Dear R-Gurus I am a PhD student from South Africa working on chimpanzee behaviour. I am looking at patterns of shade utilization and am using generalized linear mixed models to examine the effects of various factors on whether chimpanzees choose to spend time in the sun or shade. I realise that the lme4 package and the outputs of the lmer functions have been discussed ad nauseum but I have been reading through many of them and am finding it all extremely confusing. I have used programs like Statistica to run glm's with no random factors but now that I have to include random effects, this is no longer an option. Thus I have turned to R (and hence I am a complete R virgin).
What I would like to know is the following. What is the accepted general consensus on how to report the outputs of a lmer model? What is the currently accepted method for determining whether fixed effect parameters are significant in predicting the outcomes of the model (LHR, AIC, Wald X^2...?)? While I recognise that the "Pr(>|z|)" value is not a definitive p-value (rather an approximation), can one treat it loosely as an 'estimated' p-value? My model comprises 2 categorical predictor variables (Time of day: 'Time'; Available amount of shade, coded as a three-way classification: 'Tertile'), two continuous predictor variables (maximum temperature: 'Max'; minimum temperature: 'Min') and three random effects (Which experimental dataset the data were derived from: 'Exp'; Which individual chimpanzee was observed: 'Indiv'; Which area/zone of the enclosure they occupied at the time of observation: 'Zone'). These are the outputs that I have generated thus far using LHR testing. How should I be interpretting and reporting these outputs? Generalized linear mixed model fit by the Laplace approximation Formula: prop ~ Time + Tertile + (1 | Exp) + (1 | Indiv) + (1 | Zone) + Max + Min Data: sdata AIC BIC logLik deviance 215.5 259 -95.77 191.5 Random effects: Groups Name Variance Std.Dev. Zone (Intercept) 2.6596e-01 5.1571e-01 Indiv (Intercept) 0.0000e+00 0.0000e+00 Exp (Intercept) 2.9021e-11 5.3871e-06 Number of obs: 276, groups: Zone, 8; Indiv, 7; Exp, 2 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.15725 1.58304 -1.363 0.17297 Time11h00 0.96362 0.40956 2.353 0.01863 * Time12h00 1.57906 0.49033 3.220 0.00128 ** Time13h00 1.58951 0.40705 3.905 9.43e-05 *** Time14h00 1.07939 0.53876 2.003 0.04513 * TertileLOW -1.40906 0.53761 -2.621 0.00877 ** TertileMEDIUM -1.24862 0.57396 -2.175 0.02960 * Max 0.10122 0.08611 1.175 0.23985 Min 0.13439 0.10292 1.306 0.19162 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) Tm1100 Tm1200 Tm1300 Tm1400 TrtLOW TMEDIU Max Time11h00 0.056 Time12h00 0.258 0.447 Time13h00 0.115 0.510 0.486 Time14h00 -0.049 0.318 0.276 0.370 TertileLOW -0.146 -0.119 -0.215 -0.236 -0.096 TertlMEDIUM -0.128 0.024 -0.145 -0.155 -0.224 0.707 Max -0.914 -0.162 -0.277 -0.198 -0.084 -0.025 -0.022 Min 0.178 0.074 -0.023 0.105 0.244 -0.101 -0.077 -0.463 > anova(m1,m2) Data: sdata Models: m2: prop ~ Time + (1 | Exp) + (1 | Indiv) + (1 | Zone) + Max + Min m1: prop ~ Time + Tertile + (1 | Exp) + (1 | Indiv) + (1 | Zone) + m1: Max + Min Df AIC BIC logLik Chisq Chi Df Pr(>Chisq) m2 10 216.72 252.92 -98.359 m1 12 215.55 258.99 -95.773 5.1721 2 0.07532 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(m1,m3) Data: sdata Models: m3: prop ~ Tertile + (1 | Exp) + (1 | Indiv) + (1 | Zone) + Max + m3: Min m1: prop ~ Time + Tertile + (1 | Exp) + (1 | Indiv) + (1 | Zone) + m1: Max + Min Df AIC BIC logLik Chisq Chi Df Pr(>Chisq) m3 8 226.11 255.08 -105.057 m1 12 215.55 258.99 -95.773 18.567 4 0.0009556 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > anova(m1,m4) Data: sdata Models: m4: prop ~ Time + Tertile + (1 | Exp) + (1 | Indiv) + (1 | Zone) + m4: Min m1: prop ~ Time + Tertile + (1 | Exp) + (1 | Indiv) + (1 | Zone) + m1: Max + Min Df AIC BIC logLik Chisq Chi Df Pr(>Chisq) m4 11 214.81 254.64 -96.407 m1 12 215.55 258.99 -95.773 1.2672 1 0.2603 > anova(m1,m5) Data: sdata Models: m5: prop ~ Time + Tertile + (1 | Exp) + (1 | Indiv) + (1 | Zone) + m5: Max m1: prop ~ Time + Tertile + (1 | Exp) + (1 | Indiv) + (1 | Zone) + m1: Max + Min Df AIC BIC logLik Chisq Chi Df Pr(>Chisq) m5 11 215.22 255.05 -96.613 m1 12 215.55 258.99 -95.773 1.6792 1 0.195 As I understand this output, the only significant predictor in the model appears to be time of day. But, I don't really know how this should be reported. Can you point me to some papers or examples where lmer outputs have been reported formally? Any help that you could offer would be MOST appreciated. Sincerely (in desperation) Luke Duncan PhD Candidate School of Animal, Plant and Environmental Sciences University of the Witwatersrand Johannesburg, South Africa +27 11 717 6452 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
