din: Parameter Estimation for Mixed DINA/DINO Model
In CDM: Cognitive Diagnosis Modeling
din R Documentation
Parameter Estimation for Mixed DINA/DINO Model
Description
din provides parameter estimation for cognitive
diagnosis models of the types “DINA”, “DINO” and “mixed DINA
and DINO”.
Usage
din(data, q.matrix, skillclasses=NULL,
conv.crit=0.001, dev.crit=10^(-5), maxit=500,
constraint.guess=NULL, constraint.slip=NULL,
guess.init=rep(0.2, ncol(data)), slip.init=guess.init,
guess.equal=FALSE, slip.equal=FALSE, zeroprob.skillclasses=NULL,
weights=rep(1, nrow(data)), rule="DINA",
wgt.overrelax=0, wgtest.overrelax=FALSE, param.history=FALSE,
seed=0, progress=TRUE, guess.min=0, slip.min=0, guess.max=1, slip.max=1)
## S3 method for class 'din'
print(x, ...)
Arguments
data
A required N \times J data matrix
containing the binary responses, 0 or 1, of N respondents to J
test items, where 1 denotes a correct response and 0 an incorrect one. The
nth row of the matrix represents the binary response pattern of respondent
n. NA values are allowed.
q.matrix
A required binary J \times K containing the
attributes not required or required, 0 or 1, to master the items. The
jth row of the matrix is a binary indicator vector indicating which
attributes are not required (coded by 0) and which attributes are
required (coded by 1) to master item j.
skillclasses
An optional matrix for determining the skill space.
The argument can be used if a user wants less than 2^K skill classes.
conv.crit
A numeric which defines the termination criterion
of iterations in the parameter
estimation process. Iteration ends if the maximal change in parameter
estimates is below this value.
dev.crit
A numeric value which defines the termination criterion
of iterations in relative change in deviance.
maxit
An integer which defines the maximum number
of iterations in the estimation process.
constraint.guess
An optional matrix of fixed guessing
parameters. The first column of this matrix indicates the numbers of the
items whose guessing parameters are fixed and the second
column the values the guessing parameters are fixed to.
constraint.slip
An optional matrix of fixed slipping
parameters. The first column of this matrix indicates the numbers of the
items whose slipping parameters are fixed and the second column
the values the slipping parameters are fixed to.
guess.init
An optional initial vector of guessing parameters.
Guessing parameters are bounded between 0 and 1.
slip.init
An optional initial vector of slipping parameters.
Slipping parameters are bounded between 0 and 1.
guess.equal
An optional logical indicating if all guessing parameters
are equal to each other. Default is FALSE.
slip.equal
An optional logical indicating if all slipping parameters
are equal to each other. Default is FALSE.
zeroprob.skillclasses
An optional vector of integers which indicates
which skill classes should have zero probability. Default is NULL
(no skill classes with zero probability).
weights
An optional vector of weights for the response pattern.
Non-integer weights allow for different sampling schemes.
rule
An optional character string or vector of character strings
specifying the model rule that is used. The character strings must be
of "DINA" or "DINO". If a vector of character strings is
specified, implying an item wise condensation rule, the vector must
be of length J, which is the number of items. The default is
the condensation rule "DINA" for all items.
wgt.overrelax
A parameter which is relevant when an overrelaxation
algorithm is used
wgtest.overrelax
A logical which indicates if the overrelexation
parameter being estimated during iterations
param.history
A logical which indicates if the parameter history during
iterations should be saved. The default is FALSE.
seed
Simulation seed for initial parameters. A value of zero corresponds
to deterministic starting values, an integer value different from
zero to random initial values with set.seed(seed).
progress
An optional logical indicating whether the function
should print the progress of iteration in the estimation process.
guess.min
Minimum value of guessing parameters to be estimated.
slip.min
Minimum value of slipping parameters to be estimated.
guess.max
Maximum value of guessing parameters to be estimated.
slip.max
Maximum value of slipping parameters to be estimated.
x
Object of class din
...
Further arguments to be passed
Details
In the CDM DINA (deterministic-input, noisy-and-gate; de la Torre &
Douglas, 2004) and DINO (deterministic-input, noisy-or-gate; Templin &
Henson, 2006) models endorsement probabilities are modeled
based on guessing and slipping parameters, given the different skill
classes. The probability of respondent n (or corresponding respondents class n)
for solving item j is calculated as a function of the
respondent's latent response eta_{nj}
and the guessing and slipping rates g_j and s_j for item
j conditional on the respondent's skill class alpha_n:
P(X_{nj}=1 | α_n)=g_j^{(1- η_{nj})}(1 - s_j) ^{η_{nj}}.
The respondent's latent response (class) eta_{nj} is a binary number,
0 or 1, where 1 indicates presence of all (rule="DINO")
or at least one (rule="DINO") required skill(s) for
item j, respectively.
DINA and DINO parameter estimation is performed by
maximization of the marginal likelihood of the data. The
a priori distribution of the skill vectors is a uniform distribution.
The implementation follows the EM algorithm by de la Torre (2009).
The function din returns an object of the class
din (see ‘Value’), for which plot,
print, and summary methods are provided;
plot.din, print.din, and
summary.din, respectively.
Value
coef
Estimated model parameters. Note that only freely
estimated parameters are included.
item
A data frame giving for each item condensation rule, the
estimated guessing and slipping parameters and their standard errors.
All entries are rounded to 3 digits.
guess
A data frame giving the estimated guessing parameters
and their standard errors for each item.
slip
A data frame giving the estimated slipping parameters
and their standard errors for each item.
IDI
A matrix giving the item discrimination
index (IDI; Lee, de la Torre & Park, 2012) for each item j
IDI_j=1 - s_j - g_j,
where a high IDI corresponds to good test items
which have both low guessing and slipping rates. Note that
a negative IDI indicates violation of the monotonicity condition
g_j < 1 - s_j. See din for help.
itemfit.rmsea
The RMSEA item fit index (see itemfit.rmsea).
mean.rmsea
Mean of RMSEA item fit indexes.
loglike
A numeric giving the value of the maximized
log likelihood.
