sirt (version 1.14-0)

pcm.fit: Item and Person Fit Statistics for the Partial Credit Model

Description

Computes item and person fit statistics in the partial credit model (Wright & Masters, 1990). The rating scale model is accomodated as a particular partial credit model (see Example 3).

Usage

pcm.fit(b, theta, dat)

Arguments

b

Matrix with item category parameters (see Examples)

theta

Vector with estimated person parameters

dat

Dataset with item responses

Value

A list with entries

References

Wright, B. D., & Masters, G. N. (1990). Computation of outfit and infit statistics. Rasch Measurement Transactions, 3:4, 84-85.

See Also

See also personfit.stat for person fit statistics for dichotomous item responses. See also the PerFit package for further person fit statistics.

Item fit in other R packages: eRm::itemfit, TAM::tam.fit, mirt::itemfit, ltm::item.fit,

Person fit in other R packages: eRm::itemfit, mirt::itemfit, ltm::person.fit,

See pcm.conversion for conversions of different parametrizations of the partial credit model.

Examples

## Not run: 	
# #############################################################################
# # EXAMPLE 1: Partial credit model
# #############################################################################	
# 
# data(data.Students,package="CDM")
# dat <- data.Students
# # select items
# items <- c(paste0("sc" , 1:4 ) , paste0("mj" , 1:4 ) ) 
# dat <- dat[,items]
# dat <- dat[ rowSums( 1 - is.na(dat) ) > 0 , ]
# 
# #*** Model 1a: Partial credit model in TAM
# # estimate model
# mod1a <- TAM::tam.mml( resp=dat )
# summary(mod1a)
# # estimate person parameters
# wle1a <- TAM::tam.wle(mod1a)
# # extract item parameters
# b1 <- - mod1a$AXsi[ , -1 ]
# # parametrization in xsi parameters
# b2 <- matrix( mod1a$xsi$xsi , ncol=3 , byrow=TRUE )
# # convert b2 to b1
# b1b <- 0*b1
# b1b[,1] <- b2[,1]
# b1b[,2] <- rowSums( b2[,1:2] )
# b1b[,3] <- rowSums( b2[,1:3] )
# # assess fit
# fit1a <- pcm.fit(b=b1, theta=wle1a$theta , dat)
# fit1a$item
# 
# #############################################################################
# # EXAMPLE 2: Rasch model
# #############################################################################   
# 
# data(data.read)
# dat <- data.read
# 
# #*** Rasch model in TAM
# # estimate model
# mod <- TAM::tam.mml( resp=dat )
# summary(mod)
# # estimate person parameters
# wle <- TAM::tam.wle(mod)
# # extract item parameters
# b1 <- - mod$AXsi[ , -1 ]
# # assess fit
# fit1a <- pcm.fit(b=b1, theta=wle$theta , dat)
# fit1a$item
# 
# #############################################################################
# # EXAMPLE 3: Rating scale model
# #############################################################################
# 
# data(data.Students,package="CDM")
# dat <- data.Students
# items <- paste0("sc" , 1:4 ) 
# dat <- dat[,items]
# dat <- dat[ rowSums( 1 - is.na(dat) ) > 0 , ]
# 
# #*** Model 1: Rating scale model in TAM
# # estimate model
# mod1 <- tam.mml( resp=dat , irtmodel="RSM")
# summary(mod1)
# # estimate person parameters
# wle1 <- tam.wle(mod1)
# # extract item parameters
# b1 <- - mod1a$AXsi[ , -1 ]
# # fit statistic
# pcm.fit(b=b1, theta=wle1$theta, dat)
# ## End(Not run)