CODEWITHSUNDEEP
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agbarnett
agbarnett

Reputation: 180

Mixed model without an intercept

I want to use a mixed model without a random intercept but with a correlation structure. The reason is to get the AIC to help choose the best correlation structure (e.g., autoregressive versus compound symmetry). So it is essentially a GEE, but GEEs don't allow estimation of the AIC. They are also called covariance pattern models.

The code below simulates random data with a compound symmetry correlation. The model fits both a random intercept and a variance-covariance matrix. Is there any way to switch off the random intercept?

library(MASS)
library(nlme)
Sigma = toeplitz(c(1,0.5,0.5,0.5))
data = data.frame(mvrnorm(n=10, mu=1:4, Sigma=Sigma))
data$id = 1:nrow(data)
long = reshape(data, direction='long', varying=list(1:4), v.names='Y')
cs = corCompSymm(0.5, form = ~ 1 | id)
model = lme(Y~time , random=list(~1|id), data=long, correlation=cs)
summary(model)

Upvotes: 1

Views: 781

Answers (1)

Liz Page-Gould
Liz Page-Gould

Reputation: 186

If you are solely interested in comparing correlation structures, then I am pretty sure your goal could be served by a generalized least squares model fit with gls:

model = gls(Y~time, data=long, correlation=cs)
summary(model)
AIC(model)

Otherwise, a linear mixed effects model fit with lme must have random effects specified.

Upvotes: 1

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