AIC
A numeric giving the AIC value of the model.
BIC
A numeric giving the BIC value of the model.
Npars
Number of estimated parameters
posterior
A matrix given the posterior skill distribution
for all respondents. The nth row of the matrix gives the probabilities for
respondent n to possess any of the 2^K skill classes.
like
A matrix giving the values of the maximized likelihood
for all respondents.
data
The input matrix of binary response data.
q.matrix
The input matrix of the required attributes.
pattern
A matrix giving the skill classes leading to highest endorsement
probability for the respective response pattern (mle.est) with the
corresponding posterior class probability (mle.post), the attribute
classes having the highest occurrence posterior probability given the
response pattern (map.est) with the corresponding posterior class
probability (map.post), and the estimated posterior for each
response pattern (pattern).
attribute.patt
A data frame giving the estimated occurrence
probabilities of the skill classes and the expected frequency of the
attribute classes given the model.
skill.patt
A matrix given the population prevalences of the
skills.
subj.pattern
A vector of strings indicating the item response
pattern for each subject.
attribute.patt.splitted
A dataframe giving the skill class
of the respondents.
display
A character giving the model specified under
rule.
item.patt.split
A matrix giving the splitted response pattern.
item.patt.freq
A numeric vector given the frequencies of the response
pattern in item.patt.split.
seed
Used simulation seed for initial parameters
partable
Parameter table which is used for coef and vcov.
vcov.derived
Design matrix for extended set of parameters in
vcov.
converged
Logical indicating whether convergence was achieved.
control
Optimization parameters used in estimation
Note
The calculation of standard errors using sampling weights which
represent multistage sampling schemes is not correct. Please use
replication methods (like Jackknife) instead.
References
de la Torre, J. (2009). DINA model parameter estimation:
A didactic. Journal of Educational and Behavioral
Statistics, 34, 115–130.
de la Torre, J., & Douglas, J. (2004). Higher-order latent trait models
for cognitive diagnosis. Psychometrika, 69, 333–353.
Lee, Y.-S., de la Torre, J., & Park, Y. S. (2012).
Relationships between cognitive diagnosis, CTT, and IRT indices:
An empirical investigation. Asia Pacific Educational Research, 13, 333-345.
Rupp, A. A., Templin, J., & Henson, R. A. (2010). Diagnostic
Measurement: Theory, Methods, and Applications. New York: The Guilford
Press.
Templin, J., & Henson, R. (2006). Measurement of
psychological disorders using cognitive diagnosis
models. Psychological Methods, 11, 287–305.
See Also
plot.din, the S3 method for plotting objects of
the class din; print.din, the S3 method
for printing objects of the class din;
summary.din, the S3 method for summarizing objects
of the class din, which creates objects of the class
summary.din; din, the main function for
DINA and DINO parameter estimation, which creates objects of the class
din.
See the gdina function for the estimation of
the generalized DINA (GDINA) model.
For assessment of model fit see modelfit.cor.din and
anova.din.
See itemfit.sx2 for item fit statistics.
See discrim.index for computing discrimination indices.
See also CDM-package for general
information about this package.
See the NPCD::JMLE function in the NPCD package for
joint maximum likelihood estimation
of the DINA, DINO and NIDA model.
See the dina::DINA_Gibbs function in the dina
package for MCMC based estimation of the DINA model.
Examples
#############################################################################
# EXAMPLE 1: Examples based on dataset fractions.subtraction.data
#############################################################################
## dataset fractions.subtraction.data and corresponding Q-Matrix
head(fraction.subtraction.data)
fraction.subtraction.qmatrix
## Misspecification in parameter specification for method CDM::din()
## leads to warnings and terminates estimation procedure. E.g.,
# See Q-Matrix specification
fractions.dina.warning1 <- CDM::din(data=fraction.subtraction.data,
q.matrix=t(fraction.subtraction.qmatrix))
# See guess.init specification
fractions.dina.warning2 <- CDM::din(data=fraction.subtraction.data,
q.matrix=fraction.subtraction.qmatrix, guess.init=rep(1.2,
ncol(fraction.subtraction.data)))
# See rule specification
fractions.dina.warning3 <- CDM::din(data=fraction.subtraction.data,
q.matrix=fraction.subtraction.qmatrix, rule=c(rep("DINA",
10), rep("DINO", 9)))
## Parameter estimation of DINA model
# rule="DINA" is default
fractions.dina <- CDM::din(data=fraction.subtraction.data,
q.matrix=fraction.subtraction.qmatrix, rule="DINA")
attributes(fractions.dina)
str(fractions.dina)
## For instance assessing the guessing parameters through
## assignment
fractions.dina$guess
## corresponding summaries, including IDI,
## most frequent skill classes and information
## criteria AIC and BIC
summary(fractions.dina)
## In particular, assessing detailed summary through assignment
detailed.summary.fs <- summary(fractions.dina)
str(detailed.summary.fs)
## Item discrimination index of item 8 is too low. This is also
## visualized in the first plot
plot(fractions.dina)
## The reason therefore is a high guessing parameter
round(fractions.dina$guess[,1], 2)
## Estimate DINA model with different random initial parameters using seed=1345
fractions.dina1 <- CDM::din(data=fraction.subtraction.data,
q.matrix=fraction.subtraction.qmatrix, rule="DINA", seed=1345)
## Fix the guessing parameters of items 5, 8 and 9 equal to .20
# define a constraint.guess matrix
constraint.guess <- matrix(c(5,8,9, rep(0.2, 3)), ncol=2)
fractions.dina.fixed <- CDM::din(data=fraction.subtraction.data,
q.matrix=fraction.subtraction.qmatrix,
constraint.guess=constraint.guess)
