sugsComp: The computational function for the SUGS with variable...

In ococrook/sugsvarsel: SUGS algorithm of Wang and Dunson (2011), SUGS VarSel algorithm Crook et al.

Description

The computational function for the SUGS with variable selection algorithm.

Usage

sugsComp(mydata, intfeature, Model, mu_0 = NULL, lambda_0 = 0.01,
  nu_0 = NULL, S_0 = NULL, betaHat = c(0.01, 0.1, 1, 5, 10, 15, 30, 50,
  100), a = 10, b = 1, w = c(0.5, 0.5))

Arguments

mydata	Data matrix with observations as rows.
intfeature	A binary vector of feature which are parition as irrelevant (0) or relevant (1).
Model	A character string sating whether PML, ML or both are used for feature selection and returned.
mu_0	The mean hyperparameter, default is the column means of the data matrix.
lambda_0	The variance of the Guassian mean prior, the dafault value is 0.01.
nu_0	The degrees of freedom hyperparameter, the default value is D, where D is the number of variables.
S_0	The scale hyperparamter, the deault value is a fifth of the column variance of the data matrix.
betaHat	A grid of hyperparameters for the dirichlet concentration parameter, the default is c(0.01, 0.1, 1, 5, 10, 15, 30, 50, 100).
a	The scale of the gamma prior for the dirichlet concentration parameter, the dafault value is 10.
b	The rate of the gamma prior for the dirichlet concentration parameter, the default value is 1.
w	The prior probability of a variable belong to the irrelevant or relevant partition. The vector must contain two entries the first entry being the probabiliy of being irreleavnt and the second being the probability of being relevant The default value is c(0.5,0.5).

Value

An allocation vector, called member, of observation to clusters, K the number of clusters, n the number of observation in each cluster, the log PML or log ML or both as appropriate, a binary vector of features that have been classified as irrelevant (0) or relevant (1).

ococrook/sugsvarsel documentation built on May 27, 2019, 12:12 p.m.