data.big5: Dataset Big 5 from 'qgraph' Package
In sirt: Supplementary Item Response Theory Models
data.big5 R Documentation
Dataset Big 5 from qgraph Package
Description
This is a Big 5 dataset from the qgraph package (Dolan, Oorts,
Stoel, Wicherts, 2009). It contains 500 subjects on 240 items.
Usage
data(data.big5)
data(data.big5.qgraph)
Format
The format of data.big5 is:
num [1:500, 1:240] 1 0 0 0 0 1 1 2 0 1 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:240] "N1" "E2" "O3" "A4" ...
The format of data.big5.qgraph is:
num [1:500, 1:240] 2 3 4 4 5 2 2 1 4 2 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:240] "N1" "E2" "O3" "A4" ...
Details
In these datasets, there exist 48 items for each dimension. The Big 5
dimensions are Neuroticism (N), Extraversion (E),
Openness (O), Agreeableness (A) and
Conscientiousness (C). Note that the data.big5 differs from
data.big5.qgraph in a way that original items were recoded into
three categories 0,1 and 2.
Source
See big5 in qgraph package.
References
Dolan, C. V., Oort, F. J., Stoel, R. D., & Wicherts, J. M. (2009).
Testing measurement invariance in the target rotates multigroup exploratory
factor model. Structural Equation Modeling, 16, 295-314.
Examples
## Not run:
# list of needed packages for the following examples
packages <- scan(what="character")
sirt TAM eRm CDM mirt ltm mokken psychotools psychomix
psych
# load packages. make an installation if necessary
miceadds::library_install(packages)
#############################################################################
# EXAMPLE 1: Unidimensional models openness scale
#############################################################################
data(data.big5)
# extract first 10 openness items
items <- which( substring( colnames(data.big5), 1, 1 )=="O" )[1:10]
dat <- data.big5[, items ]
I <- ncol(dat)
summary(dat)
## > colnames(dat)
## [1] "O3" "O8" "O13" "O18" "O23" "O28" "O33" "O38" "O43" "O48"
# descriptive statistics
psych::describe(dat)
#****************
# Model 1: Partial credit model
#****************
#-- M1a: rm.facets (in sirt)
m1a <- sirt::rm.facets( dat )
summary(m1a)
#-- M1b: tam.mml (in TAM)
m1b <- TAM::tam.mml( resp=dat )
summary(m1b)
#-- M1c: gdm (in CDM)
theta.k <- seq(-6,6,len=21)
m1c <- CDM::gdm( dat, irtmodel="1PL",theta.k=theta.k, skillspace="normal")
summary(m1c)
# compare results with loglinear skillspace
m1c2 <- CDM::gdm( dat, irtmodel="1PL",theta.k=theta.k, skillspace="loglinear")
summary(m1c2)
#-- M1d: PCM (in eRm)
m1d <- eRm::PCM( dat )
summary(m1d)
#-- M1e: gpcm (in ltm)
m1e <- ltm::gpcm( dat, constraint="1PL", control=list(verbose=TRUE))
summary(m1e)
#-- M1f: mirt (in mirt)
m1f <- mirt::mirt( dat, model=1, itemtype="1PL", verbose=TRUE)
summary(m1f)
coef(m1f)
#-- M1g: PCModel.fit (in psychotools)
mod1g <- psychotools::PCModel.fit(dat)
summary(mod1g)
plot(mod1g)
#****************
# Model 2: Generalized partial credit model
#****************
#-- M2a: rm.facets (in sirt)
m2a <- sirt::rm.facets( dat, est.a.item=TRUE)
summary(m2a)
# Note that in rm.facets the mean of item discriminations is fixed to 1
#-- M2b: tam.mml.2pl (in TAM)
m2b <- TAM::tam.mml.2pl( resp=dat, irtmodel="GPCM")
summary(m2b)
#-- M2c: gdm (in CDM)
m2c <- CDM::gdm( dat, irtmodel="2PL",theta.k=seq(-6,6,len=21),
skillspace="normal", standardized.latent=TRUE)
