| GDINA {GDINA} | R Documentation |
CDM calibration under the G-DINA model framework
Description
GDINA calibrates the generalized deterministic inputs, noisy and
gate (G-DINA; de la Torre, 2011) model for dichotomous responses, and its extension, the sequential
G-DINA model (Ma, & de la Torre, 2016a; Ma, 2017) for ordinal and nominal responses.
By setting appropriate constraints, the deterministic inputs,
noisy and gate (DINA; de la Torre, 2009; Junker & Sijtsma, 2001) model,
the deterministic inputs, noisy or gate (DINO; Templin & Henson, 2006)
model, the reduced reparametrized unified model (R-RUM; Hartz, 2002),
the additive CDM (A-CDM; de la Torre, 2011), the linear logistic
model (LLM; Maris, 1999), and the multiple-strategy DINA model (MSDINA; de la Torre & Douglas, 2008; Huo & de la Torre, 2014)
can also be calibrated. Note that the LLM is equivalent to
the C-RUM (Hartz, 2002), a special case of the GDM (von Davier, 2008), and that the R-RUM
is also known as a special case of the generalized NIDA model (de la Torre, 2011).
In addition, users are allowed to specify design matrix and link function for each item, and distinct models may be used in a single test for different items. The attributes can be either dichotomous or polytomous (Chen & de la Torre, 2013). Joint attribute distribution may be modelled using independent or saturated model, structured model, higher-order model (de la Torre & Douglas, 2004), or loglinear model (Xu & von Davier, 2008). Marginal maximum likelihood method with Expectation-Maximization (MMLE/EM) alogrithm is used for item parameter estimation.
To compare two or more GDINA objects, use method anova.
To calculate structural parameters for item and joint attribute distributions, use method coef.
To calculate lower-order incidental (person) parameters
use method personparm. To extract other components returned, use extract.
To plot item/category response function, use plot. To
check whether monotonicity is violated, use monocheck. To conduct anaysis in graphical user interface,
use startGDINA.
Usage
GDINA(
dat,
Q,
model = "GDINA",
sequential = FALSE,
att.dist = "saturated",
mono.constraint = FALSE,
group = NULL,
linkfunc = NULL,
design.matrix = NULL,
no.bugs = 0,
att.prior = NULL,
att.str = NULL,
verbose = 1,
higher.order = list(),
loglinear = 2,
catprob.parm = NULL,
control = list(),
item.names = NULL,
solver = NULL,
nloptr.args = list(),
auglag.args = list(),
solnp.args = list(),
...
)
## S3 method for class 'GDINA'
anova(object, ...)
## S3 method for class 'GDINA'
coef(
object,
what = c("catprob", "delta", "gs", "itemprob", "LCprob", "rrum", "lambda"),
withSE = FALSE,
SE.type = 2,
digits = 4,
...
)
## S3 method for class 'GDINA'
extract(object, what, SE.type = 2, ...)
## S3 method for class 'GDINA'
personparm(object, what = c("EAP", "MAP", "MLE", "mp", "HO"), digits = 4, ...)
## S3 method for class 'GDINA'
logLik(object, ...)
## S3 method for class 'GDINA'
deviance(object, ...)
## S3 method for class 'GDINA'
nobs(object, ...)
## S3 method for class 'GDINA'
vcov(object, ...)
## S3 method for class 'GDINA'
npar(object, ...)
## S3 method for class 'GDINA'
indlogLik(object, ...)
## S3 method for class 'GDINA'
indlogPost(object, ...)
## S3 method for class 'GDINA'
summary(object, ...)
Arguments
dat |
A required |
Q |
A required matrix; The number of rows occupied by a single-strategy dichotomous item is 1, by a polytomous item is
the number of nonzero categories, and by a mutiple-strategy dichotomous item is the number of strategies.
The number of column is equal to the number of attributes if all items are single-strategy dichotomous items, but
the number of attributes + 2 if any items are polytomous or have multiple strategies.
For a polytomous item, the first column represents the item number and the second column indicates the nonzero category number.
For a multiple-strategy dichotomous item, the first column represents the item number and the second column indicates the strategy number.
For binary attributes, 1 denotes the attributes are measured by the items and 0 means the attributes are not
measured. For polytomous attributes, non-zero elements indicate which level
of attributes are needed (see Chen, & de la Torre, 2013). See |
model |
A vector for each item or nonzero category, or a scalar which will be used for all
items or nonzero categories to specify the CDMs fitted. The possible options
include |
sequential |
logical; |
att.dist |
How is the joint attribute distribution estimated? It can be (1) |
mono.constraint |
logical; |
group |
a factor or a vector indicating the group each individual belongs to. Its length must be equal to the number of individuals. |
linkfunc |
a vector of link functions for each item/category; It can be |
design.matrix |
a list of design matrices; Its length must be equal to the number of items (or nonzero categories for sequential models).
