extract elements from objects of various classes
Objects which can be extracted from GDINA
objects include:
AIC
attribute prior weights for calculating marginalized likelihood in the last EM iteration
all attribute patterns involved in the current calibration
BIC
Consistent AIC
covariance matrix of item probability parameter estimates; Need to specify SE.type
item parameter estimates
standard error of item probability parameter estimates; Need to specify SE.type
TRUE
if the calibration is converged.
raw data
deleted observation number
covariance matrix of delta parameter estimates; Need to specify SE.type
delta parameter estimates
standard error of delta parameter estimates; Need to specify SE.type
A list of design matrices for each item/category
deviance, or negative two times observed marginal log likelihood
GDINA discrimination index
expected # of examinees in each latent group answering item correctly
expected # of examinees in each latent group
higher-order model specifications
success probabilities for all latent classes
observed marginal log likelihood
link functions for each item
initial item category probability parameters
number of attributes
number of categories
number of groups
number of items
number of EM iterations
number of observations, or sample size
number of latent classes
prevalence of each attribute
posterior weights for each latent class
Reduced latent group for each item
Sample size Adusted BIC
is a sequential model fitted?
extract(object, what, ...)
object |
objects from class |
what |
what to extract |
... |
additional arguments |
## Not run: dat <- sim10GDINA$simdat Q <- sim10GDINA$simQ fit <- GDINA(dat = dat, Q = Q, model = "GDINA") extract(fit,"discrim") extract(fit,"designmatrix") ## End(Not run)
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