gdm: General Diagnostic Model

Description

This function estimates the general diagnostic model (von Davier, 2008; Xu & von Davier, 2008) which handles multidimensional item response models with ordered discrete or continuous latent variables for polytomous item responses.

Usage

gdm( data, theta.k, irtmodel="2PL", group=NULL, weights=rep(1, nrow(data)),
    Qmatrix=NULL, thetaDes=NULL, skillspace="loglinear",
    b.constraint=NULL, a.constraint=NULL,
    mean.constraint=NULL, Sigma.constraint=NULL, delta.designmatrix=NULL,
    standardized.latent=FALSE, centered.latent=FALSE,
    centerintercepts=FALSE, centerslopes=FALSE,
    maxiter=1000,  conv=1e-5, globconv=1e-5, msteps=4, convM=.0005,
    decrease.increments=FALSE, use.freqpatt=FALSE, progress=TRUE,
    PEM=FALSE, PEM_itermax=maxiter, ...)
# S3 method for gdm
summary(object, file=NULL, ...)
# S3 method for gdm
print(x, ...)
# S3 method for gdm
plot(x, perstype="EAP", group=1, barwidth=.1, histcol=1,
       cexcor=3, pchpers=16, cexpers=.7, ... )

Value

An object of class gdm. The list contains the following entries:

item: Data frame with item parameters
person: Data frame with person parameters: EAP denotes the mean of the individual posterior distribution, SE.EAP the corresponding standard error, MLE the maximum likelihood estimate at theta.k and MAP the mode of the posterior distribution
EAP.rel: Reliability of the EAP
deviance: Deviance
ic: Information criteria, number of estimated parameters
b: Item intercepts $b_{jk}$
se.b: Standard error of item intercepts $b_{jk}$
a: Item slopes $a_{jd}$ resp. $a_{jdk}$
se.a: Standard error of item slopes $a_{jd}$ resp. $a_{jdk}$
itemfit.rmsea: The RMSEA item fit index (see itemfit.rmsea). This entry comes as a list with total and group-wise item fit statistics.
mean.rmsea: Mean of RMSEA item fit indexes.
Qmatrix: Used Q-matrix
pi.k: Trait distribution
mean.trait: Means of trait distribution
sd.trait: Standard deviations of trait distribution
skewness.trait: Skewnesses of trait distribution
correlation.trait: List of correlation matrices of trait distribution corresponding to each group
pjk: Item response probabilities evaluated at grid theta.k
n.ik: An array of expected counts $n_{cikg}$ of ability class $c$ at item $i$ at category $k$ in group $g$
G: Number of groups
D: Number of dimension of $\bold{\theta}$
I: Number of items
N: Number of persons
delta: Parameter estimates for skillspace representation
covdelta: Covariance matrix of parameter estimates for skillspace representation
data: Original data frame
group.stat: Group statistics (sample sizes, group labels)
p.xi.aj: Individual likelihood
posterior: Individual posterior distribution
skill.levels: Number of skill levels per dimension
K.item: Maximal category per item
theta.k: Used theta design or estimated theta trait distribution in case of skillspace="est"
thetaDes: Used theta design for item responses
se.theta.k: Estimated standard errors of theta.k if it is estimated
time: Info about computation time
skillspace: Used skillspace parametrization
iter: Number of iterations
converged: Logical indicating whether convergence was achieved.
object: Object of class gdm
x: Object of class gdm
perstype: Person paramter estimate type. Can be either "EAP", "MAP" or "MLE".
group: Group which should be used for plot.gdm
barwidth: Bar width in plot.gdm
histcol: Color of histogram bars in plot.gdm
cexcor: Font size for print of correlation in plot.gdm
pchpers: Point type for scatter plot of person parameters in plot.gdm
cexpers: Point size for scatter plot of person parameters in plot.gdm
...: Optional parameters to be passed to or from other methods will be ignored.