If CDM for item j is specified as "UDF" in argument |
no.bugs |
A numeric scalar (whole numbers only) indicating the number of bugs or misconceptions in the Q-matrix. The bugs must be included in the last |
att.prior |
A vector of length |
att.str |
Specify attribute structures. |
verbose |
How to print calibration information after each EM iteration? Can be 0, 1 or 2, indicating to print no information, information for current iteration, or information for all iterations. |
higher.order |
A list specifying the higher-order joint attribute distribution with the following components:
|
loglinear |
the order of loglinear smooth for attribute space. It can be either 1 or 2 indicating the loglinear model with main effect only and with main effect and first-order interaction; It can also be a matrix, representing the design matrix for the loglinear model. |
catprob.parm |
A list of initial success probability parameters for each nonzero category. |
control |
A list of control parameters with elements:
|
item.names |
A vector giving the item names. By default, items are named as "Item 1", "Item 2", etc. |
solver |
A string indicating which solver should be used in M-step. By default, the solver is automatically chosen according to the models specified. Possible options include slsqp, nloptr, solnp and auglag. |
nloptr.args |
a list of control parameters to be passed to |
auglag.args |
a list of control parameters to be passed to the alabama::auglag() function. It can contain two elements:
|
solnp.args |
a list of control parameters to be passed to |
... |
additional arguments |
object |
GDINA object for various S3 methods |
what |
argument for various S3 methods; For calculating structural parameters using
For calculating incidental parameters using
|
withSE |
argument for method |
SE.type |
type of standard errors. For now, SEs are calculated based on outper-product of gradient.
It can be |
digits |
How many decimal places in each number? The default is 4. |
Value
GDINA returns an object of class GDINA. Methods for GDINA objects
include extract for extracting various components, coef
for extracting structural parameters, personparm
for calculating incidental (person) parameters, summary for summary information.
AIC, BIC,logLik, deviance and npar can also be used to
calculate AIC, BIC, observed log-likelihood, deviance and number of parameters.
Methods (by generic)
-
anova(GDINA): Model comparison using likelihood ratio test -
coef(GDINA): extract structural parameter estimates -
extract(GDINA): extract various elements of GDINA estimates -
personparm(GDINA): calculate person attribute patterns and higher-order ability -
logLik(GDINA): calculate log-likelihood -
deviance(GDINA): calculate deviance -
nobs(GDINA): calculate number of observations -
vcov(GDINA): calculate covariance-matrix for delta parameters -
npar(GDINA): calculate the number of parameters -
indlogLik(GDINA): extract log-likelihood for each individual -
indlogPost(GDINA): extract log posterior for each individual -
summary(GDINA): print summary information
The G-DINA model
The generalized DINA model (G-DINA; de la Torre, 2011) is an extension of the DINA model.
Unlike the DINA model, which collaspes all latent classes into two latent groups for
each item, if item j requires K_j^*
attributes, the G-DINA model collapses 2^K latent classes into 2^{K_j^*}
latent groups with unique success probabilities on item j, where
K_j^*=\sum_{k=1}^{K}q_{jk}.
Let \boldsymbol{\alpha}_{lj}^* be the reduced attribute
pattern consisting of the columns of the attributes required by item j, where
l=1,\ldots,2^{K_j^*}. For example, if only the first and the last attributes are
required, \boldsymbol{\alpha}_{lj}^*=(\alpha_{l1},\alpha_{lK}). For notational
convenience, the first K_j^* attributes can be assumed to be the required attributes
for item j as in de la Torre (2011). The probability of success P(X_{j}=1|\boldsymbol{\alpha}_{lj}^*) is denoted
by P(\boldsymbol{\alpha}_{lj}^*). To model this probability of success, different link functions
as in the generalized linear models are used in the G-DINA model. The item response
function of the G-DINA model using the identity link can be written as
f[P(\boldsymbol{\alpha}_{lj}^*)]=\delta_{j0}+\sum_{k=1}^{K_j^*}\delta_{jk}\alpha_{lk}+
\sum_{k'=k+1}^{K_j^*}\sum_{k=1}^{K_j^*-1}\delta_{jkk'}\alpha_{lk}\alpha_{lk'}+\cdots+
\delta_{j12{\cdots}K_j^*}\prod_{k=1}^{K_j^*}\alpha_{lk},
or in matrix form,
f[\boldsymbol{P}_j]=\boldsymbol{M}_j\boldsymbol{\delta}_j,
where \delta_{j0} is the intercept for item j, \delta_{jk} is the main effect
due to \alpha_{lk}, \delta_{jkk'} is the interaction effect due to
\alpha_{lk} and \alpha_{lk'}, \delta_{j12{\ldots}K_j^*} is the interaction
effect due to \alpha_{l1}, \cdots,\alpha_{lK_j^*}. The log and logit links can also
be employed.
Other CDMs as special cases
Several widely used CDMs can be obtained by setting appropriate constraints to the G-DINA model. This section introduces the parameterization of different CDMs within the G-DINA model framework very breifly. Readers interested in this please refer to de la Torre(2011) for details.
