Hi Eric
The longitudinal lme model is no different from cross-sectional models when it comes to testing the significance of a covariate. You just simply use an F-test with the contrast [0 0 0 0 0 1] to test the last covariate in the first model. You can then drop the last covariate if the test is not significant.
The likelihood ratio test implemented in lme_mass_LR is for testing a different thing. For instance it can be used to compare a model with a single random effect (eg. random effect for the intercept term) against a model with two random effects (eg. considering both intercept and time as random effects).
Best -Jorge
El Martes 4 de marzo de 2014 19:15, Eric Cunningham etc42@hawaii.edu escribió:
Sorry!
I made a mistake with my example. I meant to say group_effect instead of rand_effect. It should read as follows:
Y1 = B1 + B2(time) + B3(group_effect1) + B4(group_effect*time) + B5(covariate_1) +B6(covariate_2) Y2 = B1 + B2(time) + B3(group_effect1) + B4(group_effect*time) + B5(covariate_1)
The difference between the two that I was trying to highlight is the presence of the second covariate term.
Thanks, -E
On Tue, Mar 4, 2014 at 11:18 AM, Eric Cunningham etc42@hawaii.edu wrote:
Hello Freesurfer experts,
I have a question about how to determine if a covariate is important in a mixed effects model
for example:
Y1 = B1 + B2(time) + B3(rand_effect1) + B4(rand_effect*time) + B5(covariate_1) +B6(covariate_2) Y2 = B1 + B2(time) + B3(rand_effect1) + B4(rand_effect*time) + B5(covariate_1)
Would it be appropriate to use the lme_mass_LR script? (the script says it is for comparing models with q and q+1 random effects).
Thanks, -E
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