ParEst: Estimate theta and psi in multinomial mixture model

In MMDai: Multivariate Multinomial Distribution Approximation and Imputation for Incomplete Categorical Data

Description

This function is used to estimate theta and psi in multinomial mixture model given the number of components k.

Usage

ParEst(data, d, k, TT = 1000)

Arguments

data	- data in matrix formation with n rows and p columns
d	- number of categories for each variable
k	- number of components
TT	- number of iterations in Gibbs sampler, default value is 1000. T should be an even number for 'burn-in'.

Value

theta - vector of probability for each component

psi - specific probability for each variable in each component

Examples

# dimension parameters
n<-200; p<-5; d<-rep(2,p);
# generate complete data
Complete<-GenerateData(n, p, d, k = 3)
# mask percentage of data at MCAR
Incomplete<-Complete
Incomplete[sample(1:n*p,0.2*n*p,replace = FALSE)]<-NA
# k identify
K<-kIdentifier(data = Incomplete, d, TT = 10)
Par<-ParEst(data = Incomplete, d, k = K$k_est, TT = 10)

Example output

Loading required package: DirichletReg
Loading required package: Formula
Loading required package: rgl
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE 
3: .onUnload failed in unloadNamespace() for 'rgl', details:
  call: fun(...)
  error: object 'rgl_quit' not found 

MMDai documentation built on May 2, 2020, 9:05 a.m.