DINA model-
In DINA model, each item has two item parameters - guessing (
g) and slip (s). In traditional parameterization of the DINA model, a latent variable\etafor personiand itemjis defined as\eta_{ij}=\prod_{k=1}^K\alpha_{ik}^{q_{jk}}Briefly speaking, if individual
imaster all attributes required by itemj,\eta_{ij}=1; otherwise,\eta_{ij}=0. Item response function of the DINA model can be written byP(X_{ij}=1|\eta_{ij})=(1-s_j)^{\eta_{ij}}g_j^{1-\eta_{ij}}To obtain the DINA model from the G-DINA model, all terms in identity link G-DINA model except
\delta_0and\delta_{12{\ldots}K_j^*}need to be fixed to zero, that is,P(\boldsymbol{\alpha}_{lj}^*)= \delta_{j0}+\delta_{j12{\cdots}K_j^*}\prod_{k=1}^{K_j^*}\alpha_{lk}In this parameterization,
\delta_{j0}=g_jand\delta_{j0}+\delta_{j12{\cdots}K_j^*}=1-s_j. DINO model-
The DINO model can be given by
P(\boldsymbol{\alpha}_{lj}^{*})= \delta_{j0}+\delta_{j1}I(\boldsymbol{\alpha}_{lj}^*\neq \boldsymbol{0})where
I(\cdot)is an indicator variable. The DINO model is also a constrained identity link G-DINA model. As shown by de la Torre (2011), the appropriate constraint is\delta_{jk}=-\delta_{jk^{'}k^{''}}=\cdots=(-1)^{K_j^*+1}\delta_{j12{\cdots}K_j^*},for
k=1,\cdots,K_j^*, k^{'}=1,\cdots,K_j^*-1, andk^{''}>k^{'},\cdots,K_j^*. Additive models with different link functions-
The A-CDM, LLM and R-RUM can be obtained by setting all interactions to be zero in identity, logit and log link G-DINA model, respectively. Specifically, the A-CDM can be formulated as
P(\boldsymbol{\alpha}_{lj}^*)= \delta_{j0}+\sum_{k=1}^{K_j^*}\delta_{jk}\alpha_{lk}The item response function for LLM can be given by
logit[P(\boldsymbol{\alpha}_{lj}^*)]=\delta_{j0}+\sum_{k=1}^{K_j^*}\delta_{jk}\alpha_{lk},and lastly, the RRUM, can be written as
log[P(\boldsymbol{\alpha}_{lj}^*)]=\delta_{j0}+\sum_{k=1}^{K_j^*}\delta_{jk}\alpha_{lk}.It should be noted that the LLM is equivalent to the compensatory RUM, which is subsumed by the GDM, and that the RRUM is a special case of the generalized noisy inputs, deterministic “And" gate model (G-NIDA).
Simultaneously identifying skills and misconceptions (SISM)-
The SISM can be can be reformulated as
P(\boldsymbol{\alpha}_{lj}^*)= \delta_{j0}+\delta_{j1}I[{mastering all skills}]+ \delta_{j2}I[{having no bugs}]+\delta_{j12}I[{mastering all skills and having no bugs}].As a result,the success probability of students who have mastered all the measured skills and possess none of the measured misconceptions (
h_jin Equation 4 of Kuo, et al, 2018) is\delta_{j0}+\delta_{j1}+\delta_{j2}+\delta_{j12}, the success probability of students who have mastered all the measured skills but possess some of the measured misconceptions (\omega_j) is\delta_{j0}+\delta_{j1}, the success probability of students who have not mastered all the measured skills and possess none of the measured misconceptions (g_j) is\delta_{j0}+\delta_{j2}and success probability of students who have not mastered all the measured skills and possess at least one of the measured misconceptions(\epsilon_j) is\delta_{j0}.By specifying
no.bugsbeing equal to the number of attributes, the Bug-DINO is obtained, as inP(\boldsymbol{\alpha}_{lj}^*)=\delta_{j0}+\delta_{j1}I[{having no bugs}].
Joint Attribute Distribution
The joint attribute distribution can be modeled using various methods. This section mainly focuses on the so-called
higher-order approach, which was originally proposed by de la Torre
and Douglas (2004) for the DINA model. It has been extended in this package for all condensation rules.
Particularly, three approaches are available for the higher-order attribute structure:
intercept only approach, common slope approach and varied slope approach.
For the intercept only approach, the probability of mastering attribute k for individual i is defined as
P(\alpha_k=1|\theta_i,\lambda_{0k})=\frac{exp(\theta_i+\lambda_{0k})}{1+exp(\theta_i+\lambda_{0k})}
For the common slope approach, the probability of mastering attribute k for individual i is defined as
P(\alpha_k=1|\theta_i,\lambda_{0k},\lambda_{1})=\frac{exp(\lambda_{1}\theta_i+\lambda_{0k})}{1+exp(\lambda_{1}\theta_i+\lambda_{0k})}
For the varied slope approach, the probability of mastering attribute k for individual i is defined as
P(\alpha_k=1|\theta_i,\lambda_{0k},\lambda_{1k})=\frac{exp(\lambda_{1k}\theta_i+\lambda_{0k})}{1+exp(\lambda_{1k}\theta_i+\lambda_{0k})}
where \theta_i is the ability of examinee i. \lambda_{0k} and \lambda_{1k} are the intercept
and slope parameters for attribute k, respectively.
