# GEE QIC update

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Here is improved code for calculating QIC from geeglm in geepack in R (original post). Let me know how it works. I haven’t tested it much, but is seems that QIC may select overparameterized models. In the code below, I had to replace <- with = because wordpress didn’t except <- within code or pre tags. It should still work just fine.

Here is a quick example of how to run this function. First, highlight and run the code below in R. That will save the function in your workspace. Then run your gee model using geeglm in geepack (package available from CRAN). Next, run QIC(your_gee_model) and you get the QIC. You can then repeat this with alternative a priori models. Below the function is an example using data available as part of geepack.

############################################################ # QIC for GEE models # Daniel J. Hocking ############################################################ QIC = function(model.R) { library(MASS) model.indep = update(model.R, corstr = "independence") # Quasilikelihood mu.R = model.R$fitted.values y = model.R$y type = family(model.R)$family quasi.R = switch(type, poisson = sum((y*log(mu.R)) - mu.R), gaussian = sum(((y - mu.R)^2)/-2), binomial = sum(y*log(mu.R/(1 - mu.R)) + log(1 - mu.R)), Gamma = sum(-y/(mu.R - log(mu.R))), stop("Error: distribution not recognized")) # Trace Term (penalty for model complexity) omegaI = ginv(model.indep$geese$vbeta.naiv) # Omega-hat(I) via Moore-Penrose generalized inverse of a matrix in MASS package #AIinverse = solve(model.indep$geese$vbeta.naiv) # solve via indenity Vr = model.R$geese$vbeta trace.R = sum(diag(omegaI %*% Vr)) px = length(mu.R) # number non-redunant columns in design matrix # QIC QIC = 2*(trace.R - quasi.R) [EDIT: original post was missing '*'] #QICu = (-2)*quasi.R + 2*px # Approximation assuming model structured correctly output = c(QIC, quasi.R, trace.R, px) names(output) = c('QIC', 'Quasi Lik', 'Trace', 'px') return(output) }

Here’s an example you can run in R.

library(geepack) data(dietox) dietox$Cu = as.factor(dietox$Cu) mf = formula(Weight ~ Cu * (Time + I(Time^2) + I(Time^3))) gee1 = geeglm(mf, data = dietox, id = Pig, family = poisson, corstr = "ar1") gee1 summary(gee1) mf2 = formula(Weight ~ Cu * Time + I(Time^2) + I(Time^3)) gee2 = geeglm(mf2, data = dietox, id = Pig, family = poisson, corstr = "ar1") summary(gee2) anova(gee2) anova(gee1, gee2) mf3 = formula(Weight ~ Cu + Time + I(Time^2)) gee3 = geeglm(mf3, data = dietox, id = Pig, family = poisson, corstr = "ar1") gee3.I = update(gee3, corstr = "independence") gee3.Ex = update(gee3, corstr = "exchangeable") sapply(list(gee1, gee2, gee3, gee3.I, gee3.Ex), QIC)

In the output of this model it suggests that model gee1 is the best model. I have some concerns that QIC will almost inevitably choose the most complex model. More testing with simulated data will be necessary.

[,1] [,2] [,3] [,4] [,5] QIC -333199.7 -333188.0 -333187.5 -333181.8 -333153.6 Quasi Lik 166623.0 166622.7 166620.4 166622.2 166615.4 Trace 23.2 28.7 26.6 31.3 38.6 px 861.0 861.0 861.0 861.0 861.0

You will get warnings when running this model because it uses a Poisson distribution for continuous data. I will work on finding a better example in the future before I make this available as an R package.

Filed under: GEE, Modeling, R Tagged: GEE, generalized estimating equations, QIC, R, regression

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