This examples uses the vaccinesim
dataset from the inferference
package to compare the estimated covariance matrix obtained from geex
and sandwich
.
An example $\psi$ function written in R
.
This function computes the score functions for a GLM.
eefun <- function(data, model){ X <- model.matrix(model, data = data) Y <- model.response(model.frame(model, data = data)) function(theta){ lp <- X %*% theta rho <- plogis(lp) score_eqns <- apply(X, 2, function(x) sum((Y - rho) * x)) score_eqns } }
Compare sandwich variance estimators to sandwich
treating individuals as units:
library(geex) library(inferference) mglm <- glm(A ~ X1, data = vaccinesim, family = binomial) estimates <- m_estimate( estFUN = eefun, data = vaccinesim, root_control = setup_root_control(start = c(-.35, 0)), outer_args = list(model = mglm)) # Compare point estimates coef(estimates) # from GEEX coef(mglm) # from the GLM function # Compare variance estimates vcov(estimates) sandwich::sandwich(mglm)
Pretty darn good! Note that the geex
method is much slower than sandwich
(especially using method = 'Richardson'
for numDeriv
), but this is because sandwich
uses the closed form of the score equations, while geex
compute them numerically. However, geex
's real utility comes when you have more complicated estimating equations. Also, the analyst has the ability to code faster $\psi$ functions by optimizing their code or using Rccp
, for example.
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