The probability of joint attributes can be written as
P(\boldsymbol{\alpha}|\theta_i,\boldsymbol{\lambda})=\prod_k P(\alpha_k|\theta_i,\boldsymbol{\lambda})
.
Model Estimation
The MMLE/EM algorithm is implemented in this package. For G-DINA, DINA and DINO models, closed-form solutions exist. See de la Torre (2009) and de la Torre (2011) for details. For ACDM, LLM and RRUM, closed-form solutions do not exist, and therefore some general optimization techniques are adopted in M-step (Ma, Iaconangelo & de la Torre, 2016). The selection of optimization techniques mainly depends on whether some specific constraints need to be added.
The sequential G-DINA model is a special case of the diagnostic tree model (DTM; Ma, 2019) and estimated using the mapping matrix accordingly (See Tutz, 1997; Ma, 2019).
The Number of Parameters
For dichotomous response models:
Assume a test measures K attributes and item j requires K_j^* attributes:
The DINA and DINO model has 2 item parameters for each item;
if item j is ACDM, LLM or RRUM, it has K_j^*+1 item parameters; if it is G-DINA model, it has 2^{K_j^*} item parameters.
Apart from item parameters, the parameters involved in the estimation of joint attribute distribution need to be estimated as well.
When using the saturated attribute structure, there are 2^K-1 parameters for joint attribute distribution estimation; when
using a higher-order attribute structure, there are K, K+1, and 2\times K parameters for the intercept only approach, common slope approach and varied slope approach, respectively.
For polytomous response data using the sequential G-DINA model, the number of item parameters
are counted at category level.
Note
anova function does NOT check whether models compared are nested or not.
Author(s)
Wenchao Ma, The University of Alabama, wenchao.ma@ua.edu
Jimmy de la Torre, The University of Hong Kong
References
Bock, R. D., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm. Psychometrika, 46, 443-459.
Bock, R. D., & Lieberman, M. (1970). Fitting a response model forn dichotomously scored items. Psychometrika, 35, 179-197.
Bor-Chen Kuo, Chun-Hua Chen, Chih-Wei Yang, & Magdalena Mo Ching Mok. (2016). Cognitive diagnostic models for tests with multiple-choice and constructed-response items. Educational Psychology, 36, 1115-1133.
Carlin, B. P., & Louis, T. A. (2000). Bayes and empirical bayes methods for data analysis. New York, NY: Chapman & Hall
de la Torre, J., & Douglas, J. A. (2008). Model evaluation and multiple strategies in cognitive diagnosis: An analysis of fraction subtraction data. Psychometrika, 73, 595-624.
de la Torre, J. (2009). DINA Model and Parameter Estimation: A Didactic. Journal of Educational and Behavioral Statistics, 34, 115-130.
de la Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76, 179-199.
de la Torre, J., & Douglas, J. A. (2004). Higher-order latent trait models for cognitive diagnosis. Psychometrika, 69, 333-353.
de la Torre, J., & Lee, Y. S. (2013). Evaluating the wald test for item-level comparison of saturated and reduced models in cognitive diagnosis. Journal of Educational Measurement, 50, 355-373.
Haertel, E. H. (1989). Using restricted latent class models to map the skill structure of achievement items. Journal of Educational Measurement, 26, 301-321.
Hartz, S. M. (2002). A bayesian framework for the unified model for assessing cognitive abilities: Blending theory with practicality (Unpublished doctoral dissertation). University of Illinois at Urbana-Champaign.
Huo, Y., & de la Torre, J. (2014). Estimating a Cognitive Diagnostic Model for Multiple Strategies via the EM Algorithm. Applied Psychological Measurement, 38, 464-485.
Junker, B. W., & Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Applied Psychological Measurement, 25, 258-272.
Kuo, B.-C., Chen.-H., & de la Torre,J. (2018). A cognitive diagnosis model for identifying coexisting skills and misconceptions.Applied Psychological Measuremet, 179–191.
Ma, W., & de la Torre, J. (2016). A sequential cognitive diagnosis model for polytomous responses. British Journal of Mathematical and Statistical Psychology. 69, 253-275.
Ma, W., & de la Torre, J. (2020). GDINA: An R Package for Cognitive Diagnosis Modeling. Journal of Statistical Software, 93(14), 1-26.
Ma, W. (2019). A diagnostic tree model for polytomous responses with multiple strategies. British Journal of Mathematical and Statistical Psychology, 72, 61-82.
Ma, W., Iaconangelo, C., & de la Torre, J. (2016). Model similarity, model selection and attribute classification. Applied Psychological Measurement, 40, 200-217.
Ma, W. (2017). A Sequential Cognitive Diagnosis Model for Graded Response: Model Development, Q-Matrix Validation,and Model Comparison. Unpublished doctoral dissertation. New Brunswick, NJ: Rutgers University.
Maris, E. (1999). Estimating multiple classification latent class models. Psychometrika, 64, 187-212.
Tatsuoka, K. K. (1983). Rule space: An approach for dealing with misconceptions based on item response theory. Journal of Educational Measurement, 20, 345-354.
