computeD: Computes the main Goodness of Fit Test Statistic

In BarkleyBG/glmerGOF: Goodness of Fit of Random Effect Distribution in Logistic Mixed Model

Description

This function takes the results of computeDeltas as formal arguments, and outputs the main test statistic introduced in Tchetgen and Coull (2006).

Usage

computeD(delta, sigma_delta)

Arguments

delta	the difference in estimated model coefficients
sigma_delta	the difference in estimated variance matrices (with respect to the variables that vary within cluster)

References

Tchetgen, E. J., & Coull, B. A. (2006) A Diagnostic Test for the Mixing Distribution in a Generalised Linear Mixed Model. Biometrika, 93(4), 1003-1010. https://doi.org/10.1093/biomet/93.4.1003

Examples

library(glmerGOF)
set.seed(1)
n <- 50
m <- 4
beta <- c(2, 2)
id <- rep(1:n, each=m)
x <- rnorm(m*n)
b <- rep(rnorm(n), each=m)
y <- rbinom(m*n, 1, plogis(cbind(1, x) %*% beta + b))
my_data <- data.frame(y,x,id)

variable_names <- list(DV = "y", grouping = "id")

library(lme4)
fit_glmm <- lme4::glmer(
  formula = y ~ x + (1|id),
  family = "binomial",
  data = my_data
)
library(survival)
fit_clogit <- survival::clogit(
  formula = y ~ x + strata(id),
  data = my_data,
  method = "exact"
)

test_results <- testGOF(
  data = my_data,
  fitted_model_clogit = fit_clogit,
  fitted_model_glmm  = fit_glmm,
  var_names = variable_names,
  gradient_derivative_method = "simple"
)
test_results

deltas <- do.call(computeDeltas, test_results$fits)
deltas

test_results_by_hand <- do.call(computeD, deltas)
test_results_by_hand

test_results$results

BarkleyBG/glmerGOF documentation built on July 18, 2019, 6:43 p.m.