## The second plot shows the expected (MAP) and observed skill
## probabilities. The third plot visualizes the skill class
## occurrence probabilities; Only the 'top.n.skill.classes' most frequent
## skill classes are labeled; it is obvious that the skill class '11111111'
## (all skills are mastered) is the most probable in this population.
## The fourth plot shows the skill probabilities conditional on response
## patterns; in this population the skills 3 and 6 seem to be
## mastered easier than the others. The fourth plot shows the
## skill probabilities conditional on a specified response
## pattern; it is shown whether a skill is mastered (above
## .5+'uncertainty') unclassifiable (within the boundaries) or
## not mastered (below .5-'uncertainty'). In this case, the
## 527th respondent was chosen; if no response pattern is
## specified, the plot will not be shown (of course)
pattern <- paste(fraction.subtraction.data[527, ], collapse="")
plot(fractions.dina, pattern=pattern, display.nr=4)
#uncertainty=0.1, top.n.skill.classes=6 are default
plot(fractions.dina.fixed, uncertainty=0.1, top.n.skill.classes=6,
pattern=pattern)
## Not run:
#############################################################################
# EXAMPLE 2: Examples based on dataset sim.dina
#############################################################################
# DINA Model
d1 <- CDM::din(sim.dina, q.matr=sim.qmatrix, rule="DINA",
conv.crit=0.01, maxit=500, progress=TRUE)
summary(d1)
# DINA model with hierarchical skill classes (Hierarchical DINA model)
# 1st step: estimate an initial full model to look at the indexing
# of skill classes
d0 <- CDM::din(sim.dina, q.matr=sim.qmatrix, maxit=1)
d0$attribute.patt.splitted
# [,1] [,2] [,3]
# [1,] 0 0 0
# [2,] 1 0 0
# [3,] 0 1 0
# [4,] 0 0 1
# [5,] 1 1 0
# [6,] 1 0 1
# [7,] 0 1 1
# [8,] 1 1 1
#
# In this example, following hierarchical skill classes are only allowed:
# 000, 001, 011, 111
# We define therefore a vector of indices for skill classes with
# zero probabilities (see entries in the rows of the matrix
# d0$attribute.patt.splitted above)
zeroprob.skillclasses <- c(2,3,5,6) # classes 100, 010, 110, 101
# estimate the hierarchical DINA model
d1a <- CDM::din(sim.dina, q.matr=sim.qmatrix,
zeroprob.skillclasses=zeroprob.skillclasses )
summary(d1a)
# Mixed DINA and DINO Model
d1b <- CDM::din(sim.dina, q.matr=sim.qmatrix, rule=
c(rep("DINA", 7), rep("DINO", 2)), conv.crit=0.01,
maxit=500, progress=FALSE)
summary(d1b)
# DINO Model
d2 <- CDM::din(sim.dina, q.matr=sim.qmatrix, rule="DINO",
conv.crit=0.01, maxit=500, progress=FALSE)
summary(d2)
# Comparison of DINA and DINO estimates
lapply(list("guessing"=rbind("DINA"=d1$guess[,1],
"DINO"=d2$guess[,1]), "slipping"=rbind("DINA"=
d1$slip[,1], "DINO"=d2$slip[,1])), round, 2)
# Comparison of the information criteria
c("DINA"=d1$AIC, "MIXED"=d1b$AIC, "DINO"=d2$AIC)
# following estimates:
d1$coef # guessing and slipping parameter
d1$guess # guessing parameter
d1$slip # slipping parameter
d1$skill.patt # probabilities for skills
d1$attribute.patt # skill classes with probabilities
d1$subj.pattern # pattern per subject
# posterior probabilities for every response pattern
d1$posterior
# Equal guessing parameters
d2a <- CDM::din( data=sim.dina, q.matrix=sim.qmatrix,
guess.equal=TRUE, slip.equal=FALSE )
d2a$coef
# Equal guessing and slipping parameters
d2b <- CDM::din( data=sim.dina, q.matrix=sim.qmatrix,
guess.equal=TRUE, slip.equal=TRUE )
d2b$coef
#############################################################################
# EXAMPLE 3: Examples based on dataset sim.dino
#############################################################################
# DINO Estimation
d3 <- CDM::din(sim.dino, q.matr=sim.qmatrix, rule="DINO",
conv.crit=0.005, progress=FALSE)
# Mixed DINA and DINO Model
d3b <- CDM::din(sim.dino, q.matr=sim.qmatrix,
rule=c(rep("DINA", 4), rep("DINO", 5)), conv.crit=0.001,
progress=FALSE)
# DINA Estimation
d4 <- CDM::din(sim.dino, q.matr=sim.qmatrix, rule="DINA",
conv.crit=0.005, progress=FALSE)
# Comparison of DINA and DINO estimates
lapply(list("guessing"=rbind("DINO"=d3$guess[,1], "DINA"=d4$guess[,1]),
"slipping"=rbind("DINO"=d3$slip[,1], "DINA"=d4$slip[,1])), round, 2)
# Comparison of the information criteria
c("DINO"=d3$AIC, "MIXED"=d3b$AIC, "DINA"=d4$AIC)
