bootstrap_stats function

Non-parametric boostrap of StepMix estimator.

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.

## 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.

Returns

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.

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

Author(s)

Éric Lacourse, Roxane de la Sablonnière, Charles-Édouard Giguère, Sacha Morin, Robin Legault, Félix Laliberté, Zsusza Bakk