Hi Listers, I am trying to run a logistic regression to look at the effects of experiment type and age on the behavior of fish in a choice chamber experiment.
I am using the glm approach and would like some advice on how or whether to perform contrasts to work out what levels of Factor1 (Age) and Factor 2 (Test) are significantly different from each other. I have not been able to clarify from my reading what is the appropriate approach to take when dealing with a significant interaction term. I am also not sure as to how one interprets a model when all the coefficients are non-significant but the chi-square ANOVA shows a highly significant interaction term. I have graphed up the data as dot plots and there is definitely evidence of changes in proportions in later ages. I want to provide evidence for when and for which tests there was a 'significant' change in behavior. > snapper2 age test prefer avoid 1 1 LR 15 14 2 1 SD 15 13 3 1 SG 17 14 4 1 SW 14 14 5 2 LR 17 14 6 2 SD 16 19 7 2 SG 20 10 8 2 SW 15 21 9 3 LR 10 16 10 3 SD 14 10 11 3 SG 14 9 12 3 SW 13 15 13 4 LR 12 11 14 4 SD 14 11 15 4 SG 13 12 16 4 SW 11 14 17 5 LR 4 12 18 5 SD 8 8 19 5 SG 0 18 20 5 SW 10 6 21 6 LR 0 6 22 6 SD 3 4 23 6 SG 0 5 24 6 SW 5 3 > dotplot(age~prefer/avoid,data=snapper2,group=snapper2$test,cex=1.5,pch=19,ylab="age",auto.key=list(space="right",title="Tests")) > out2 <- glm(cbind(prefer,avoid) ~ age*test, data=snapper2, family=binomial) > summary(out2) Call: glm(formula = cbind(prefer, avoid) ~ age * test, family = binomial, data = snapper2) Deviance Residuals: [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 6.899e-02 3.716e-01 0.186 0.8527 age2 1.252e-01 5.180e-01 0.242 0.8091 age3 -5.390e-01 5.483e-01 -0.983 0.3256 age4 1.802e-02 5.589e-01 0.032 0.9743 age5 -1.168e+00 6.866e-01 -1.701 0.0890 . age6 -2.575e+01 9.348e+04 0.000 0.9998 testSD 7.411e-02 5.307e-01 0.140 0.8890 testSG 1.252e-01 5.180e-01 0.242 0.8091 testSW -6.899e-02 5.301e-01 -0.130 0.8964 age2:testSD -4.401e-01 7.260e-01 -0.606 0.5444 age3:testSD 7.324e-01 7.846e-01 0.933 0.3506 age4:testSD 8.004e-02 7.863e-01 0.102 0.9189 age5:testSD 1.024e+00 9.301e-01 1.102 0.2707 age6:testSD 2.532e+01 9.348e+04 0.000 0.9998 age2:testSG 3.738e-01 7.407e-01 0.505 0.6138 age3:testSG 7.867e-01 7.832e-01 1.004 0.3152 age4:testSG -1.321e-01 7.764e-01 -0.170 0.8649 age5:testSG -2.568e+01 8.768e+04 0.000 0.9998 age6:testSG 2.121e-02 1.334e+05 0.000 1.0000 age2:testSW -4.616e-01 7.249e-01 -0.637 0.5242 age3:testSW 3.959e-01 7.662e-01 0.517 0.6054 age4:testSW -2.592e-01 7.858e-01 -0.330 0.7415 age5:testSW 1.678e+00 9.386e-01 1.788 0.0737 . age6:testSW 2.626e+01 9.348e+04 0.000 0.9998 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 5.4908e+01 on 23 degrees of freedom Residual deviance: 2.6113e-10 on 0 degrees of freedom AIC: 122.73 Number of Fisher Scoring iterations: 23 > anova(out2, test="Chisq") Analysis of Deviance Table Model: binomial, link: logit Response: cbind(prefer, avoid) Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev Pr(>Chi) NULL 23 54.908 age 5 11.235 18 43.673 0.0469115 * test 3 1.593 15 42.079 0.6608887 age:test 15 42.079 0 0.000 0.0002185 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 cheers Andy [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology