gdMomentum_GLM: Generalized Linear Model first order MLE using momentum...

In nixgank-wang/bis557: What the Package Does (One Line, Title Case)

Description

This function implement a first-order solution for the GLM maximum likelihood problem using only gradient information with adaptive momentum gradient descent algorithm

Usage

gdMomentum_GLM(X, y, mu_fun, lambda, maxit = 10000, tol = 1e-05, gamma = 0.8)

Arguments

X	The design matrix
y	The response variable
mu_fun	The function of eta, which is the matrix multiplication of design matrix with vector beta
lambda	The stepsize in gradient descent updata
maxit	Integer maximum number of iterations
tol	Numeric tolerance parameter
gamma	The momentum term which indicates how much acceleration you want

Examples

 pd <- tibble(offers=c(rep(0,50),rep(1,30),rep(2,10),rep(3,7),rep(4,3)),
division=sample(c("A","B","C"),100,replace = TRUE),
exam=c(runif(50,60,80),runif(30,65,95),runif(20,75,95)))
y<- matrix(pd$offers,ncol = 1)
X<- model.matrix(offers~division+exam,data=pd)
gdMomentum_GLM(X,y,mu_fun=function(eta) exp(eta),lambda = 1e-3)

nixgank-wang/bis557 documentation built on Dec. 26, 2020, 9:54 p.m.