Arguments

data: An $N \times I$ matrix of polytomous item responses with categories $k=0,1,...,K$
theta.k: In the one-dimensional case it must be a vector. For multidimensional models it has to be a list of skill vectors if the theta grid differs between dimensions. If not, a vector input can be supplied. If an estimated skillspace (skillspace="est" should be estimated, a vector or a matrix theta.k will be used as initial values of the estimated $\bold{\theta}$ grid.
irtmodel: The default 2PL corresponds to the model where item slopes on dimensions are equal for all item categories. If item-category slopes should be estimated, use 2PLcat. If no item slopes should be estimated then 1PL can be selected. Note that fixed item slopes can be specified in the Q-matrix (argument Qmatrix).
group: An optional vector of group identifiers for multiple group estimation. For plot.gdm it is an integer indicating which group should be used for plotting.
weights: An optional vector of sample weights
Qmatrix: An optional array of dimension $I \times D \times K$ which indicates pre-specified item loadings on dimensions. The default for category $k$ is the score $k$, i.e. the scoring in the (generalized) partial credit model.
thetaDes: A design matrix for specifying nonlinear item response functions (see Example 1, Models 4 and 5)
skillspace: The parametric assumption of the skillspace. If skillspace="normal" then a univariate or multivariate normal distribution is assumed. The default "loglinear" corresponds to log-linear smoothing of the skillspace distribution (Xu & von Davier, 2008). If skillspace="full", then all probabilities of the skill space are nonparametrically estimated. If skillspace="est", then the $\bold{\theta}$ distribution vectors will be estimated (see Details and Examples 4 and 5; Bartolucci, 2007).
b.constraint: In this optional matrix with $C_b$ rows and three columns, $C_b$ item intercepts $b_{ik}$ can be fixed. 1st column: item index, 2nd column: category index, 3rd column: fixed item thresholds
a.constraint: In this optional matrix with $C_a$ rows and four columns, $C_a$ item intercepts $a_{idk}$ can be fixed. 1st column: item index, 2nd column: dimension index, 3rd column: category index, 4th column: fixed item slopes
mean.constraint: A $C \times 3$ matrix for constraining $C$ means in the normal distribution assumption (skillspace="normal"). 1st column: Dimension, 2nd column: Group, 3rd column: Value
Sigma.constraint: A $C \times 4$ matrix for constraining $C$ covariances in the normal distribution assumption (skillspace="normal"). 1st column: Dimension 1, 2nd column: Dimension 2, 3rd column: Group, 4th column: Value
delta.designmatrix: The design matrix of $\delta$ parameters for the reduced skillspace estimation (see Xu & von Davier, 2008)
standardized.latent: A logical indicating whether in a uni- or multidimensional model all latent variables of the first group should be normally distributed and standardized. The default is FALSE.
centered.latent: A logical indicating whether in a uni- or multidimensional model all latent variables of the first group should be normally distributed and do have zero means? The default is FALSE.
centerintercepts: A logical indicating whether intercepts should be centered to have a mean of 0 for all dimensions. This argument does not (yet) work properly for varying numbers of item categories.
centerslopes: A logical indicating whether item slopes should be centered to have a mean of 1 for all dimensions. This argument only works for irtmodel="2PL". The default is FALSE.
maxiter: Maximum number of iterations
conv: Convergence criterion for item parameters and distribution parameters
globconv: Global deviance convergence criterion
msteps: Maximum number of M steps in estimating $b$ and $a$ item parameters. The default is to use 4 M steps.
convM: Convergence criterion in M step
decrease.increments: Should in the M step the increments of $a$ and $b$ parameters decrease during iterations? The default is FALSE. If there is an increase in deviance during estimation, setting decrease.increments to TRUE is recommended.
use.freqpatt: A logical indicating whether frequencies of unique item response patterns should be used. In case of large data set use.freqpatt=TRUE can speed calculations (depending on the problem). Note that in this case, not all person parameters are calculated as usual in the output.
progress: An optional logical indicating whether the function should print the progress of iteration in the estimation process.
PEM: Logical indicating whether the P-EM acceleration should be applied (Berlinet & Roland, 2012).
PEM_itermax: Number of iterations in which the P-EM method should be applied.
object: A required object of class gdm
file: Optional file name for a file in which summary should be sinked.
x: A required object of class gdm
perstype: Person parameter estimate type. Can be either "EAP", "MAP" or "MLE".
barwidth: Bar width in plot.gdm
histcol: Color of histogram bars in plot.gdm
cexcor: Font size for print of correlation in plot.gdm
pchpers: Point type for scatter plot of person parameters in plot.gdm
cexpers: Point size for scatter plot of person parameters in plot.gdm
...: Optional parameters to be passed to or from other methods will be ignored.