#############################################################################
# EXAMPLE 4: Example estimation with weights based on dataset sim.dina
#############################################################################
# Here, a weighted maximum likelihood estimation is used
# This could be useful for survey data.
# i.e. first 200 persons have weight 2, the other have weight 1
(weights <- c(rep(2, 200), rep(1, 200)))
d5 <- CDM::din(sim.dina, sim.qmatrix, rule="DINA", conv.crit=
0.005, weights=weights, progress=FALSE)
# Comparison of the information criteria
c("DINA"=d1$AIC, "WEIGHTS"=d5$AIC)
#############################################################################
# EXAMPLE 5: Example estimation within a balanced incomplete
## block (BIB) design generated on dataset sim.dina
#############################################################################
# generate BIB data
# The next example shows that the din function works for
# (relatively arbitrary) missing value pattern
# Here, a missing by design is generated in the dataset dinadat.bib
sim.dina.bib <- sim.dina
sim.dina.bib[1:100, 1:3] <- NA
sim.dina.bib[101:300, 4:8] <- NA
sim.dina.bib[301:400, c(1,2,9)] <- NA
d6 <- CDM::din(sim.dina.bib, sim.qmatrix, rule="DINA",
conv.crit=0.0005, weights=weights, maxit=200)
d7 <- CDM::din(sim.dina.bib, sim.qmatrix, rule="DINO",
conv.crit=0.005, weights=weights)
# Comparison of DINA and DINO estimates
lapply(list("guessing"=rbind("DINA"=d6$guess[,1],
"DINO"=d7$guess[,1]), "slipping"=rbind("DINA"=
d6$slip[,1], "DINO"=d7$slip[,1])), round, 2)
#############################################################################
# EXAMPLE 6: DINA model with attribute hierarchy
#############################################################################
set.seed(987)
# assumed skill distribution: P(000)=P(100)=P(110)=P(111)=.245 and
# "deviant pattern": P(010)=.02
K <- 3 # number of skills
# define alpha
alpha <- scan()
0 0 0
1 0 0
1 1 0
1 1 1
0 1 0
alpha <- matrix( alpha, length(alpha)/K, K, byrow=TRUE )
alpha <- alpha[ c( rep(1:4,each=245), rep(5,20) ), ]
# define Q-matrix
q.matrix <- scan()
1 0 0 1 0 0 1 0 0
0 1 0 0 1 0 0 1 0
0 0 1 0 1 0 0 0 1
1 1 0 1 0 1 0 1 1
q.matrix <- matrix( q.matrix, nrow=length(q.matrix)/K, ncol=K, byrow=TRUE )
# simulate DINA data
dat <- CDM::sim.din( alpha=alpha, q.matrix=q.matrix )$dat
#*** Model 1: estimate DINA model | no skill space restriction
mod1 <- CDM::din( dat, q.matrix )
#*** Model 2: DINA model | hierarchy A2 > A3
B <- "A2 > A3"
skill.names <- paste0("A",1:3)
skillspace <- CDM::skillspace.hierarchy( B, skill.names )$skillspace.reduced
mod2 <- CDM::din( dat, q.matrix, skillclasses=skillspace )
#*** Model 3: DINA model | linear hierarchy A1 > A2 > A3
# This is a misspecied model because due to P(010)=.02 the relation A1>A2
# does not hold.
B <- "A1 > A2
A2 > A3"
skill.names <- paste0("A",1:3)
skillspace <- CDM::skillspace.hierarchy( B, skill.names )$skillspace.reduced
mod3 <- CDM::din( dat, q.matrix, skillclasses=skillspace )
#*** Model 4: 2PL model in gdm
mod4 <- CDM::gdm( dat, theta.k=seq(-5,5,len=21),
decrease.increments=TRUE, skillspace="normal" )
summary(mod4)
anova(mod1,mod2)
## Model loglike Deviance Npars AIC BIC Chisq df p
## 2 Model 2 -7052.460 14104.92 29 14162.92 14305.24 0.9174 2 0.63211
## 1 Model 1 -7052.001 14104.00 31 14166.00 14318.14 NA NA NA
anova(mod2,mod3)
## Model loglike Deviance Npars AIC BIC Chisq df p
## 2 Model 2 -7059.058 14118.12 27 14172.12 14304.63 13.19618 2 0.00136
## 1 Model 1 -7052.460 14104.92 29 14162.92 14305.24 NA NA NA
anova(mod2,mod4)
## Model loglike Deviance Npars AIC BIC Chisq df p
## 2 Model 2 -7220.05 14440.10 24 14488.10 14605.89 335.1805 5 0
## 1 Model 1 -7052.46 14104.92 29 14162.92 14305.24 NA NA NA
# compare fit statistics
summary( CDM::modelfit.cor.din( mod2 ) )
summary( CDM::modelfit.cor.din( mod4 ) )
#############################################################################
# EXAMPLE 7: Fitting the basic local independence model (BLIM) with din
#############################################################################
library(pks)
data(DoignonFalmagne7, package="pks")
## str(DoignonFalmagne7)
## $ K : int [1:9, 1:5] 0 1 0 1 1 1 1 1 1 0 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:9] "00000" "10000" "01000" "11000" ...
## .. ..$ : chr [1:5] "a" "b" "c" "d" ...
## $ N.R: Named int [1:32] 80 92 89 3 2 1 89 16 18 10 ...
## ..- attr(*, "names")=chr [1:32] "00000" "10000" "01000" "00100" ...
# The idea is to fit the local independence model with the din function.
# This can be accomplished by specifying a DINO model with
# prespecified skill classes.
# extract dataset
dat <- as.numeric( unlist( sapply( names(DoignonFalmagne7$N.R),
FUN=function( ll){ strsplit( ll, split="") } ) ) )
dat <- matrix( dat, ncol=5, byrow=TRUE )
colnames(dat) <- colnames(DoignonFalmagne7$K)
rownames(dat) <- names(DoignonFalmagne7$N.R)
# sample weights
weights <- DoignonFalmagne7$N.R
# define Q-matrix
q.matrix <- t(DoignonFalmagne7$K)
v1 <- colnames(q.matrix) <- paste0("S", colnames(q.matrix))
q.matrix <- q.matrix[, - 1] # remove S00000
# define skill classes
SC <- ncol(q.matrix)
skillclasses <- matrix( 0, nrow=SC+1, ncol=SC)
colnames(skillclasses) <- colnames(q.matrix)
rownames(skillclasses) <- v1
skillclasses[ cbind( 2:(SC+1), 1:SC ) ] <- 1
# estimate BLIM with din function
mod1 <- CDM::din(data=dat, q.matrix=q.matrix, skillclasses=skillclasses,
rule="DINO", weights=weights )
summary(mod1)
## Item parameters
## item guess slip IDI rmsea
## a a 0.158 0.162 0.680 0.011
## b b 0.145 0.159 0.696 0.009
## c c 0.008 0.181 0.811 0.001
## d d 0.012 0.129 0.859 0.001
## e e 0.025 0.146 0.828 0.007
# estimate basic local independence model with pks package
mod2 <- pks::blim(K, N.R, method="ML") # maximum likelihood estimation by EM algorithm
mod2
## Error and guessing parameters
## beta eta
## a 0.164871 0.103065
## b 0.163113 0.095074
## c 0.188839 0.000004
## d 0.079835 0.000003
## e 0.088648 0.019910
## End(Not run)
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| din | R Documentation |
Parameter Estimation for Mixed DINA/DINO Model
Description
din provides parameter estimation for cognitive
diagnosis models of the types “DINA”, “DINO” and “mixed DINA
and DINO”.