summary(m2c)
#-- M2d: gpcm (in ltm)
m2d <- ltm::gpcm( dat, control=list(verbose=TRUE))
summary(m2d)
#-- M2e: mirt (in mirt)
m2e <- mirt::mirt( dat, model=1, itemtype="GPCM", verbose=TRUE)
summary(m2e)
coef(m2e)
#****************
# Model 3: Nonparametric item response model
#****************
#-- M3a: ISOP and ADISOP model - isop.poly (in sirt)
m3a <- sirt::isop.poly( dat )
summary(m3a)
plot(m3a)
#-- M3b: Mokken scale analysis (in mokken)
# Scalability coefficients
mokken::coefH(dat)
# Assumption of monotonicity
monotonicity.list <- mokken::check.monotonicity(dat)
summary(monotonicity.list)
plot(monotonicity.list)
# Assumption of non-intersecting ISRFs using method restscore
restscore.list <- mokken::check.restscore(dat)
summary(restscore.list)
plot(restscore.list)
#****************
# Model 4: Graded response model
#****************
#-- M4a: mirt (in mirt)
m4a <- mirt::mirt( dat, model=1, itemtype="graded", verbose=TRUE)
print(m4a)
mirt.wrapper.coef(m4a)
#---- M4b: WLSMV estimation with cfa (in lavaan)
lavmodel <- "F=~ O3__O48
F ~~ 1*F
"
# transform lavaan syntax with lavaanify.IRT
lavmodel <- TAM::lavaanify.IRT( lavmodel, items=colnames(dat) )$lavaan.syntax
mod4b <- lavaan::cfa( data=as.data.frame(dat), model=lavmodel, std.lv=TRUE,
ordered=colnames(dat), parameterization="theta")
summary(mod4b, standardized=TRUE, fit.measures=TRUE, rsquare=TRUE)
coef(mod4b)
#****************
# Model 5: Normally distributed residuals
#****************
#---- M5a: cfa (in lavaan)
lavmodel <- "F=~ O3__O48
F ~~ 1*F
F ~ 0*1
O3__O48 ~ 1
"
lavmodel <- TAM::lavaanify.IRT( lavmodel, items=colnames(dat) )$lavaan.syntax
mod5a <- lavaan::cfa( data=as.data.frame(dat), model=lavmodel, std.lv=TRUE,
estimator="MLR" )
summary(mod5a, standardized=TRUE, fit.measures=TRUE, rsquare=TRUE)
#---- M5b: mirt (in mirt)
# create user defined function
name <- 'normal'
par <- c("d"=1, "a1"=0.8, "vy"=1)
est <- c(TRUE, TRUE,FALSE)
P.normal <- function(par,Theta,ncat){
d <- par[1]
a1 <- par[2]
vy <- par[3]
psi <- vy - a1^2
# expected values given Theta
mui <- a1*Theta[,1] + d
TP <- nrow(Theta)
probs <- matrix( NA, nrow=TP, ncol=ncat )
eps <- .01
for (cc in 1:ncat){
probs[,cc] <- stats::dnorm( cc, mean=mui, sd=sqrt( abs( psi + eps) ) )
}
psum <- matrix( rep(rowSums( probs ),each=ncat), nrow=TP, ncol=ncat, byrow=TRUE)
probs <- probs / psum
return(probs)
}
# create item response function
normal <- mirt::createItem(name, par=par, est=est, P=P.normal)
customItems <- list("normal"=normal)
itemtype <- rep( "normal",I)
# define parameters to be estimated
mod5b.pars <- mirt::mirt(dat, 1, itemtype=itemtype,
customItems=customItems, pars="values")
ind <- which( mod5b.pars$name=="vy")
vy <- apply( dat, 2, var, na.rm=TRUE )
mod5b.pars[ ind, "value" ] <- vy
ind <- which( mod5b.pars$name=="a1")
mod5b.pars[ ind, "value" ] <- .5* sqrt(vy)
ind <- which( mod5b.pars$name=="d")
mod5b.pars[ ind, "value" ] <- colMeans( dat, na.rm=TRUE )
# estimate model
mod5b <- mirt::mirt(dat, 1, itemtype=itemtype, customItems=customItems,
pars=mod5b.pars, verbose=TRUE )
sirt::mirt.wrapper.coef(mod5b)$coef
# some item plots
par(ask=TRUE)
plot(mod5b, type='trace', layout=c(1,1))
par(ask=FALSE)
# Alternatively:
sirt::mirt.wrapper.itemplot(mod5b)
## End(Not run)
sirt documentation built on May 29, 2024, 8:43 a.m.