Details

Case irtmodel="1PL":
Equal item slopes of 1 are assumed in this model. Therefore, it corresponds to a generalized multidimensional Rasch model. $$logit P( X_{nj}=k | \theta_n )=b_{j0} + \sum_d q_{jdk} \theta_{nd} $$ The Q-matrix entries $q_{jdk}$ are pre-specified by the user.

Case irtmodel="2PL":
For each item and each dimension, different item slopes $a_{jd}$ are estimated: $$logit P( X_{nj}=k | \theta_n )=b_{j0} + \sum_d a_{jd} q_{jdk} \theta_{nd} $$ Case irtmodel="2PLcat":
For each item, each dimension and each category, different item slopes $a_{jdk}$ are estimated: $$logit P( X_{nj}=k | \theta_n )=b_{j0} + \sum_d a_{jdk} q_{jdk} \theta_{nd} $$

Note that this model can be generalized to include terms of any transformation $t_h$ of the $\theta_n$ vector (e.g. quadratic terms, step functions or interaction) such that the model can be formulated as $$logit P( X_{nj}=k | \theta_n )=b_{j0} + \sum_h a_{jhk} q_{jhk} t_h( \theta_{n} ) $$ In general, the number of functions $t_1, ..., t_H$ will be larger than the $\theta$ dimension of $D$.

The estimation follows an EM algorithm as described in von Davier and Yamamoto (2004) and von Davier (2008).

In case of skillspace="est", the $\bold{\theta}$ vectors (the grid of the theta distribution) are estimated (Bartolucci, 2007; Bacci, Bartolucci & Gnaldi, 2012). This model is called a multidimensional latent class item response model.

References

Bacci, S., Bartolucci, F., & Gnaldi, M. (2012). A class of multidimensional latent class IRT models for ordinal polytomous item responses. arXiv preprint, arXiv:1201.4667.

Bartolucci, F. (2007). A class of multidimensional IRT models for testing unidimensionality and clustering items. Psychometrika, 72, 141-157.

Berlinet, A. F., & Roland, C. (2012). Acceleration of the EM algorithm: P-EM versus epsilon algorithm. Computational Statistics & Data Analysis, 56(12), 4122-4137.

von Davier, M. (2008). A general diagnostic model applied to language testing data. British Journal of Mathematical and Statistical Psychology, 61, 287-307.

von Davier, M., & Yamamoto, K. (2004). Partially observed mixtures of IRT models: An extension of the generalized partial-credit model. Applied Psychological Measurement, 28, 389-406.

Xu, X., & von Davier, M. (2008). Fitting the structured general diagnostic model to NAEP data. ETS Research Report ETS RR-08-27. Princeton, ETS.

