Dear R users, I have been having problems getting believable estimates from anova on a model fit from lmer. I get the impression that F is being greatly underestimated, as can be seen by running the example I have given below.
First an explanation of what I'm trying to do. I am trying to fit a glmm with binomial errors to some data. The experiment involves 10 shadehouses, divided between 2 light treatments (high, low). Within each shadehouse there are 12 seedlings of each of 2 species (hn & sl). 3 damage treatments (0, 0.1, 0.25 leaf area removal) were applied to the seedlings (at random) so that there are 4 seedlings of each species*damage treatment in each shadehouse. There maybe a shadehouse effect, so I need to include it as a random effect. Light is applied to a shadehouse, so it is outer to shadehouse. The other 2 factors are inner to shadehouse. We want to assess if light, damage and species affect survival of seedlings. To test this I fitted a binomial mixed effects model with lmer (actually with quasibinomial errors). THe summary function suggests a large effect of both light and species (which agrees with graphical analysis). However, anova produces F values close to 0 and p values close to 1 (see example below). Is this a bug, or am I doing something fundamentally wrong? If anova doesn't work with lmer is there a way to perform hypothesis tests on fixed effects in an lmer model? I was going to just delete terms and then do liklihood ratio tests, but according to Pinheiro & Bates (p. 87) that's very untrustworthy. Any suggestions? I'm using R 2.1.1 on windows XP and lme4 0.98-1 Any help will be much appreciated. many thanks Robert ############################### The data are somewhat like this #setting up the dataframe bm.surv<-data.frame( house=rep(1:10, each=6), light=rep(c("h", "l"), each=6, 5), species=rep(c("sl", "hn"), each=3, 10), damage=rep(c(0,.1,.25), 20) ) bm.surv$survival<-ifelse(bm.surv$light=="h", rbinom(60, 4, .9), rbinom(60, 4, .6)) # difference in probablility should ensure a light effect bm.surv$death<-4-bm.surv$survival # fitting the model m1<-lmer(cbind(survival, death)~light+species+damage+(1|house), data=bm.surv, family="quasibinomial") summary(m1) # suggests that light is very significant Generalized linear mixed model fit using PQL Formula: cbind(survival, death) ~ light + species + damage + (1 | table) Data: bm.surv Family: quasibinomial(logit link) AIC BIC logLik deviance 227.0558 239.6218 -107.5279 215.0558 Random effects: Groups Name Variance Std.Dev. table (Intercept) 1.8158e-09 4.2613e-05 Residual 3.6317e+00 1.9057e+00 # of obs: 60, groups: table, 10 Fixed effects: Estimate Std. Error DF t value Pr(>|t|) (Intercept) 2.35140 0.36832 56 6.3841 3.581e-08 *** lightl -1.71517 0.33281 56 -5.1535 3.447e-06 *** speciessl -0.57418 0.30085 56 -1.9085 0.06145 . damage 1.49963 1.46596 56 1.0230 0.31072 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Correlation of Fixed Effects: (Intr) lightl spcssl lightl -0.665 speciessl -0.494 0.070 damage -0.407 -0.038 -0.017 anova(m1) # very low F value for light, corresponding to p values approaching 1 Analysis of Variance Table Df Sum Sq Mean Sq Denom F value Pr(>F) light 1 0.014 0.014 56.000 0.0018 0.9661 species 1 0.002 0.002 56.000 0.0002 0.9887 damage 1 0.011 0.011 56.000 0.0014 0.9704 -- Robert Bagchi Animal & Plant Science Alfred Denny Building University of Sheffield Western Bank Sheffield S10 2TN UK t: +44 (0)114 2220062 e: [EMAIL PROTECTED] [EMAIL PROTECTED] http://www.shef.ac.uk/aps/apsrtp/bagchi-r ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html