Templin, J. L., & Henson, R. A. (2006). Measurement of psychological disorders using cognitive diagnosis models. Psychological Methods, 11, 287-305.
Tutz, G. (1997). Sequential models for ordered responses. In W.J. van der Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory p. 139-152). New York, NY: Springer.
Xu, X., & von Davier, M. (2008). Fitting the structured general diagnostic model to NAEP data. ETS research report, RR-08-27.
See Also
See autoGDINA for Q-matrix validation, item-level model comparison and model calibration
in one run; See modelfit and itemfit for model and item fit analysis, Qval for Q-matrix validation,
modelcomp for item level model comparison and simGDINA for data simulation.
GMSCDM for a series of multiple strategy CDMs for dichotomous data,
and DTM for diagnostic tree model for multiple strategies in polytomous response data
Also see gdina in CDM package for the G-DINA model estimation.
Examples
## Not run:
####################################
# Example 1. #
# GDINA, DINA, DINO #
# ACDM, LLM and RRUM #
# estimation and comparison #
# #
####################################
dat <- sim10GDINA$simdat
Q <- sim10GDINA$simQ
#--------GDINA model --------#
mod1 <- GDINA(dat = dat, Q = Q, model = "GDINA")
mod1
# summary information
summary(mod1)
AIC(mod1) #AIC
BIC(mod1) #BIC
logLik(mod1) #log-likelihood value
deviance(mod1) # deviance: -2 log-likelihood
npar(mod1) # number of parameters
head(indlogLik(mod1)) # individual log-likelihood
head(indlogPost(mod1)) # individual log-posterior
# structural parameters
# see ?coef
coef(mod1) # item probabilities of success for each latent group
coef(mod1, withSE = TRUE) # item probabilities of success & standard errors
coef(mod1, what = "delta") # delta parameters
coef(mod1, what = "delta",withSE=TRUE) # delta parameters
coef(mod1, what = "gs") # guessing and slip parameters
coef(mod1, what = "gs",withSE = TRUE) # guessing and slip parameters & standard errors
# person parameters
# see ?personparm
personparm(mod1) # EAP estimates of attribute profiles
personparm(mod1, what = "MAP") # MAP estimates of attribute profiles
personparm(mod1, what = "MLE") # MLE estimates of attribute profiles
#plot item response functions for item 10
plot(mod1,item = 10)
plot(mod1,item = 10,withSE = TRUE) # with error bars
#plot mastery probability for individuals 1, 20 and 50
plot(mod1,what = "mp", person =c(1,20,50))
# Use extract function to extract more components
# See ?extract
# ------- DINA model --------#
dat <- sim10GDINA$simdat
Q <- sim10GDINA$simQ
mod2 <- GDINA(dat = dat, Q = Q, model = "DINA")
mod2
coef(mod2, what = "gs") # guess and slip parameters
coef(mod2, what = "gs",withSE = TRUE) # guess and slip parameters and standard errors
# Model comparison at the test level via likelihood ratio test
anova(mod1,mod2)
# -------- DINO model -------#
dat <- sim10GDINA$simdat
Q <- sim10GDINA$simQ
mod3 <- GDINA(dat = dat, Q = Q, model = "DINO")
#slip and guessing
coef(mod3, what = "gs") # guess and slip parameters
coef(mod3, what = "gs",withSE = TRUE) # guess and slip parameters + standard errors
# Model comparison at test level via likelihood ratio test
anova(mod1,mod2,mod3)
# --------- ACDM model -------#
dat <- sim10GDINA$simdat
Q <- sim10GDINA$simQ
mod4 <- GDINA(dat = dat, Q = Q, model = "ACDM")
mod4
# --------- LLM model -------#
dat <- sim10GDINA$simdat
Q <- sim10GDINA$simQ
mod4b <- GDINA(dat = dat, Q = Q, model = "LLM")
mod4b
# --------- RRUM model -------#
dat <- sim10GDINA$simdat
Q <- sim10GDINA$simQ
mod4c <- GDINA(dat = dat, Q = Q, model = "RRUM")
mod4c
# --- Different CDMs for different items --- #
dat <- sim10GDINA$simdat
Q <- sim10GDINA$simQ
models <- c(rep("GDINA",3),"LLM","DINA","DINO","ACDM","RRUM","LLM","RRUM")
mod5 <- GDINA(dat = dat, Q = Q, model = models)
anova(mod1,mod2,mod3,mod4,mod4b,mod4c,mod5)
####################################
# Example 2. #
# Model estimations #
# With monotonocity constraints #
####################################
dat <- sim10GDINA$simdat
Q <- sim10GDINA$simQ
# for item 10 only
mod11 <- GDINA(dat = dat, Q = Q, model = "GDINA",mono.constraint = c(rep(FALSE,9),TRUE))