Usage
din(data, q.matrix, skillclasses=NULL,
conv.crit=0.001, dev.crit=10^(-5), maxit=500,
constraint.guess=NULL, constraint.slip=NULL,
guess.init=rep(0.2, ncol(data)), slip.init=guess.init,
guess.equal=FALSE, slip.equal=FALSE, zeroprob.skillclasses=NULL,
weights=rep(1, nrow(data)), rule="DINA",
wgt.overrelax=0, wgtest.overrelax=FALSE, param.history=FALSE,
seed=0, progress=TRUE, guess.min=0, slip.min=0, guess.max=1, slip.max=1)
## S3 method for class 'din'
print(x, ...)
Arguments
data |
A required N \times J data matrix
containing the binary responses, 0 or 1, of N respondents to J
test items, where 1 denotes a correct response and 0 an incorrect one. The
nth row of the matrix represents the binary response pattern of respondent
n. |
q.matrix |
A required binary J \times K containing the attributes not required or required, 0 or 1, to master the items. The jth row of the matrix is a binary indicator vector indicating which attributes are not required (coded by 0) and which attributes are required (coded by 1) to master item j. |
skillclasses |
An optional matrix for determining the skill space. The argument can be used if a user wants less than 2^K skill classes. |
conv.crit |
A numeric which defines the termination criterion of iterations in the parameter estimation process. Iteration ends if the maximal change in parameter estimates is below this value. |
dev.crit |
A numeric value which defines the termination criterion of iterations in relative change in deviance. |
maxit |
An integer which defines the maximum number of iterations in the estimation process. |
constraint.guess |
An optional matrix of fixed guessing parameters. The first column of this matrix indicates the numbers of the items whose guessing parameters are fixed and the second column the values the guessing parameters are fixed to. |
constraint.slip |
An optional matrix of fixed slipping parameters. The first column of this matrix indicates the numbers of the items whose slipping parameters are fixed and the second column the values the slipping parameters are fixed to. |
guess.init |
An optional initial vector of guessing parameters. Guessing parameters are bounded between 0 and 1. |
slip.init |
An optional initial vector of slipping parameters. Slipping parameters are bounded between 0 and 1. |
guess.equal |
An optional logical indicating if all guessing parameters
are equal to each other. Default is |
slip.equal |
An optional logical indicating if all slipping parameters
are equal to each other. Default is |
zeroprob.skillclasses |
An optional vector of integers which indicates
which skill classes should have zero probability. Default is |
weights |
An optional vector of weights for the response pattern. Non-integer weights allow for different sampling schemes. |
rule |
An optional character string or vector of character strings
specifying the model rule that is used. The character strings must be
of |
wgt.overrelax |
A parameter which is relevant when an overrelaxation algorithm is used |
wgtest.overrelax |
A logical which indicates if the overrelexation parameter being estimated during iterations |
param.history |
A logical which indicates if the parameter history during
iterations should be saved. The default is |
seed |
Simulation seed for initial parameters. A value of zero corresponds
to deterministic starting values, an integer value different from
zero to random initial values with |
progress |
An optional logical indicating whether the function should print the progress of iteration in the estimation process. |
guess.min |
Minimum value of guessing parameters to be estimated. |
slip.min |
Minimum value of slipping parameters to be estimated. |
guess.max |
Maximum value of guessing parameters to be estimated. |
slip.max |
Maximum value of slipping parameters to be estimated. |
x |
Object of class |
... |
Further arguments to be passed |
Details
In the CDM DINA (deterministic-input, noisy-and-gate; de la Torre & Douglas, 2004) and DINO (deterministic-input, noisy-or-gate; Templin & Henson, 2006) models endorsement probabilities are modeled based on guessing and slipping parameters, given the different skill classes. The probability of respondent n (or corresponding respondents class n) for solving item j is calculated as a function of the respondent's latent response eta_{nj} and the guessing and slipping rates g_j and s_j for item j conditional on the respondent's skill class alpha_n:
P(X_{nj}=1 | α_n)=g_j^{(1- η_{nj})}(1 - s_j) ^{η_{nj}}.
The respondent's latent response (class) eta_{nj} is a binary number,
0 or 1, where 1 indicates presence of all (rule="DINO")
or at least one (rule="DINO") required skill(s) for
item j, respectively.
DINA and DINO parameter estimation is performed by maximization of the marginal likelihood of the data. The a priori distribution of the skill vectors is a uniform distribution. The implementation follows the EM algorithm by de la Torre (2009).
The function din returns an object of the class
din (see ‘Value’), for which plot,
print, and summary methods are provided;
plot.din, print.din, and
summary.din, respectively.
