 7th and 9th item have an asymptote of 1

# latent regression model
mod1 <- latent.regression.em.raschtype( data = dat1 , X  = dfr[,-1] ,
		b = b , c = cI , d = dI    )
## Regression Parameters
## 
##        est se.simple     se        t p   beta    fmi N.simple pseudoN.latent
## X1 -0.7929    0.0243 0.0315 -25.1818 0 0.0000 0.4044     2000       1247.306
## x   0.5025    0.0188 0.0241  20.8273 0 0.5093 0.3936     2000       1247.306
## y   0.2149    0.0209 0.0266   8.0850 0 0.1960 0.3831     2000       1247.306
## 
## Residual Variance  = 0.9338 
## Explained Variance = 0.5487 
## Total Variance     = 1.4825 
##                 R2 = 0.3701