glm_gd: Implement gradient descent GLM, constant

In kimgannon/bis557: Contains Homework Assignments for BIS 557 2020

Description

This function implements gradient descent GLM using both a constant adaptive step size

Usage

glm_gd(X, Y, mu_fun, lr, max_n = 25, tol = 1e-10)

Arguments

X	a model matrix, the X variables as columns
Y	a factor vector, the response variable
mu_fun	a function depending on Xbeta, giving the mean of the link function
lr	the learning rate
max_n	max number of iterations (a positive integer)
tol	the difference threshold for which we will exit the algorithm for iterations less than max_n

Examples

n <- 5000; p <- 3
beta <- c(-1, 0.2, 0.1)
X <- cbind(1, matrix(rnorm(n*(p-1)), ncol = p-1))
eta <- X %*% beta
lambda <- exp(eta)
y <- stats::rpois(n, lambda = lambda)
beta_hat <- glm_gd(X, y, mu_fun = function(eta) exp(eta), lr=0.00000001, max_n = 100000)

kimgannon/bis557 documentation built on Nov. 25, 2020, 7:09 a.m.