Dear all<> I am running several generalized mixed model using lmer. <>The models typical look like this: model2xx<-lmer(numbers~Treatment+b+(1|Genotype)+(1|Field)+(1|Genotype:Treatment), family=quasipoisson)<> All factors are categorical. <>And the output looks like this: Generalized linear mixed model fit using Laplace formula:numbers~Treatment+Dead+(1|Genotype)+(1|Field)+(1|Genotype:Treatment), family=quasipoisson) Family: quasipoisson(log link) AIC BIC logLik deviance 1033 1051 -509.4 1019 <>Random effects: Groups Name Variance Std.Dev. Genotype:Treatment (Intercept) 0.40555 0.63683 Genotype (Intercept) 1.16642 1.08001 Field (Intercept) 1.23738 1.11238 Residual 9.47740 3.07854 number of obs: 100, groups: Genotype1:Treatment1, 14; Genotype1, 5; Felt1, 4 Fixed effects: <>Estimate Std. Error t value (Intercept) 3.20061 0.80010 4.000 Treatment a -0.05482 0.42619 -0.129 Treatment c 0.08316 0.46395 0.179 Dead No 0.27604 0.14873 1.856 Correlation of Fixed Effects:<> (Intr) Tretmnt1 Trtmnt1c Treatment a -0.247 Treatment c -0.239 0.450 Dead No -0.111 -0.132 0.003 <> I need some help to evaluate the importance of the random factors. The random factor of interest is Genotype. I have tried to delete random factors from the model and comparing the model with the original model by log likelihood-ratio statistics. Is this an appropriate method for testing the random factors in lmer? Is it possible to evaluate how much of the total variation the random factor Genotype explains? <> I have am a new user in lmer and my questions is probably very naive. But I appreciate any help. Thanks for the help. <> Line -- **************************************************************************** Line Johansen Department of Biology Norwegian University of Natural Science and Technology H?gskoleringen 15 No-7491 Trondheim Phone: 73 55 12 94