Examples

Run this code

#############################################################################
# EXAMPLE 1: Fraction Dataset 1
#      Unidimensional Models for dichotomous data
#############################################################################

data(data.fraction1, package="CDM")
dat <- data.fraction1$data
theta.k <- seq( -6, 6, len=15 )   # discretized ability

#***
# Model 1: Rasch model (normal distribution)
mod1 <- CDM::gdm( dat, irtmodel="1PL", theta.k=theta.k, skillspace="normal",
               centered.latent=TRUE)
summary(mod1)
plot(mod1)

#***
# Model 2: Rasch model (log-linear smoothing)
# set the item difficulty of the 8th item to zero
b.constraint <- matrix( c(8,1,0), 1, 3 )
mod2 <- CDM::gdm( dat, irtmodel="1PL", theta.k=theta.k,
               skillspace="loglinear", b.constraint=b.constraint  )
summary(mod2)

#***
# Model 3: 2PL model
mod3 <- CDM::gdm( dat, irtmodel="2PL", theta.k=theta.k,
               skillspace="normal", standardized.latent=TRUE  )
summary(mod3)

if (FALSE) {
#***
# Model 4: include quadratic term in item response function
#   using the argument decrease.increments=TRUE leads to a more
#   stable estimate
thetaDes <- cbind( theta.k, theta.k^2 )
colnames(thetaDes) <- c( "F1", "F1q" )
mod4 <- CDM::gdm( dat, irtmodel="2PL", theta.k=theta.k,
          thetaDes=thetaDes, skillspace="normal",
          standardized.latent=TRUE, decrease.increments=TRUE)
summary(mod4)

#***
# Model 5: step function for ICC
#          two different probabilities theta < 0 and theta > 0
thetaDes <- matrix( 1*(theta.k>0), ncol=1 )
colnames(thetaDes) <- c( "Fgrm1" )
mod5 <- CDM::gdm( dat, irtmodel="2PL", theta.k=theta.k,
          thetaDes=thetaDes, skillspace="normal" )
summary(mod5)

#***
# Model 6: DINA model with din function
mod6 <- CDM::din( dat, q.matrix=matrix( 1, nrow=ncol(dat),ncol=1 ) )
summary(mod6)

#***
# Model 7: Estimating a version of the DINA model with gdm
theta.k <- c(-.5,.5)
mod7 <- CDM::gdm( dat, irtmodel="2PL", theta.k=theta.k, skillspace="loglinear" )
summary(mod7)

#############################################################################
# EXAMPLE 2: Cultural Activities - data.Students
#      Unidimensional Models for polytomous data
#############################################################################

data(data.Students, package="CDM")
dat <- data.Students

dat <- dat[, grep( "act", colnames(dat) ) ]
theta.k <- seq( -4, 4, len=11 )   # discretized ability

#***
# Model 1: Partial Credit Model (PCM)
mod1 <- CDM::gdm( dat, irtmodel="1PL", theta.k=theta.k, skillspace="normal",
           centered.latent=TRUE)
summary(mod1)
plot(mod1)

#***
# Model 1b: PCM using frequency patterns
mod1b <- CDM::gdm( dat, irtmodel="1PL", theta.k=theta.k, skillspace="normal",
           centered.latent=TRUE, use.freqpatt=TRUE)
summary(mod1b)

#***
# Model 2: PCM with two groups
mod2 <- CDM::gdm( dat, irtmodel="1PL", theta.k=theta.k,
            group=CDM::data.Students$urban + 1, skillspace="normal",
            centered.latent=TRUE)
summary(mod2)

#***
# Model 3: PCM with loglinear smoothing
b.constraint <- matrix( c(1,2,0), ncol=3 )
mod3 <- CDM::gdm( dat, irtmodel="1PL", theta.k=theta.k,
    skillspace="loglinear", b.constraint=b.constraint )
summary(mod3)

#***
# Model 4: Model with pre-specified item weights in Q-matrix
Qmatrix <- array( 1, dim=c(5,1,2) )
Qmatrix[,1,2] <- 2     # default is score 2 for category 2
# now change the scoring of category 2:
Qmatrix[c(2,4),1,1] <- .74
Qmatrix[c(2,4),1,2] <- 2.3
# for items 2 and 4 the score for category 1 is .74 and for category 2 it is 2.3
mod4 <- CDM::gdm( dat, irtmodel="1PL", theta.k=theta.k, Qmatrix=Qmatrix,
           skillspace="normal", centered.latent=TRUE)
summary(mod4)