Value
coef |
Estimated model parameters. Note that only freely estimated parameters are included. |
item |
A data frame giving for each item condensation rule, the estimated guessing and slipping parameters and their standard errors. All entries are rounded to 3 digits. |
guess |
A data frame giving the estimated guessing parameters and their standard errors for each item. |
slip |
A data frame giving the estimated slipping parameters and their standard errors for each item. |
IDI |
A matrix giving the item discrimination index (IDI; Lee, de la Torre & Park, 2012) for each item j IDI_j=1 - s_j - g_j, where a high IDI corresponds to good test items
which have both low guessing and slipping rates. Note that
a negative IDI indicates violation of the monotonicity condition
g_j < 1 - s_j. See |
itemfit.rmsea |
The RMSEA item fit index (see |
mean.rmsea |
Mean of RMSEA item fit indexes. |
loglike |
A numeric giving the value of the maximized log likelihood. |
AIC |
A numeric giving the AIC value of the model. |
BIC |
A numeric giving the BIC value of the model. |
Npars |
Number of estimated parameters |
posterior |
A matrix given the posterior skill distribution for all respondents. The nth row of the matrix gives the probabilities for respondent n to possess any of the 2^K skill classes. |
like |
A matrix giving the values of the maximized likelihood for all respondents. |
data |
The input matrix of binary response data. |
q.matrix |
The input matrix of the required attributes. |
pattern |
A matrix giving the skill classes leading to highest endorsement
probability for the respective response pattern ( |
attribute.patt |
A data frame giving the estimated occurrence probabilities of the skill classes and the expected frequency of the attribute classes given the model. |
skill.patt |
A matrix given the population prevalences of the skills. |
subj.pattern |
A vector of strings indicating the item response pattern for each subject. |
attribute.patt.splitted |
A dataframe giving the skill class of the respondents. |
display |
A character giving the model specified under
|
item.patt.split |
A matrix giving the splitted response pattern. |
item.patt.freq |
A numeric vector given the frequencies of the response
pattern in |
seed |
Used simulation seed for initial parameters |
partable |
Parameter table which is used for |
vcov.derived |
Design matrix for extended set of parameters in
|
converged |
Logical indicating whether convergence was achieved. |
control |
Optimization parameters used in estimation |
Note
The calculation of standard errors using sampling weights which represent multistage sampling schemes is not correct. Please use replication methods (like Jackknife) instead.
References
de la Torre, J. (2009). DINA model parameter estimation: A didactic. Journal of Educational and Behavioral Statistics, 34, 115–130.
de la Torre, J., & Douglas, J. (2004). Higher-order latent trait models for cognitive diagnosis. Psychometrika, 69, 333–353.
Lee, Y.-S., de la Torre, J., & Park, Y. S. (2012). Relationships between cognitive diagnosis, CTT, and IRT indices: An empirical investigation. Asia Pacific Educational Research, 13, 333-345.
Rupp, A. A., Templin, J., & Henson, R. A. (2010). Diagnostic Measurement: Theory, Methods, and Applications. New York: The Guilford Press.
Templin, J., & Henson, R. (2006). Measurement of psychological disorders using cognitive diagnosis models. Psychological Methods, 11, 287–305.
See Also
plot.din, the S3 method for plotting objects of
the class din; print.din, the S3 method
for printing objects of the class din;
summary.din, the S3 method for summarizing objects
of the class din, which creates objects of the class
summary.din; din, the main function for
DINA and DINO parameter estimation, which creates objects of the class
din.
See the gdina function for the estimation of
the generalized DINA (GDINA) model.
For assessment of model fit see modelfit.cor.din and
anova.din.
See itemfit.sx2 for item fit statistics.
See discrim.index for computing discrimination indices.
See also CDM-package for general
information about this package.
See the NPCD::JMLE function in the NPCD package for
joint maximum likelihood estimation
of the DINA, DINO and NIDA model.
See the dina::DINA_Gibbs function in the dina
package for MCMC based estimation of the DINA model.
Examples
#############################################################################
# EXAMPLE 1: Examples based on dataset fractions.subtraction.data
#############################################################################
## dataset fractions.subtraction.data and corresponding Q-Matrix
head(fraction.subtraction.data)
fraction.subtraction.qmatrix
## Misspecification in parameter specification for method CDM::din()
## leads to warnings and terminates estimation procedure. E.g.,
# See Q-Matrix specification
fractions.dina.warning1 <- CDM::din(data=fraction.subtraction.data,
q.matrix=t(fraction.subtraction.qmatrix))
# See guess.init specification
fractions.dina.warning2 <- CDM::din(data=fraction.subtraction.data,
q.matrix=fraction.subtraction.qmatrix, guess.init=rep(1.2,
ncol(fraction.subtraction.data)))
# See rule specification
fractions.dina.warning3 <- CDM::din(data=fraction.subtraction.data,
q.matrix=fraction.subtraction.qmatrix, rule=c(rep("DINA",
10), rep("DINO", 9)))
## Parameter estimation of DINA model
# rule="DINA" is default
fractions.dina <- CDM::din(data=fraction.subtraction.data,
q.matrix=fraction.subtraction.qmatrix, rule="DINA")
attributes(fractions.dina)
str(fractions.dina)
## For instance assessing the guessing parameters through
## assignment
fractions.dina$guess
## corresponding summaries, including IDI,
## most frequent skill classes and information
## criteria AIC and BIC
summary(fractions.dina)
## In particular, assessing detailed summary through assignment
detailed.summary.fs <- summary(fractions.dina)
str(detailed.summary.fs)
## Item discrimination index of item 8 is too low. This is also
## visualized in the first plot
plot(fractions.dina)
## The reason therefore is a high guessing parameter
round(fractions.dina$guess[,1], 2)
## Estimate DINA model with different random initial parameters using seed=1345
fractions.dina1 <- CDM::din(data=fraction.subtraction.data,
q.matrix=fraction.subtraction.qmatrix, rule="DINA", seed=1345)
## Fix the guessing parameters of items 5, 8 and 9 equal to .20
# define a constraint.guess matrix
constraint.guess <- matrix(c(5,8,9, rep(0.2, 3)), ncol=2)
fractions.dina.fixed <- CDM::din(data=fraction.subtraction.data,
q.matrix=fraction.subtraction.qmatrix,
constraint.guess=constraint.guess)
