conditional: Conditionalize a Gaussian mixture model

In gmgm: Gaussian Mixture Graphical Model Learning and Inference

Conditionalize a Gaussian mixture model

Description

This function conditionalizes a Gaussian mixture model (Sun et al., 2006).

Usage

conditional(gmm, y = rownames(gmm$mu)[1])

Arguments

gmm	An object of class gmm.
y	A character vector containing the dependent variables (by default the first variable of gmm).

Value

A list with elements:

alpha	A numeric vector containing the mixture proportions.
mu_x	A numeric matrix containing the marginal mean vectors of the explanatory variables bound by column.
sigma_x	A list containing the marginal covariance matrices of the explanatory variables.
coeff	A list containing the regression coefficient matrices of the dependent variables on the explanatory variables.
sigma_c	A list containing the conditional covariance matrices.

References

Sun, S., Zhang, C. and Yu, G. (2006). A Bayesian Network Approach to Traffic Flow Forecasting. IEEE Transactions on Intelligent Transportation Systems, 7(1):124–132.

Examples

data(gmm_body)
cond <- conditional(gmm_body)

gmgm documentation built on Sept. 9, 2022, 1:07 a.m.