Hi Andrew, Please keep the mailing list in cc.
The estimates in mc are the differences of the parameter estimates (betas) between both levels. E.g. 5.LR -1.LR = -1.168 or 5.LR = 1.LR - 1.168 summary(mc) should give you the significance of those differences. That should work. If it doesn't, please provide more info: at least your code and the error message. A small reproducible example is better. confint(mc) gives the confidence intervals of the differences. Best regards, Thierry ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance Kliniekstraat 25 1070 Anderlecht Belgium + 32 2 525 02 51 + 32 54 43 61 85 thierry.onkel...@inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey Van: Andrew Halford [mailto:andrew.half...@gmail.com] Verzonden: dinsdag 21 oktober 2014 16:19 Aan: ONKELINX, Thierry Onderwerp: Re: [R-sig-eco] Logistic regression with 2 categorical predictors Hi Thierry, Thanks for the response. I have run your code but it seems you cant call the summary function, you just have to call the object on its own i.e. mc. The results are I get are below but I am not sure how to interpret these, exactly what does the estimate represent? How do I measure significance here? Estimate 2.LR - 1.LR == 0 1.252e-01 3.LR - 1.LR == 0 -5.390e-01 4.LR - 1.LR == 0 1.802e-02 5.LR - 1.LR == 0 -1.168e+00 6.LR - 1.LR == 0 -2.575e+01 1.SD - 1.LR == 0 7.411e-02 2.SD - 1.LR == 0 -2.408e-01 3.SD - 1.LR == 0 2.675e-01 etc etc Andy On 20 October 2014 23:04, ONKELINX, Thierry <thierry.onkel...@inbo.be> wrote: Dear Andrew, anova() and summary() test different hypotheses. anova() tests is at least one level is different from the others. summary() tests if the coefficient is different from zero. Multiple comparison of different interaction levels is probably the most relevant in this case. The easiest way is to make a new variable. snapper2$inter <- with(snapper2, interaction(age, test)) model <- glm(cbind(prefer,avoid) ~ 0 + inter, data=snapper2, family=binomial) library(multcomp) mc <- glht(model, mcp(inter = "Tukey)) summary(mc) Best regards, ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance Kliniekstraat 25 1070 Anderlecht Belgium + 32 2 525 02 51 + 32 54 43 61 85 thierry.onkel...@inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey -----Oorspronkelijk bericht----- Van: r-sig-ecology-boun...@r-project.org [mailto:r-sig-ecology-boun...@r-project.org] Namens Andrew Halford Verzonden: maandag 20 oktober 2014 16:06 Aan: r-sig-ecology@r-project.org Onderwerp: [R-sig-eco] Logistic regression with 2 categorical predictors 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 * * * * * * * * * * * * * D I S C L A I M E R * * * * * * * * * * * * * Dit bericht en eventuele bijlagen geven enkel de visie van de schrijver weer en binden het INBO onder geen enkel beding, zolang dit bericht niet bevestigd is door een geldig ondertekend document. The views expressed in this message and any annex are purely those of the writer and may not be regarded as stating an official position of INBO, as long as the message is not confirmed by a duly signed document. -- Andrew Halford Ph.D Research Scientist (Kimberley Marine Parks)| Adjunct Research Scientist (Curtin University) Dept. Parks and Wildlife Western Australia Ph: +61 8 9219 9795 Mobile: +61 (0) 468 419 473 * * * * * * * * * * * * * D I S C L A I M E R * * * * * * * * * * * * * Dit bericht en eventuele bijlagen geven enkel de visie van de schrijver weer en binden het INBO onder geen enkel beding, zolang dit bericht niet bevestigd is door een geldig ondertekend document. The views expressed in this message and any annex are purely those of the writer and may not be regarded as stating an official position of INBO, as long as the message is not confirmed by a duly signed document. _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
