Hello. I am fitting a 2-level mixed model using the lme() function involving two independent variables (?t? and ?starchR?) in which the intercept, both slopes and the interaction of the two slopes is also random: fit.rgr2<- lme(log(TotalDM)~t+starchR + t:starchR,random=(~t+t:starchR|Sp),data=dianna) The model converges normally without any warning messages. All of the fixed terms are clearly different from zero. Mmy working hypothesis requires that there also be a negative between?group correlation between the slope of ?t? and the interaction term (i.e. groups whose slope for ?t? is high at low values of ?starchR? have this slope decrease more rapidly as ?starchR? increases). When I fit the above mixed model using the lme() function, I indeed find a strong negative correlation of -0.867; here is the relevant part of the output from summary: StdDev Corr (Intercept) 1.650783941 (Intr) t t 0.055870605 -0.124 t:starchR 0.000309582 -0.340 -0.867 Residual 0.337147863 However, there are only 20 groups and I know that large absolute correlations between parameters can arise if the model is overparameterized. Question: how can I determine if the value of -0.867 is really different from zero? Intuitively, I would fit another model in which the covariance between the random components of ?t? and ?t:starchR? is constrained to be zero and then compare the two models via their likelihoods, but I don?t know how to fit such a constrained model in either lme() or lmer(). Any help or pointers to relevant literature would be appreciated. Thanks. Bill Shipley Laboratoire d??cologie Fonctionnelle D?partement de biologie, Universit? de Sherbrooke, Sherbrooke (Qc) Canada J1K 2R1 (819) 821-8000, poste 62079 Fax: (819) 821-8049 <http://www.billshipley.recherche.usherbrooke.ca/> http://www.billshipley.recherche.usherbrooke.ca/
testing for a significant correlation between random slopes
1 message · Bill Shipley