mod11
mod11a <- GDINA(dat = dat, Q = Q, model = "DINA",mono.constraint = TRUE)
mod11a
mod11b <- GDINA(dat = dat, Q = Q, model = "ACDM",mono.constraint = TRUE)
mod11b
mod11c <- GDINA(dat = dat, Q = Q, model = "LLM",mono.constraint = TRUE)
mod11c
mod11d <- GDINA(dat = dat, Q = Q, model = "RRUM",mono.constraint = TRUE)
mod11d
coef(mod11d,"delta")
coef(mod11d,"rrum")
####################################
# Example 3a. #
# Model estimations #
# With Higher-order att structure #
####################################
dat <- sim10GDINA$simdat
Q <- sim10GDINA$simQ
# --- Higher order G-DINA model ---#
mod12 <- GDINA(dat = dat, Q = Q, model = "DINA",
att.dist="higher.order",higher.order=list(nquad=31,model = "2PL"))
personparm(mod12,"HO") # higher-order ability
# structural parameters
# first column is slope and the second column is intercept
coef(mod12,"lambda")
# --- Higher order DINA model ---#
mod22 <- GDINA(dat = dat, Q = Q, model = "DINA", att.dist="higher.order",
higher.order=list(model = "2PL",Prior=TRUE))
####################################
# Example 3b. #
# Model estimations #
# With log-linear att structure #
####################################
# --- DINA model with loglinear smoothed attribute space ---#
dat <- sim10GDINA$simdat
Q <- sim10GDINA$simQ
mod23 <- GDINA(dat = dat, Q = Q, model = "DINA",att.dist="loglinear",loglinear=1)
coef(mod23,"lambda") # intercept and three main effects
####################################
# Example 3c. #
# Model estimations #
# With independent att structure #
####################################
# --- GDINA model with independent attribute space ---#
dat <- sim10GDINA$simdat
Q <- sim10GDINA$simQ
mod33 <- GDINA(dat = dat, Q = Q, att.dist="independent")
coef(mod33,"lambda") # mastery probability for each attribute
####################################
# Example 4. #
# Model estimations #
# With fixed att structure #
####################################
# --- User-specified attribute priors ----#
# prior distribution is fixed during calibration
# Assume each of 000,100,010 and 001 has probability of 0.1
# and each of 110, 101,011 and 111 has probability of 0.15
# Note that the sum is equal to 1
#
prior <- c(0.1,0.1,0.1,0.1,0.15,0.15,0.15,0.15)
# fit GDINA model with fixed prior dist.
dat <- sim10GDINA$simdat
Q <- sim10GDINA$simQ
modp1 <- GDINA(dat = dat, Q = Q, att.prior = prior, att.dist = "fixed")
extract(modp1, what = "att.prior")
####################################
# Example 5a. #
# G-DINA #
# with hierarchical att structure #
####################################
# --- User-specified attribute structure ----#
Q <- sim30GDINA$simQ
K <- ncol(Q)
# divergent structure A1->A2->A3;A1->A4->A5
diverg <- list(c(1,2),
c(2,3),
c(1,4),
c(4,5))
struc <- att.structure(diverg,K)
set.seed(123)
# data simulation
N <- 1000
true.lc <- sample(c(1:2^K),N,replace=TRUE,prob=struc$att.prob)
table(true.lc) #check the sample
true.att <- attributepattern(K)[true.lc,]
gs <- matrix(rep(0.1,2*nrow(Q)),ncol=2)
# data simulation
simD <- simGDINA(N,Q,gs.parm = gs, model = "GDINA",attribute = true.att)
dat <- extract(simD,"dat")
modp1 <- GDINA(dat = dat, Q = Q, att.str = diverg, att.dist = "saturated")
modp1
coef(modp1,"lambda")
####################################
# Example 5b. #
# Reduced model (e.g.,ACDM) #
# with hierarchical att structure #
####################################
# --- User-specified attribute structure ----#
Q <- sim30GDINA$simQ
K <- ncol(Q)
# linear structure A1->A2->A3->A4->A5
linear <- list(c(1,2),
c(2,3),
c(3,4),
c(4,5))
struc <- att.structure(linear,K)
set.seed(123)
# data simulation
N <- 1000
true.lc <- sample(c(1:2^K),N,replace=TRUE,prob=struc$att.prob)
table(true.lc) #check the sample
true.att <- attributepattern(K)[true.lc,]
gs <- matrix(rep(0.1,2*nrow(Q)),ncol=2)
# data simulation
simD <- simGDINA(N,Q,gs.parm = gs, model = "ACDM",attribute = true.att)
dat <- extract(simD,"dat")
modp1 <- GDINA(dat = dat, Q = Q, model = "ACDM",
att.str = linear, att.dist = "saturated")
coef(modp1)
coef(modp1,"lambda")
####################################
# Example 6. #
# Specify initial values for item #
# parameters #
####################################
