EMSNM (version 1.0)

update_eta: Updata Eta

Description

Updata eta in step t+1 with given data and coeffients estimated in step t.

Usage

update_eta(fun, alphat, sigmat, etat, X, Y, Z, learning_rate_eta = 0.001, 
           regular_parameter_eta = 0.001, max_iteration_eta = 10000)

Arguments

fun

the function updata eta

alphat

the estimated coeffients of the mean of each subgroup in step t

sigmat

the estimated standard error of Y in step t

etat

the estimated coeffients determining subgroup in step t

X

the covariables of the mean of each subgroup

Z

the covaraibles determining subgroup

Y

the respond variable

learning_rate_eta

learning rate of updating eta

regular_parameter_eta

regular value of updating eta by gradiant descending methond.

max_iteration_eta

maximal steps of eta interation to avoid unlimited looping.

Value

alpha

alpha estimated in step t.

eta

eta estimated in step t+1.

sigma

sigma estimated in step t.

Examples

# NOT RUN {
#some variables
samplesize <- 1000
classsize <- 6
etasize <- 3
alphasize <- 2

Xtest <- data.frame(matrix(rnorm(samplesize*etasize),samplesize,etasize))
Ztest <- matrix(rnorm(samplesize*alphasize),samplesize,alphasize)

etatest <- matrix(seq(1.15,1,length=etasize*classsize),etasize,classsize)
alphatest <- matrix(seq(1.15,1,length=alphasize*classsize),alphasize,classsize)
sigmatest <- 0.1

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

#test of update_eta
thetaupdate_eta <- update_eta(fun=eta_gradient_fun,alphat=alphatest,sigmat=sigmatest,
           etat=etatest,X=Wtest$X,Z=Wtest$Z,Y=Wtest$Y,
           learning_rate=0.1,regular_parameter=0.001,max_iteration=10000)
# }