EMalgorithm: Parameter Estimation

In EMSNM: EM Algorithm for Sigmoid Normal Model

Description

Estimate paramters value by EM algorithm under the assumption of Sigmoid-Normal Model.

Usage

EMalgorithm(X, Y, Z, etat, alphat, sigmat, classsize = 2, learning_rate = 0.1, 
            regular_parameter_eta = 0.001, max_iteration = 10000,
            max_iteration_eta = 10000, compact_flag = FALSE, C0 = 5, C1 = 2, C2 = 9)

Arguments

X	the covariables of the mean of each subgroup
Y	the respond variable
Z	the covaraibles determining subgroup
etat	the coeffients determining subgroup
alphat	the coeffients of the mean of each subgroup
sigmat	the variance of Y
classsize	the number of subgroup types in your model assumption
learning_rate	learning rate of updating eta
regular_parameter_eta	regular value of updating eta by gradiant descending methond.
max_iteration	maximum steps of interation to avoid unlimited looping.
max_iteration_eta	maximal steps of eta interation to avoid unlimited looping.
compact_flag	if the value of eta is limited in a compact set, set it TRUE
C0	the maximum of intercept of eta.
C1	the minimum of the norm of slope of eta
C2	the maximum of the norm of slope of eta

Value

alpha	alpha estimation
eta	eta estimation
sigma	sigma estimation

Author(s)

Linsui Deng

Examples

#data generation
samplesize <- 1000
classsize <- 2
etasize <- 3
alphasize <- 3

set.seed(1)
Xtest <- data.frame(matrix(rnorm(samplesize*etasize),samplesize,etasize))
etatest <- matrix(c(1,2,-1,
                    0,0,0),etasize,classsize)

Ztest <- matrix(rnorm(samplesize*alphasize),samplesize,alphasize)
alphatest <- matrix(c(1,0,2,
                      5,0,7),alphasize,classsize)
sigmatest <- 5

Wtest <- Wgenerate(alpha=alphatest,eta=etatest,X=Xtest,Z=Ztest,sigma=sigmatest)

eta_initial <- matrix(c(rnorm(3),0,0,0),etasize,classsize)
alpha_initial<- matrix(rnorm(alphasize*classsize)*3,alphasize,classsize)
sigma_initial <- 1

EMtheta <- EMalgorithm(X=Wtest$X,Z=Wtest$Z,Y=Wtest$Y,classsize=2,
                       etat=eta_initial,alphat=alpha_initial,sigmat=sigma_initial,
                       learning_rate=0.01,regular_parameter_eta=0.001,
                       max_iteration=1000,max_iteration_eta=10000, 
                       compact_flag = TRUE, C0 = 5, C1 = 2, C2 = 9)

EMSNM documentation built on May 2, 2019, 1:41 p.m.