extract: Extract elements from objects of various classes

Description

A generic function to extract elements from objects of class CDM, validation or sim.data.

Objects which can be extracted from CDM object include:

analysis.obj: An GDINA object (@seealso GDINA) gained from GDINA package or an list after BM algorithm, depending on which estimation is used.
alpha: Individuals' attribute parameters calculated by EAP method
P.alpha.Xi: Individual's posterior probability
alpha.P: Individuals' marginal mastery probabilities matrix
P.alpha: Attribute prior weights for calculating marginalized likelihood in the last iteration
Deviance: deviance, or negative two times observed marginal log likelihood
npar: The number of parameters
AIC: AIC
BIC: BIC

Objects which can be extracted from validation object include:

Q.orig: The original Q-matrix that maybe contain some mis-specifications and need to be validated.
Q.sug: The Q-matrix that suggested by certain validation method.
time.cost: The time that CPU cost to finish the validation.
process: A matrix that contains the modification process of each item during each iteration. Each row represents an iteration, and each column corresponds to the q-vector index of respective item. The order of the indices is consistent with the row number in the matrix generated by the attributepattern function in the GDINA package. Only when maxitr > 1, the value is available.
iter: The number of iteration. Only when maxitr > 1, the value is available.
priority: An I × K matrix that contains the priority of every attribute for each item. Only when the search.method is "PAA", the value is available.
Hull.fit: A list containing all the information needed to plot the Hull plot, which is available only when method = "Hull".

Objects which can be extracted from sim.data object include:

dat: An N × I simulated item response matrix.
Q: The Q-matrix.
attribute: An N × K matrix for inviduals' attribute patterns.
catprob.parm: A list of non-zero category success probabilities for each attribute mastery pattern.
delta.parm: A list of delta parameters.
higher.order.parm: Higher-order parameters.
mvnorm.parm: Multivariate normal distribution parameters.
LCprob.parm: A matrix of item/category success probabilities for each attribute mastery pattern.

Usage

extract(object, what, ...)
# S3 method for CDM
extract(object, what, ...)
# S3 method for validation
extract(object, what, ...)
# S3 method for sim.data
extract(object, what, ...)

Arguments

object: objects from class CDM, validation, sim.data
what: what to extract
...: Additional arguments.

Methods (by class)

extract(CDM): various elements of CDM object
extract(validation): various elements of validation object
extract(sim.data): various elements of sim.data object

Examples

Run this code

set.seed(123)

library(Qval)

## generate Q-matrix and data to fit
K <- 3
I <- 30
example.Q <- sim.Q(K, I)
IQ <- list(
  P0 = runif(I, 0.0, 0.2),
  P1 = runif(I, 0.8, 1.0)
)
example.data <- sim.data(Q = example.Q, N = 1000, IQ = IQ,
                         model = "GDINA", distribute = "horder")
extract(example.data,"dat")


## using MMLE/EM to fit GDINA model
example.CDM.obj <- CDM(example.data$dat, example.Q, model = "GDINA",
                       method = "EM", maxitr = 2000, verbose = 1)
extract(example.CDM.obj,"alpha")
extract(example.CDM.obj,"npar")

example.MQ <- sim.MQ(example.Q, 0.1)
example.CDM.obj <- CDM(example.data$dat, example.MQ, model = "GDINA",
                       method = "EM", maxitr = 2000, verbose = 1)
                       
validation.obj <- validation(example.data$dat, example.MQ, 
                             example.CDM.obj, method = "MLR-B", eps = 0.90)
extract(validation.obj,"Q.sug")

Run the code above in your browser using DataLab