## The second plot shows the expected (MAP) and observed skill
## probabilities. The third plot visualizes the skill class
## occurrence probabilities; Only the 'top.n.skill.classes' most frequent
## skill classes are labeled; it is obvious that the skill class '11111111'
## (all skills are mastered) is the most probable in this population.
## The fourth plot shows the skill probabilities conditional on response
## patterns; in this population the skills 3 and 6 seem to be
## mastered easier than the others. The fourth plot shows the
## skill probabilities conditional on a specified response
## pattern; it is shown whether a skill is mastered (above
## .5+'uncertainty') unclassifiable (within the boundaries) or
## not mastered (below .5-'uncertainty'). In this case, the
## 527th respondent was chosen; if no response pattern is
## specified, the plot will not be shown (of course)
pattern <- paste(fraction.subtraction.data[527, ], collapse="")
plot(fractions.dina, pattern=pattern, display.nr=4)
#uncertainty=0.1, top.n.skill.classes=6 are default
plot(fractions.dina.fixed, uncertainty=0.1, top.n.skill.classes=6,
pattern=pattern)
## Not run:
#############################################################################
# EXAMPLE 2: Examples based on dataset sim.dina
#############################################################################
# DINA Model
d1 <- CDM::din(sim.dina, q.matr=sim.qmatrix, rule="DINA",
conv.crit=0.01, maxit=500, progress=TRUE)
summary(d1)
# DINA model with hierarchical skill classes (Hierarchical DINA model)
# 1st step: estimate an initial full model to look at the indexing
# of skill classes
d0 <- CDM::din(sim.dina, q.matr=sim.qmatrix, maxit=1)
d0$attribute.patt.splitted
# [,1] [,2] [,3]
# [1,] 0 0 0
# [2,] 1 0 0
# [3,] 0 1 0
# [4,] 0 0 1
# [5,] 1 1 0
# [6,] 1 0 1
# [7,] 0 1 1
# [8,] 1 1 1
#
# In this example, following hierarchical skill classes are only allowed:
# 000, 001, 011, 111
# We define therefore a vector of indices for skill classes with
# zero probabilities (see entries in the rows of the matrix
# d0$attribute.patt.splitted above)
zeroprob.skillclasses <- c(2,3,5,6) # classes 100, 010, 110, 101
# estimate the hierarchical DINA model
d1a <- CDM::din(sim.dina, q.matr=sim.qmatrix,
zeroprob.skillclasses=zeroprob.skillclasses )
summary(d1a)
# Mixed DINA and DINO Model
d1b <- CDM::din(sim.dina, q.matr=sim.qmatrix, rule=
c(rep("DINA", 7), rep("DINO", 2)), conv.crit=0.01,
maxit=500, progress=FALSE)
summary(d1b)
# DINO Model
d2 <- CDM::din(sim.dina, q.matr=sim.qmatrix, rule="DINO",
conv.crit=0.01, maxit=500, progress=FALSE)
summary(d2)
# Comparison of DINA and DINO estimates
lapply(list("guessing"=rbind("DINA"=d1$guess[,1],
"DINO"=d2$guess[,1]), "slipping"=rbind("DINA"=
d1$slip[,1], "DINO"=d2$slip[,1])), round, 2)
# Comparison of the information criteria
c("DINA"=d1$AIC, "MIXED"=d1b$AIC, "DINO"=d2$AIC)
# following estimates:
d1$coef # guessing and slipping parameter
d1$guess # guessing parameter
d1$slip # slipping parameter
d1$skill.patt # probabilities for skills
d1$attribute.patt # skill classes with probabilities
d1$subj.pattern # pattern per subject
# posterior probabilities for every response pattern
d1$posterior
# Equal guessing parameters
d2a <- CDM::din( data=sim.dina, q.matrix=sim.qmatrix,
guess.equal=TRUE, slip.equal=FALSE )
d2a$coef
# Equal guessing and slipping parameters
d2b <- CDM::din( data=sim.dina, q.matrix=sim.qmatrix,
guess.equal=TRUE, slip.equal=TRUE )
d2b$coef
#############################################################################
# EXAMPLE 3: Examples based on dataset sim.dino
#############################################################################
# DINO Estimation
d3 <- CDM::din(sim.dino, q.matr=sim.qmatrix, rule="DINO",
conv.crit=0.005, progress=FALSE)
# Mixed DINA and DINO Model
d3b <- CDM::din(sim.dino, q.matr=sim.qmatrix,
rule=c(rep("DINA", 4), rep("DINO", 5)), conv.crit=0.001,
progress=FALSE)
# DINA Estimation
d4 <- CDM::din(sim.dino, q.matr=sim.qmatrix, rule="DINA",
conv.crit=0.005, progress=FALSE)
# Comparison of DINA and DINO estimates
lapply(list("guessing"=rbind("DINO"=d3$guess[,1], "DINA"=d4$guess[,1]),
"slipping"=rbind("DINO"=d3$slip[,1], "DINA"=d4$slip[,1])), round, 2)
# Comparison of the information criteria
c("DINO"=d3$AIC, "MIXED"=d3b$AIC, "DINA"=d4$AIC)
