ptmixed (version 0.4.1)

wald.test: Wald test for regression coefficients

Description

Compute a multivariate Wald test for one of the following models: Poisson-Tweedie GLMM, negative binomial GLMM, Poisson-Tweedie GLM, negative binomial GLM. The null hypothesis has to be specified in the (matrix) form $L b = k$

Usage

wald.test(obj, L, k = NULL)

Arguments

obj

an object of class ptglmm (obtained from ptmixed or nbmixed) or ptglm (obtained from ptglm or nbglm)

L

a matrix used to define the hypothesis to test, in the form $L b = k$

k

a vector used to define the hypothesis to test, in the form $L b = k$. Default is a null vector ($L b = 0$)

Value

A data frame with the result of the test

Examples

# NOT RUN {
# generate data
set.seed(1234)
n = 50
group = rep(c(0,1), each = n/2)
age = rpois(n, lambda = 5)
beta = c(3, 0.05, 0.2, 0.03)
X = model.matrix(~group + age + age*group)
mu = exp(X %*% beta)
y = rep(NA, n) 
library(tweeDEseq)
for (i in 1:n) y[i] = rPT(1, mu = mu[i], D = 2, a = 0, max = 1000)
dataset = data.frame(y, group, age)
rm(list = setdiff(ls(), 'dataset'))
# estimate a negative binomial glm
fit1 = nbglm(formula = y ~ group + age + age*group, data = dataset)
# define L for beta2 = beta4 = 0
L = matrix(0, nrow = 2, ncol = 4)
L[1, 2] = L[2, 4] = 1              
# compute multivariate Wald test
wald.test(obj = fit1, L = L, k = NULL)
# }