#***
# Model 5: Generalized partial credit model
mod5 <- CDM::gdm( dat, irtmodel="2PL", theta.k=theta.k,
          skillspace="normal", standardized.latent=TRUE )
summary(mod5)

#***
# Model 6: Item-category slope estimation
mod6 <- CDM::gdm( dat, irtmodel="2PLcat", theta.k=theta.k,  skillspace="normal",
                 standardized.latent=TRUE, decrease.increments=TRUE)
summary(mod6)

#***
# Models 7: items with different number of categories
dat0 <- dat
dat0[ paste(dat0[,1])==2, 1 ] <- 1 # 1st item has only two categories
dat0[ paste(dat0[,3])==2, 3 ] <- 1 # 3rd item has only two categories

# Model 7a: PCM
mod7a <- CDM::gdm( dat0, irtmodel="1PL", theta.k=theta.k,  centered.latent=TRUE )
summary(mod7a)

# Model 7b: Item category slopes
mod7b <- CDM::gdm( dat0, irtmodel="2PLcat", theta.k=theta.k,
                 standardized.latent=TRUE, decrease.increments=TRUE )
summary(mod7b)

#############################################################################
# EXAMPLE 3: Fraction Dataset 2
#      Multidimensional Models for dichotomous data
#############################################################################

data(data.fraction2, package="CDM")
dat <- data.fraction2$data
Qmatrix <- data.fraction2$q.matrix3

#***
# Model 1: One-dimensional Rasch model
theta.k <- seq( -4, 4, len=11 )   # discretized ability
mod1 <- CDM::gdm( dat, irtmodel="1PL", theta.k=theta.k,  centered.latent=TRUE)
summary(mod1)
plot(mod1)

#***
# Model 2: One-dimensional 2PL model
mod2 <- CDM::gdm( dat, irtmodel="2PL", theta.k=theta.k, standardized.latent=TRUE)
summary(mod2)
plot(mod2)

#***
# Model 3: 3-dimensional Rasch Model (normal distribution)
mod3 <- CDM::gdm( dat, irtmodel="1PL", theta.k=theta.k, Qmatrix=Qmatrix,
            centered.latent=TRUE,  globconv=5*1E-3, conv=1E-4 )
summary(mod3)

#***
# Model 4: 3-dimensional Rasch model (loglinear smoothing)
# set some item parameters of items 4,1 and 2 to zero
b.constraint <- cbind( c(4,1,2), 1, 0 )
mod4 <- CDM::gdm( dat, irtmodel="1PL", theta.k=theta.k, Qmatrix=Qmatrix,
              b.constraint=b.constraint, skillspace="loglinear" )
summary(mod4)

#***
# Model 5: define a different theta grid for each dimension
theta.k <- list( "Dim1"=seq( -5, 5, len=11 ),
                 "Dim2"=seq(-5,5,len=8),
                 "Dim3"=seq( -3,3,len=6) )
mod5 <- CDM::gdm( dat, irtmodel="1PL", theta.k=theta.k, Qmatrix=Qmatrix,
                 b.constraint=b.constraint,  skillspace="loglinear")
summary(mod5)

#***
# Model 6: multdimensional 2PL model (normal distribution)
theta.k <- seq( -5, 5, len=13 )
a.constraint <- cbind( c(8,1,3), 1:3, 1, 1 ) # fix some slopes to 1
mod6 <- CDM::gdm( dat, irtmodel="2PL", theta.k=theta.k,  Qmatrix=Qmatrix,
            centered.latent=TRUE, a.constraint=a.constraint, decrease.increments=TRUE,
            skillspace="normal")
summary(mod6)