#############################################################################
# EXAMPLE 4: Example estimation with weights based on dataset sim.dina
#############################################################################
# Here, a weighted maximum likelihood estimation is used
# This could be useful for survey data.
# i.e. first 200 persons have weight 2, the other have weight 1
(weights <- c(rep(2, 200), rep(1, 200)))
d5 <- CDM::din(sim.dina, sim.qmatrix, rule="DINA", conv.crit=
0.005, weights=weights, progress=FALSE)
# Comparison of the information criteria
c("DINA"=d1$AIC, "WEIGHTS"=d5$AIC)
#############################################################################
# EXAMPLE 5: Example estimation within a balanced incomplete
## block (BIB) design generated on dataset sim.dina
#############################################################################
# generate BIB data
# The next example shows that the din function works for
# (relatively arbitrary) missing value pattern
# Here, a missing by design is generated in the dataset dinadat.bib
sim.dina.bib <- sim.dina
sim.dina.bib[1:100, 1:3] <- NA
sim.dina.bib[101:300, 4:8] <- NA
sim.dina.bib[301:400, c(1,2,9)] <- NA
d6 <- CDM::din(sim.dina.bib, sim.qmatrix, rule="DINA",
conv.crit=0.0005, weights=weights, maxit=200)
d7 <- CDM::din(sim.dina.bib, sim.qmatrix, rule="DINO",
conv.crit=0.005, weights=weights)
# Comparison of DINA and DINO estimates
lapply(list("guessing"=rbind("DINA"=d6$guess[,1],
"DINO"=d7$guess[,1]), "slipping"=rbind("DINA"=
d6$slip[,1], "DINO"=d7$slip[,1])), round, 2)
#############################################################################
# EXAMPLE 6: DINA model with attribute hierarchy
#############################################################################
set.seed(987)
# assumed skill distribution: P(000)=P(100)=P(110)=P(111)=.245 and
# "deviant pattern": P(010)=.02
K <- 3 # number of skills
# define alpha
alpha <- scan()
0 0 0
1 0 0
1 1 0
1 1 1
0 1 0
alpha <- matrix( alpha, length(alpha)/K, K, byrow=TRUE )
alpha <- alpha[ c( rep(1:4,each=245), rep(5,20) ), ]
# define Q-matrix
q.matrix <- scan()
1 0 0 1 0 0 1 0 0
0 1 0 0 1 0 0 1 0
0 0 1 0 1 0 0 0 1
1 1 0 1 0 1 0 1 1
q.matrix <- matrix( q.matrix, nrow=length(q.matrix)/K, ncol=K, byrow=TRUE )
# simulate DINA data
dat <- CDM::sim.din( alpha=alpha, q.matrix=q.matrix )$dat
#*** Model 1: estimate DINA model | no skill space restriction
mod1 <- CDM::din( dat, q.matrix )
#*** Model 2: DINA model | hierarchy A2 > A3
B <- "A2 > A3"
skill.names <- paste0("A",1:3)
skillspace <- CDM::skillspace.hierarchy( B, skill.names )$skillspace.reduced
mod2 <- CDM::din( dat, q.matrix, skillclasses=skillspace )
#*** Model 3: DINA model | linear hierarchy A1 > A2 > A3
# This is a misspecied model because due to P(010)=.02 the relation A1>A2
# does not hold.
B <- "A1 > A2
A2 > A3"
skill.names <- paste0("A",1:3)
skillspace <- CDM::skillspace.hierarchy( B, skill.names )$skillspace.reduced
mod3 <- CDM::din( dat, q.matrix, skillclasses=skillspace )
#*** Model 4: 2PL model in gdm
mod4 <- CDM::gdm( dat, theta.k=seq(-5,5,len=21),
decrease.increments=TRUE, skillspace="normal" )
summary(mod4)
anova(mod1,mod2)
## Model loglike Deviance Npars AIC BIC Chisq df p
## 2 Model 2 -7052.460 14104.92 29 14162.92 14305.24 0.9174 2 0.63211
## 1 Model 1 -7052.001 14104.00 31 14166.00 14318.14 NA NA NA
anova(mod2,mod3)
## Model loglike Deviance Npars AIC BIC Chisq df p
## 2 Model 2 -7059.058 14118.12 27 14172.12 14304.63 13.19618 2 0.00136
## 1 Model 1 -7052.460 14104.92 29 14162.92 14305.24 NA NA NA
anova(mod2,mod4)
## Model loglike Deviance Npars AIC BIC Chisq df p
## 2 Model 2 -7220.05 14440.10 24 14488.10 14605.89 335.1805 5 0
## 1 Model 1 -7052.46 14104.92 29 14162.92 14305.24 NA NA NA
# compare fit statistics
summary( CDM::modelfit.cor.din( mod2 ) )
summary( CDM::modelfit.cor.din( mod4 ) )
#############################################################################
# EXAMPLE 7: Fitting the basic local independence model (BLIM) with din
#############################################################################
library(pks)
data(DoignonFalmagne7, package="pks")
## str(DoignonFalmagne7)
## $ K : int [1:9, 1:5] 0 1 0 1 1 1 1 1 1 0 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:9] "00000" "10000" "01000" "11000" ...
## .. ..$ : chr [1:5] "a" "b" "c" "d" ...
## $ N.R: Named int [1:32] 80 92 89 3 2 1 89 16 18 10 ...
## ..- attr(*, "names")=chr [1:32] "00000" "10000" "01000" "00100" ...
# The idea is to fit the local independence model with the din function.
# This can be accomplished by specifying a DINO model with
# prespecified skill classes.
# extract dataset
dat <- as.numeric( unlist( sapply( names(DoignonFalmagne7$N.R),
FUN=function( ll){ strsplit( ll, split="") } ) ) )
dat <- matrix( dat, ncol=5, byrow=TRUE )
colnames(dat) <- colnames(DoignonFalmagne7$K)
rownames(dat) <- names(DoignonFalmagne7$N.R)
# sample weights
weights <- DoignonFalmagne7$N.R
# define Q-matrix
q.matrix <- t(DoignonFalmagne7$K)
v1 <- colnames(q.matrix) <- paste0("S", colnames(q.matrix))
q.matrix <- q.matrix[, - 1] # remove S00000
# define skill classes
SC <- ncol(q.matrix)
skillclasses <- matrix( 0, nrow=SC+1, ncol=SC)
colnames(skillclasses) <- colnames(q.matrix)
rownames(skillclasses) <- v1
skillclasses[ cbind( 2:(SC+1), 1:SC ) ] <- 1
# estimate BLIM with din function
mod1 <- CDM::din(data=dat, q.matrix=q.matrix, skillclasses=skillclasses,
rule="DINO", weights=weights )
summary(mod1)
## Item parameters
## item guess slip IDI rmsea
## a a 0.158 0.162 0.680 0.011
## b b 0.145 0.159 0.696 0.009
## c c 0.008 0.181 0.811 0.001
## d d 0.012 0.129 0.859 0.001
## e e 0.025 0.146 0.828 0.007
# estimate basic local independence model with pks package
mod2 <- pks::blim(K, N.R, method="ML") # maximum likelihood estimation by EM algorithm
mod2
## Error and guessing parameters
## beta eta
## a 0.164871 0.103065
## b 0.163113 0.095074
## c 0.188839 0.000004
## d 0.079835 0.000003
## e 0.088648 0.019910
## End(Not run)
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