infmat_bes: infmat_bes

In bbreg: Bessel and Beta Regressions via Expectation-Maximization Algorithm for Continuous Bounded Data

infmat_bes

Description

Function to compute standard errors based on the Fisher information matrix for the bessel regression. This function can also provide the Fisher's information matrix.

Usage

infmat_bes(theta, z, x, v, link.mean, link.precision, information = FALSE)

Arguments

theta	vector of parameters (all coefficients: kappa and lambda).
z	response vector with 0 < z_i < 1.
x	matrix containing the covariates for the mean submodel. Each column is a different covariate.
v	matrix containing the covariates for the precision submodel. Each column is a different covariate.
link.mean	a string containing the link function for the mean. The possible link functions for the mean are "logit","probit", "cauchit", "cloglog".
link.precision	a string containing the link function the precision parameter. The possible link functions for the precision parameter are "identity", "log", "sqrt", "inverse".
information	optionally, a logical parameter indicating whether the Fisher's information matrix should be returned

Value

Vector of standard errors or Fisher's information matrix if the parameter 'information' is set to TRUE.

bbreg documentation built on March 18, 2022, 6:21 p.m.