#***
# Model 7: multdimensional 2PL model (loglinear distribution)
a.constraint <- cbind( c(8,1,3), 1:3, 1, 1 )
b.constraint <- cbind( c(8,1,3), 1, 0 )
mod7 <- CDM::gdm( dat, irtmodel="2PL", theta.k=theta.k,  Qmatrix=Qmatrix,
               b.constraint=b.constraint,  a.constraint=a.constraint,
               decrease.increments=FALSE, skillspace="loglinear")
summary(mod7)

#############################################################################
# EXAMPLE 4: Unidimensional latent class 1PL IRT model
#############################################################################

# simulate data
set.seed(754)
I <- 20     # number of items
N <- 2000   # number of persons
theta <- c( -2, 0, 1, 2 )
theta <- rep( theta, c(N/4,N/4, 3*N/8, N/8)  )
b <- seq(-2,2,len=I)
library(sirt)   # use function sim.raschtype from sirt package
dat <- sirt::sim.raschtype( theta=theta, b=b )

theta.k <- seq(-1, 1, len=4)      # initial vector of theta
# estimate model
mod1 <- CDM::gdm( dat, theta.k=theta.k, skillspace="est", irtmodel="1PL",
            centerintercepts=TRUE, maxiter=200)
summary(mod1)
  ##   Estimated Skill Distribution
  ##         F1    pi.k
  ##   1 -1.988 0.24813
  ##   2 -0.055 0.23313
  ##   3  0.940 0.40059
  ##   4  2.000 0.11816

#############################################################################
# EXAMPLE 5: Multidimensional latent class IRT model
#############################################################################

# We simulate a two-dimensional IRT model in which theta vectors
# are observed at a fixed discrete grid (see below).

# simulate data
set.seed(754)
I <- 13     # number of items
N <- 2400   # number of persons

# simulate Dimension 1 at 4 discrete theta points
theta <- c( -2, 0, 1, 2 )
theta <- rep( theta, c(N/4,N/4, 3*N/8, N/8)  )
b <- seq(-2,2,len=I)
library(sirt)  # use simulation function from sirt package
dat1 <- sirt::sim.raschtype( theta=theta, b=b )
# simulate Dimension 2 at 4 discrete theta points
theta <- c( -3, 0, 1.5, 2 )
theta <- rep( theta, c(N/4,N/4, 3*N/8, N/8)  )
dat2 <- sirt::sim.raschtype( theta=theta, b=b )
colnames(dat2) <- gsub( "I", "U", colnames(dat2))
dat <- cbind( dat1, dat2 )

# define Q-matrix
Qmatrix <- matrix(0,2*I,2)
Qmatrix[ cbind( 1:(2*I), rep(1:2, each=I) ) ] <- 1

theta.k <- seq(-1, 1, len=4)      # initial matrix
theta.k <- cbind( theta.k, theta.k )
colnames(theta.k) <- c("Dim1","Dim2")

# estimate model
mod2 <- CDM::gdm( dat, theta.k=theta.k, skillspace="est", irtmodel="1PL",
              Qmatrix=Qmatrix, centerintercepts=TRUE)
summary(mod2)
  ##   Estimated Skill Distribution
  ##     theta.k.Dim1 theta.k.Dim2    pi.k
  ##   1       -2.022       -3.035 0.25010
  ##   2        0.016        0.053 0.24794
  ##   3        0.956        1.525 0.36401
  ##   4        1.958        1.919 0.13795

#############################################################################
# EXAMPLE 6: Large-scale dataset data.mg
#############################################################################

data(data.mg, package="CDM")
dat <- data.mg[, paste0("I", 1:11 ) ]
theta.k <- seq(-6,6,len=21)

#***
# Model 1: Generalized partial credit model with multiple groups
mod1 <- CDM::gdm( dat, irtmodel="2PL", theta.k=theta.k, group=CDM::data.mg$group,
              skillspace="normal", standardized.latent=TRUE)
summary(mod1)
}

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