# check initials to see the format for initial item parameters
initials <- sim10GDINA$simItempar
dat <- sim10GDINA$simdat
Q <- sim10GDINA$simQ
mod.initial <- GDINA(dat,Q,catprob.parm = initials)
# compare initial item parameters
Map(rbind, initials,extract(mod.initial,"initial.catprob"))
####################################
# Example 7a. #
# Fix item and structure parameters#
# Estimate person attribute profile#
####################################
# check initials to see the format for initial item parameters
initials <- sim10GDINA$simItempar
prior <- c(0.1,0.1,0.1,0.1,0.15,0.15,0.15,0.15)
dat <- sim10GDINA$simdat
Q <- sim10GDINA$simQ
mod.ini <- GDINA(dat,Q,catprob.parm = initials,att.prior = prior,
att.dist = "fixed",control=list(maxitr = 0))
personparm(mod.ini)
# compare item parameters
Map(rbind, initials,coef(mod.ini))
####################################
# Example 7b. #
# Fix parameters for some items #
# Estimate person attribute profile#
####################################
# check initials to see the format for initial item parameters
initials <- sim10GDINA$simItempar
prior <- c(0.1,0.1,0.1,0.1,0.15,0.15,0.15,0.15)
dat <- sim10GDINA$simdat
Q <- sim10GDINA$simQ
# fix parameters of the first 5 items; do not fix mixing proportion parameters
mod.ini <- GDINA(dat,Q,catprob.parm = initials,
att.dist = "saturated",control=list(maxitr = c(rep(0,5),rep(2000,5))))
personparm(mod.ini)
# compare item parameters
Map(rbind, initials,coef(mod.ini))
####################################
# Example 8. #
# polytomous attribute #
# model estimation #
# see Chen, de la Torre 2013 #
####################################
# --- polytomous attribute G-DINA model --- #
dat <- sim30pGDINA$simdat
Q <- sim30pGDINA$simQ
#polytomous G-DINA model
pout <- GDINA(dat,Q)
# ----- polymous DINA model --------#
pout2 <- GDINA(dat,Q,model="DINA")
anova(pout,pout2)
####################################
# Example 9. #
# Sequential G-DINA model #
# see Ma, & de la Torre 2016 #
####################################
# --- polytomous attribute G-DINA model --- #
dat <- sim20seqGDINA$simdat
Q <- sim20seqGDINA$simQ
Q
# Item Cat A1 A2 A3 A4 A5
# 1 1 1 0 0 0 0
# 1 2 0 1 0 0 0
# 2 1 0 0 1 0 0
# 2 2 0 0 0 1 0
# 3 1 0 0 0 0 1
# 3 2 1 0 0 0 0
# 4 1 0 0 0 0 1
# ...
#sequential G-DINA model
sGDINA <- GDINA(dat,Q,sequential = TRUE)
sDINA <- GDINA(dat,Q,sequential = TRUE,model = "DINA")
anova(sGDINA,sDINA)
coef(sDINA) # processing function
coef(sDINA,"itemprob") # success probabilities for each item
coef(sDINA,"LCprob") # success probabilities for each category for all latent classes
####################################
# Example 10a. #
# Multiple-Group G-DINA model #
####################################
Q <- sim10GDINA$simQ
K <- ncol(Q)
# parameter simulation
# Group 1 - female
N1 <- 3000
gs1 <- matrix(rep(0.1,2*nrow(Q)),ncol=2)
# Group 2 - male
N2 <- 3000
gs2 <- matrix(rep(0.2,2*nrow(Q)),ncol=2)
# data simulation for each group
sim1 <- simGDINA(N1,Q,gs.parm = gs1,model = "DINA",att.dist = "higher.order",
higher.order.parm = list(theta = rnorm(N1),
lambda = data.frame(a=rep(1.5,K),b=seq(-1,1,length.out=K))))
sim2 <- simGDINA(N2,Q,gs.parm = gs2,model = "DINO",att.dist = "higher.order",
higher.order.parm = list(theta = rnorm(N2),
lambda = data.frame(a=rep(1,K),b=seq(-2,2,length.out=K))))
# combine data - all items have the same item parameters
dat <- rbind(extract(sim1,"dat"),extract(sim2,"dat"))
gr <- rep(c(1,2),c(3000,3000))
# Fit G-DINA model
mg.est <- GDINA(dat = dat,Q = Q,group = gr)
summary(mg.est)
extract(mg.est,"posterior.prob")
coef(mg.est,"lambda")
####################################
# Example 10b. #
# Multiple-Group G-DINA model #
####################################
Q <- sim30GDINA$simQ
K <- ncol(Q)
# parameter simulation
N1 <- 3000
gs1 <- matrix(rep(0.1,2*nrow(Q)),ncol=2)
N2 <- 3000
gs2 <- matrix(rep(0.2,2*nrow(Q)),ncol=2)
# data simulation for each group
# two groups have different theta distributions
sim1 <- simGDINA(N1,Q,gs.parm = gs1,model = "DINA",att.dist = "higher.order",
higher.order.parm = list(theta = rnorm(N1),
