Hi all,
This is probably a very basic question. I would like to test, using
lmer, whether a 2-level null model (no covariates, only random
intercept) has a better fit (lower deviance) than a 'flat' 1-level null
model. My thought was to estimate both null models with lmer and then
with anova(M02level, M01level) test for the significance of the
chisquare difference. However, with lmer one cannot simply estimate a
1-level model, as far as my knowledge goes... What I did to circumvent
this was the followin, with Y being the dependent and country the
level-2 identifier:
one <- rep(1, length(Y))
M01level <- lmer (Y ~ (1 | one), REML=FALSE)
M02level <- lmer (Y ~ (1 | one) + (1 | country), REML=FALSE)
anova(M02level, M01level)
This seams to work properly. However, I don't feel really convinced by
this solution yet. Is it to be trusted, or is there a much simpler way
to test this using lmer? Thanks for any help!!!
Ben.