bootstrap_stats {stepmixr} | R Documentation |
Non-parametric boostrap of StepMix estimator.
Description
Non-parametric boostrap of StepMix estimator. Obtain boostrapped parameters and some statistics (mean and standard deviation). If a covariate model is used in the structural model, the output keys "cw_mean" and "cw_std" are omitted.
Usage
## S3 method for class 'stepmix.stepmix.StepMix'
bootstrap_stats(x, X = NULL, y = NULL, n_repetitions = 10, ...)
bootstrap_stats(x, ...)
Arguments
x |
An object created with the fit function |
X |
The X matrix or data.frame for the measurement part of the model |
y |
The y matrix or data.frame for the structural part of the model |
n_repetitions |
The number of bootsrap sample |
... |
for future options. Currently not used |
Details
This methods returns a list with bootstrap samples (samples
)
and the log-likelihood (rep_stats
). Mean and standard deviation
are added to the results.
Value
A list containing bootstrap samples of the parameters. The mean and
standard of class weights (cw_mean
, cw_std
),
measurement model parameters (mm_mean
, mm_std
),
structural model parameters (sm_mean
, sm_std
) are also
added. If a covariate model is used in the structural model, the
output keys cw_mean
and cw_std
are omitted.
Author(s)
Éric Lacourse, Roxane de la Sablonnière, Charles-Édouard Giguère, Sacha Morin, Robin Legault, Félix Laliberté, Zsusza Bakk
References
Bolck, A., Croon, M., and Hagenaars, J. Estimating latent structure models with categorical variables: One-step versus three-step estimators. Political analysis, 12(1): 3-27, 2004.
Vermunt, J. K. Latent class modeling with covariates: Two improved three-step approaches. Political analysis, 18 (4):450-469, 2010.
Bakk, Z., Tekle, F. B., and Vermunt, J. K. Estimating the association between latent class membership and external variables using bias-adjusted three-step approaches. Sociological Methodology, 43(1):272-311, 2013.
Bakk, Z. and Kuha, J. Two-step estimation of models between latent classes and external variables. Psychometrika, 83(4):871-892, 2018