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Calculating standard errors and variance-covariance matrix using bootstrap methods

Source: R/bootSE.R
bootSE.Rd

This function conducts nonparametric and parametric bootstrap to calculate standard errors of model parameters. Parametric bootstrap is only applicable to single group models.

Usage

bootSE(
  GDINA.obj,
  bootsample = 50,
  type = "nonparametric",
  cores = 1,
  seed = 12345
)

Arguments

GDINA.obj

an object of class GDINA

bootsample

the number of bootstrap samples

type

type of bootstrap method. Can be parametric or nonparametric

cores

number of cores to be used for calculation; Default = 1; if cores>1, bootstrap will run in parallel with foreach package.

seed

random seed for resampling

Value

itemparm.se standard errors for item probability of success in list format

delta.se standard errors for delta parameters in list format

lambda.se standard errors for structural parameters of joint attribute distribution

boot.est resample estimates

References

Ma, W., & de la Torre, J. (2020). GDINA: An R Package for Cognitive Diagnosis Modeling. Journal of Statistical Software, 93(14), 1-26.

Author

Wenchao Ma, The University of Minnesota, wma@umn.edu

Examples

if (FALSE) { # \dontrun{
# For illustration, only 5 resamples are run
# results are definitely not reliable

dat <- sim30GDINA$simdat
Q <- sim30GDINA$simQ
fit <- GDINA(dat = dat, Q = Q, model = "GDINA",att.dist = "higher.order")
boot.fit <- bootSE(fit,bootsample = 5,seed=123)
boot.fit$delta.se
boot.fit$lambda.se
} # }