Heya I am fitting linear mixed effects model in R and want to assess the model fit (with Animal number as random factor; repeated measures for Animals): ts.model <- lme(LOG_FOC_MW ~ R_DN_SUM + ANIMAL + SEX+ YY, data = t.data, random = ~ 1 | ANIMAL, correlation=corCAR1(0.2, form = ~1 | ANIMAL ), method='ML', na.action=na.omit)). Is there a possability to easly compute an R-square from the output of the model summary? I would appreciate any hint. Best regards Lukas ??? Lukas Indermaur, PhD student eawag / Swiss Federal Institute of Aquatic Science and Technology ECO - Department of Aquatic Ecology ?berlandstrasse 133 CH-8600 D?bendorf Switzerland Phone: +41 (0) 71 220 38 25 Fax : +41 (0) 44 823 53 15 Email: lukas.indermaur at eawag.ch www.lukasindermaur.ch
Indermaur Lukas <Lukas.Indermaur <at> eawag.ch> writes:> > Heya > > I am fitting linear mixed effects model in R [snip] > > ts.model <- lme(LOG_FOC_MW ~ R_DN_SUM + ANIMAL + SEX+ YY, data = t.data,random = ~ 1 | ANIMAL,> correlation=corCAR1(0.2, form = ~1 | ANIMAL ),method='ML', na.action=na.omit)).> > Is there a possability to easly compute an R-squarefrom the output of the model summary?> > I would appreciate any hint. > Best regards > > Lukas >You probably need to repost this on r-help instead of r-devel; it's not a "development" question. The other bad news for you is that I suspect it may be difficult to define r-squared uniquely for a mixed model. The resid() command will give you residuals, and you could take (1-resid()^2)/var(x) -- but how do you decide which var(x) to put in the denominator ... ? Ben Bolker