make_param_errors: Calculate parameter errors via inversion of the Hessian...

In emax.glm: General Tools for Building GLM Expectation-Maximization Models

Description

Calculate parameter errors via inversion of the Hessian matrix (either pracma or numeric approximations).

Usage

make_param_errors(params, x, y, weight, family = poisson(),
  method = "numeric", dispersion = 1)

Arguments

params	Optimal parameters
x	An n-by-p design matrix.
y	A vector of observation of length n.
weight	A n length vector of observation weight terms. This is currently designed to be either the exposure for a Poisson model or the number of trials for a Logistic model.
family	GLM family to fit.
method	Control string. Set to 'numeric' or 'pracma'.
dispersion	Model dispersion parameter for over/ under-dispersed models. Defaults to 1.

Value

Calculate the errors associated with each set of parameters.

Examples

x <- model.matrix(~ factor(wool) + factor(tension), warpbreaks)
y <- warpbreaks$breaks

m <- em.glm(x = x, y = y, K = 2, b.init = "random")
make_param_errors(m$params, x = x, y = y ,weight = c(1))

emax.glm documentation built on July 4, 2019, 5:04 p.m.