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| data.big5 | R Documentation |
Dataset Big 5 from qgraph Package
Description
This is a Big 5 dataset from the qgraph package (Dolan, Oorts, Stoel, Wicherts, 2009). It contains 500 subjects on 240 items.
Usage
data(data.big5)
data(data.big5.qgraph)
Format
The format of
data.big5is:
num [1:500, 1:240] 1 0 0 0 0 1 1 2 0 1 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:240] "N1" "E2" "O3" "A4" ...
The format of
data.big5.qgraphis:
num [1:500, 1:240] 2 3 4 4 5 2 2 1 4 2 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:240] "N1" "E2" "O3" "A4" ...
Details
In these datasets, there exist 48 items for each dimension. The Big 5
dimensions are Neuroticism (N), Extraversion (E),
Openness (O), Agreeableness (A) and
Conscientiousness (C). Note that the data.big5 differs from
data.big5.qgraph in a way that original items were recoded into
three categories 0,1 and 2.
Source
See big5 in qgraph package.
References
Dolan, C. V., Oort, F. J., Stoel, R. D., & Wicherts, J. M. (2009). Testing measurement invariance in the target rotates multigroup exploratory factor model. Structural Equation Modeling, 16, 295-314.
Examples
## Not run:
# list of needed packages for the following examples
packages <- scan(what="character")
sirt TAM eRm CDM mirt ltm mokken psychotools psychomix
psych
# load packages. make an installation if necessary
miceadds::library_install(packages)
#############################################################################
# EXAMPLE 1: Unidimensional models openness scale
#############################################################################
data(data.big5)
# extract first 10 openness items
items <- which( substring( colnames(data.big5), 1, 1 )=="O" )[1:10]
dat <- data.big5[, items ]
I <- ncol(dat)
summary(dat)
## > colnames(dat)
## [1] "O3" "O8" "O13" "O18" "O23" "O28" "O33" "O38" "O43" "O48"
# descriptive statistics
psych::describe(dat)
#****************
# Model 1: Partial credit model
#****************
#-- M1a: rm.facets (in sirt)
m1a <- sirt::rm.facets( dat )
summary(m1a)
#-- M1b: tam.mml (in TAM)
m1b <- TAM::tam.mml( resp=dat )
summary(m1b)
#-- M1c: gdm (in CDM)
theta.k <- seq(-6,6,len=21)
m1c <- CDM::gdm( dat, irtmodel="1PL",theta.k=theta.k, skillspace="normal")
summary(m1c)
# compare results with loglinear skillspace
m1c2 <- CDM::gdm( dat, irtmodel="1PL",theta.k=theta.k, skillspace="loglinear")
summary(m1c2)
#-- M1d: PCM (in eRm)
m1d <- eRm::PCM( dat )
summary(m1d)
#-- M1e: gpcm (in ltm)
m1e <- ltm::gpcm( dat, constraint="1PL", control=list(verbose=TRUE))
summary(m1e)
#-- M1f: mirt (in mirt)
m1f <- mirt::mirt( dat, model=1, itemtype="1PL", verbose=TRUE)
summary(m1f)
coef(m1f)
#-- M1g: PCModel.fit (in psychotools)
mod1g <- psychotools::PCModel.fit(dat)
summary(mod1g)
plot(mod1g)
#****************
# Model 2: Generalized partial credit model
#****************
#-- M2a: rm.facets (in sirt)
m2a <- sirt::rm.facets( dat, est.a.item=TRUE)
summary(m2a)
# Note that in rm.facets the mean of item discriminations is fixed to 1
#-- M2b: tam.mml.2pl (in TAM)
m2b <- TAM::tam.mml.2pl( resp=dat, irtmodel="GPCM")
summary(m2b)
#-- M2c: gdm (in CDM)
m2c <- CDM::gdm( dat, irtmodel="2PL",theta.k=seq(-6,6,len=21),
skillspace="normal", standardized.latent=TRUE)
