MPoisGraph: Fit a dependent Poisson graphical model

In hwang655/HUG: Dependent Graphical Model Estimation

Description

Fit a dependent Poisson graphical model via nodewise random effect Poisson regression.

Usage

MPoisGraph(Y.r, Y.p, B.ini, Omega = NULL, nlambda = 50, lambda.max = 10, lambda.min.ratio = 0.01)

Arguments

Y.r	response of nodewise regressions, an nxp matrix
Y.p	predictors of nodewise regressions, a matrix of the same dimension as Y.r
B.ini	a reasonable initial coefficient matrix for the nodewise Hurdle regression
Omega	(optional) precision matrix of the sample dependence model
nlambda	number of lambda values on grid (default 50)
lambda.max	maximum of the lambda sequence
lambda.min.ratio	ratio between the minimum and the maximum of the lambda sequence

Value

lambda	the lambda sequence used in nodewise regressions
graphs	a sequence of estimated graphs
coef.opt	a pxp matrix of EBIC-selected coefficient estimates of all nodewise regressions
coef.aic	a pxp matrix of AIC-selected coefficient estimates of all nodewise regressions
time	time spent on each regression in second

Author(s)

Jianyu Liu

See Also

GLMGraph, hugeGraph, MHurdGraph

hwang655/HUG documentation built on Jan. 31, 2021, 2:54 a.m.