This function creates a basic R list that will be used to initialize the stepmix object in python, in order to use the fit and predict function.
stepmix(n_components = 2, n_steps = 1,
measurement = "bernoulli", structural = "gaussian_unit",
assignment = "modal", correction = NULL,
abs_tol = 1e-10, rel_tol = 0, max_iter = 1000,
n_init = 1, init_params = "random", random_state = NULL,
verbose = 0, progress_bar = 1, measurement_params = NULL,
structural_params = NULL)It returns a list of type stepmixr that contains the arguments of the object.
The number of latent class. 2 by default.
1, 2, or 3, 1 by default. Number of steps in the estimation. Must be one of : 1: run EM on both the measurement and structural models.
2: first run EM on the measurement model, then on the complete model, but keep the measurement parameters fixed for the second step. See Bakk, 2018.
3: first run EM on the measurement model, assign class probabilities, then fit the structural model via maximum likelihood. See the correction parameter for bias correction.
See Bakk & Kuha (2018) for more details.
String describing the measurement model. See details for the different available model. The default model is "bernouilli"
String describing the structural model. See details for the different available model. The default model is "bernouilli"
String indicating the type of class assignments for 3-step estimation, "modal" by default. Must be one of:
soft: keep class responsibilities (posterior probabilities) as is.
modal: assign 1 to the class with max probability, 0 otherwise (one-hot encoding).
Bias correction for 3-step estimation. Must be one of :
None: No correction. Run Naive 3-step.
BCH: Apply the empirical BCH correction from Vermunt, 2004.
ML: Apply the ML correction from Vermunt, 2010, Bakk et al., 2013.
The convergence threshold. EM iterations will stop when the lower bound average gain is below this threshold. The default value is 1e-3.
The convergence threshold. EM iterations will stop when the relative lower bound average gain is below this threshold.
The number of EM iterations to perform.
The number of initializations to perform. The best results are kept.
"kmeans", or "random", default="random". The method used to initialize the weights, the means and the precisions. Must be one of:
kmeans : responsibilities are initialized using kmeans.
random : responsibilities are initialized randomly.
State instance or NULL, default=NULL. Controls the random seed given to the method chosen to initialize the parameters. Pass an int for reproducible output across multiple function calls.
Default=0. Enable verbose output. If 1, will print detailed report of the model and the performance metrics after fitting.
Display a tqdm progress bar during fitting
Default=NULL, Additional params passed to the measurement model class. Particularly useful to specify optimization parameters for stepmix.emission.covariate.Covariate. Ignored if the measurement descriptor is a nested object (see stepmix.emission.nested.Nested).
Default=NULL, Additional params passed to the structural model class. Particularly useful to specify optimization parameters for stepmix.emission.covariate.Covariate. Ignored if the structural descriptor is a nested object (see stepmix.emission.nested.Nested).
Éric Lacourse, Roxane de la Sablonnière, Charles-Édouard Giguère, Sacha Morin, Robin Legault, Félix Laliberté, Zsusza Bakk
The options for both the measurement and structural part are describe here:
bernoulli: The observed data consists of n_features bernoulli (binary) random variables.
bernoulli_nan: the observed data consists of n_features bernoulli (binary) random variables. Supports missing values.
binary: alias for bernoulli.
binary_nan: alias for bernoulli_nan.
categorical: alias for multinoulli.
categorical_nan: alias for multinoulli_nan.
continuous: alias for gaussian diag.
continuous_nan: alias for gaussian_diag_nan. supports missing values.
covariate: covariate model where class probabilities are a multinomial logistic model of the features.
gaussian: alias for gaussian_unit.
gaussian_nan: alias for gaussian_unit. Supports missing values.
gaussian_unit: each gaussian component has unit variance. Only fit the mean.
gaussian_unit_nan: each gaussian component has unit variance. Only fit the mean. Supports missing values.
gaussian_spherical: each gaussian component has its own single variance.
gaussian_spherical_nan: each gaussian component has its own single variance. Supports missing values.
gaussian_tied: all gaussian components share the same general covariance matrix.
gaussian_diag: each gaussian component has its own diagonal covariance matrix.
gaussian_diag_nan: each gaussian component has its own diagonal covariance matrix. Supports missing values.
gaussian_full: each gaussian component has its own general covariance matrix.
multinoulli: the observed data consists of n_features multinoulli (categorical) random variables.
multinoulli_nan: the observed data consists of n_features multinoulli (categorical) random variables. Supports missing values.
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
fit
model1 <- stepmix(n_components = 2, n_steps = 3)
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