summary(m2c)
#-- M2d: gpcm (in ltm)
m2d <- ltm::gpcm( dat, control=list(verbose=TRUE))
summary(m2d)
#-- M2e: mirt (in mirt)
m2e <- mirt::mirt( dat, model=1, itemtype="GPCM", verbose=TRUE)
summary(m2e)
coef(m2e)
#****************
# Model 3: Nonparametric item response model
#****************
#-- M3a: ISOP and ADISOP model - isop.poly (in sirt)
m3a <- sirt::isop.poly( dat )
summary(m3a)
plot(m3a)
#-- M3b: Mokken scale analysis (in mokken)
# Scalability coefficients
mokken::coefH(dat)
# Assumption of monotonicity
monotonicity.list <- mokken::check.monotonicity(dat)
summary(monotonicity.list)
plot(monotonicity.list)
# Assumption of non-intersecting ISRFs using method restscore
restscore.list <- mokken::check.restscore(dat)
summary(restscore.list)
plot(restscore.list)
#****************
# Model 4: Graded response model
#****************
#-- M4a: mirt (in mirt)
m4a <- mirt::mirt( dat, model=1, itemtype="graded", verbose=TRUE)
print(m4a)
mirt.wrapper.coef(m4a)
#---- M4b: WLSMV estimation with cfa (in lavaan)
lavmodel <- "F=~ O3__O48
F ~~ 1*F
"
# transform lavaan syntax with lavaanify.IRT
lavmodel <- TAM::lavaanify.IRT( lavmodel, items=colnames(dat) )$lavaan.syntax
mod4b <- lavaan::cfa( data=as.data.frame(dat), model=lavmodel, std.lv=TRUE,
ordered=colnames(dat), parameterization="theta")
summary(mod4b, standardized=TRUE, fit.measures=TRUE, rsquare=TRUE)
coef(mod4b)
#****************
# Model 5: Normally distributed residuals
#****************
#---- M5a: cfa (in lavaan)
lavmodel <- "F=~ O3__O48
F ~~ 1*F
F ~ 0*1
O3__O48 ~ 1
"
lavmodel <- TAM::lavaanify.IRT( lavmodel, items=colnames(dat) )$lavaan.syntax
mod5a <- lavaan::cfa( data=as.data.frame(dat), model=lavmodel, std.lv=TRUE,
estimator="MLR" )
summary(mod5a, standardized=TRUE, fit.measures=TRUE, rsquare=TRUE)
#---- M5b: mirt (in mirt)
# create user defined function
name <- 'normal'
par <- c("d"=1, "a1"=0.8, "vy"=1)
est <- c(TRUE, TRUE,FALSE)
P.normal <- function(par,Theta,ncat){
d <- par[1]
a1 <- par[2]
vy <- par[3]
psi <- vy - a1^2
# expected values given Theta
mui <- a1*Theta[,1] + d
TP <- nrow(Theta)
probs <- matrix( NA, nrow=TP, ncol=ncat )
eps <- .01
for (cc in 1:ncat){
probs[,cc] <- stats::dnorm( cc, mean=mui, sd=sqrt( abs( psi + eps) ) )
}
psum <- matrix( rep(rowSums( probs ),each=ncat), nrow=TP, ncol=ncat, byrow=TRUE)
probs <- probs / psum
return(probs)
}
# create item response function
normal <- mirt::createItem(name, par=par, est=est, P=P.normal)
customItems <- list("normal"=normal)
itemtype <- rep( "normal",I)
# define parameters to be estimated
mod5b.pars <- mirt::mirt(dat, 1, itemtype=itemtype,
customItems=customItems, pars="values")
ind <- which( mod5b.pars$name=="vy")
vy <- apply( dat, 2, var, na.rm=TRUE )
mod5b.pars[ ind, "value" ] <- vy
ind <- which( mod5b.pars$name=="a1")
mod5b.pars[ ind, "value" ] <- .5* sqrt(vy)
ind <- which( mod5b.pars$name=="d")
mod5b.pars[ ind, "value" ] <- colMeans( dat, na.rm=TRUE )
# estimate model
mod5b <- mirt::mirt(dat, 1, itemtype=itemtype, customItems=customItems,
pars=mod5b.pars, verbose=TRUE )
sirt::mirt.wrapper.coef(mod5b)$coef
# some item plots
par(ask=TRUE)
plot(mod5b, type='trace', layout=c(1,1))
par(ask=FALSE)
# Alternatively:
sirt::mirt.wrapper.itemplot(mod5b)
## End(Not run)
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