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Fitting Functions

LRMoE.fit_LRMoE — Function
fit_LRMoE(Y, X, α_init, model; ...)

Fit an LRMoE model.

Arguments

  • Y: A matrix of response.
  • X: A matrix of covariates.
  • α: A matrix of logit regression coefficients.
  • model: A matrix specifying the expert functions.

Optional Arguments

  • expusure: an array of numerics, indicating the time invertal over which the count data (if applicable) are collected. If nothing is provided, it is set to 1.0 for all observations. It is assumed that all continuous expert functions are not affected by exposure.
  • exact_Y: true or false (default), indicating if Y is observed exactly or with censoring and truncation.
  • penalty: true (default) or false, indicating whether penalty is imposed on the magnitude of parameters.
  • pen_α: a numeric penalty on the magnitude of logit regression coefficients. Default is 1.0.
  • pen_params: an array of penalty term on the magnitude of parameters of component distributions/expert functions.
  • ϵ: Stopping criterion on loglikelihood (stop when the increment is less than ϵ). Default is 0.001.
  • α_iter_max: Maximum number of iterations when updating α. Default is 5.
  • ecm_iter_max: Maximum number of iterations of the ECM algorithm. Default is 200.
  • grad_jump: IN DEVELOPMENT
  • grad_seq: IN DEVELOPMENT
  • print_steps: true (default) or false, indicating whether intermediate updates of parameters should be logged.

Return Values

  • model_result.α_fit: Fitted values of logit regression coefficients α.
  • model_result.comp_dist: Fitted parameters of expert functions.
  • converge: true or false, indicating whether the fitting procedure has converged.
  • iter: Number of iterations passed in the fitting function.
  • ll: Loglikelihood of the fitted model (with penalty on the magnitude of parameters).
  • ll_np: Loglikelihood of the fitted model (without penalty on the magnitude of parameters).
  • AIC: Akaike Information Criterion (AIC) of the fitted model.
  • BIC: Bayesian Information Criterion (BIC) of the fitted model.
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Base.summary — Function
summary(obj)

Summarizes a fitted LRMoE model.

Arguments

  • obj: An object returned by fit_LRMoE function.

Return Values

Prints out a summary of the fitted LRMoE model on screen.

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