cm_step_1: Conditional-maximization step 1 (M-step 2)

In ben-j-barlow/rcbmm: Regularized Copula-Based Mixture Model: Estimation and Model Selection

Description

Implements the conditional-maximization step 1 of the ECM algorithm for regularized copula-based mixture model.

Usage

cm_step_1(
  x,
  K,
  z,
  mvdc,
  margins,
  trace = TRUE,
  restrictions = TRUE,
  variance_tolerance = 1e-05
)

Arguments

x	A numeric matrix or data frame of observations. Rows correspond to observations and columns correspond to variables.
K	The number of mixture components.
z	A numeric matrix representing the current value of the posterior probabilities of membership of the observations after the expectation step of the ECM algorithm. Columns are associated with a mixture component and rows are associated with observations. The current value of the probabilities should be computed via e.step.
mvdc	A list of objects of class mvdc. Each element of the list corresponds to a mixture component and contains the previous estimates for the component distribution's marginal and copula parameters.
margins	A character vector specifying the marginal distributions of the components in the mixture. The vector must have a length equal to the number of columns in x. Each element must be equal to "norm", "beta" or "gamma".
trace	A logical value indicating if an update regarding the step's progress should be displayed.
restrictions	A logical value indicating if the variance of each Beta marginal should be restricted
variance_tolerance	The lower bound for the variance of each Beta marginal, if restrictions = TRUE.

Value

A list of objects of class mvdc. Each element of the list corresponds to a mixture component and contains the updated estimates for the component distribution's marginal paramters and the same estimates as were parsed for the estimates of the copula parameter.

See Also

cm.step.2 ecm

ben-j-barlow/rcbmm documentation built on Feb. 12, 2021, 9:14 a.m.