lambda = data.frame(a=rep(1,K),b=seq(-2,2,length.out=K))))
sim2 <- simGDINA(N2,Q,gs.parm = gs2,model = "DINO",att.dist = "higher.order",
higher.order.parm = list(theta = rnorm(N2,1,1),
lambda = data.frame(a=rep(1,K),b=seq(-2,2,length.out=K))))
# combine data - different groups have distinct item parameters
# see ?bdiagMatrix
dat <- bdiagMatrix(list(extract(sim1,"dat"),extract(sim2,"dat")),fill=NA)
Q <- rbind(Q,Q)
gr <- rep(c(1,2),c(3000,3000))
mg.est <- GDINA(dat = dat,Q = Q,group = gr)
# Fit G-DINA model
mg.est <- GDINA(dat = dat,Q = Q,group = gr,att.dist="higher.order",
higher.order=list(model = "Rasch"))
summary(mg.est)
coef(mg.est,"lambda")
personparm(mg.est)
personparm(mg.est,"HO")
extract(mg.est,"posterior.prob")
####################################
# Example 11. #
# Bug DINO model #
####################################
set.seed(123)
Q <- sim10GDINA$simQ # 1 represents misconceptions/bugs
N <- 1000
J <- nrow(Q)
gs <- data.frame(guess=rep(0.1,J),slip=rep(0.1,J))
sim <- simGDINA(N,Q,gs.parm = gs,model = "BUGDINO")
dat <- extract(sim,"dat")
est <- GDINA(dat=dat,Q=Q,model = "BUGDINO")
coef(est)
####################################
# Example 12. #
# SISM model #
####################################
# The Q-matrix used in Kuo, et al (2018)
# The first four columns are for Attributes 1-4
# The last three columns are for Bugs 1-3
Q <- matrix(c(1,0,0,0,0,0,0,
0,1,0,0,0,0,0,
0,0,1,0,0,0,0,
0,0,0,1,0,0,0,
0,0,0,0,1,0,0,
0,0,0,0,0,1,0,
0,0,0,0,0,0,1,
1,0,0,0,1,0,0,
0,1,0,0,1,0,0,
0,0,1,0,0,0,1,
0,0,0,1,0,1,0,
1,1,0,0,1,0,0,
1,0,1,0,0,0,1,
1,0,0,1,0,0,1,
0,1,1,0,0,0,1,
0,1,0,1,0,1,1,
0,0,1,1,0,1,1,
1,0,1,0,1,1,0,
1,1,0,1,1,1,0,
0,1,1,1,1,1,0),ncol = 7,byrow = TRUE)
J <- nrow(Q)
N <- 1000
gs <- data.frame(guess=rep(0.1,J),slip=rep(0.1,J))
sim <- simGDINA(N,Q,gs.parm = gs,model = "SISM",no.bugs=3)
dat <- extract(sim,"dat")
est <- GDINA(dat=dat,Q=Q,model="SISM",no.bugs=3)
coef(est,"delta")
####################################
# Example 13a. #
# user specified design matrix #
# LCDM (logit G-DINA) #
####################################
dat <- sim30GDINA$simdat
Q <- sim30GDINA$simQ
# LCDM
lcdm <- GDINA(dat = dat, Q = Q, model = "logitGDINA", control=list(conv.type="neg2LL"))
#Another way is to find design matrix for each item first => must be a list
D <- lapply(rowSums(Q),designmatrix,model="GDINA")
# for comparison, use change in -2LL as convergence criterion
# LCDM
lcdm2 <- GDINA(dat = dat, Q = Q, model = "UDF", design.matrix = D,
linkfunc = "logit", control=list(conv.type="neg2LL"),solver="slsqp")
# identity link GDINA
iGDINA <- GDINA(dat = dat, Q = Q, model = "GDINA",
control=list(conv.type="neg2LL"),solver="slsqp")
# compare all three models => identical
anova(lcdm,lcdm2,iGDINA)
####################################
# Example 13b. #
# user specified design matrix #
# RRUM #
####################################
dat <- sim30GDINA$simdat
Q <- sim30GDINA$simQ
# specify design matrix for each item => must be a list
# D can be defined by the user
D <- lapply(rowSums(Q),designmatrix,model="ACDM")
# for comparison, use change in -2LL as convergence criterion
# RRUM
logACDM <- GDINA(dat = dat, Q = Q, model = "UDF", design.matrix = D,
linkfunc = "log", control=list(conv.type="neg2LL"),solver="slsqp")
# identity link GDINA
RRUM <- GDINA(dat = dat, Q = Q, model = "RRUM",
control=list(conv.type="neg2LL"),solver="slsqp")
# compare two models => identical
anova(logACDM,RRUM)
####################################
# Example 14. #
# Multiple-strategy DINA model #
####################################
Q <- matrix(c(1,1,1,1,0,
1,2,0,1,1,
2,1,1,0,0,
3,1,0,1,0,
4,1,0,0,1,
5,1,1,0,0,
5,2,0,0,1),ncol = 5,byrow = TRUE)
d <- list(
item1=c(0.2,0.7),
item2=c(0.1,0.6),
item3=c(0.2,0.6),
item4=c(0.2,0.7),
item5=c(0.1,0.8))
set.seed(12345)
sim <- simGDINA(N=1000,Q = Q, delta.parm = d,
model = c("MSDINA","MSDINA","DINA",
"DINA","DINA","MSDINA","MSDINA"))
# simulated data
dat <- extract(sim,what = "dat")
# estimation
# MSDINA need to be specified for each strategy
est <- GDINA(dat,Q,model = c("MSDINA","MSDINA","DINA",
"DINA","DINA","MSDINA","MSDINA"))
coef(est,"delta")
## End(Not run)