f_fun: Title: Penalized log-likelihood for Dirichlet Tree...

In Goodgolden/LDTM: Longitudinal Dirichlet Tree Multinomial Model

Title: Penalized log-likelihood for Dirichlet Tree Multinomial Regression

Description

Title: Penalized log-likelihood for Dirichlet Tree Multinomial Regression

Usage

f_fun(Ytree, X, B, model, alpha, lambda)

Arguments

Ytree	is the tree information from the Ytree function. Input will be a set of n * 2 matrices, each of which represent the an interior knot and its children branches
X	matrix of nxp which is the number of subjects by number of covariates
B	a list of covariate coefficients
model	character type of model to use for the Log Likelihood. Options are (Dirichlet Multinomial = "dirmult", Multinomial = "mult", or Dirichlet = "dir")
alpha	numeric the desired lasso parameter. In paper they used (0, 0.25, 0,5, and 1) to investigate the covariate selection
lambda	numericthe tuning parameter

Value

The penalized (negative) log likelihood method that estimates the parameters and selects coviariates simultaneously

Goodgolden/LDTM documentation built on May 25, 2022, 5:25 p.m.