stepmixr (version 0.1.3)

mixed_descriptor: Utility function for mixture using mixed description.

Description

This function creates a data.frame ordered by continuous, binary and categorical columns. It also creates a list used if the model uses mixed column types.

Usage

mixed_descriptor(data, continuous = NULL, binary = NULL,
                 categorical = NULL, covariate = NULL)

Value

A list containing data and a descriptor.

Arguments

data

Data.frame with the mixed data

continuous

index or name of continuous column

binary

index or name of binary column

categorical

index or name of categorical column

covariate

index or name of covariate column

Author

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

Details

This methods returns a list of a data.frame sorted by continuous, binary and categorical columns. It contains also a descriptor that can be used in the measurement section.

References

Morin, S., Legault, R., Laliberté, F., Bakk, S., Giguère, C.-E., de la Sablonnière, R. (2025). Journal of Statistical Software. StepMix: A python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variables.<doi:10.18637/jss.v113.i08>

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

Examples

md <- mixed_descriptor(iris, continuous = 1:4, categorical = 5)