This all looks bogus to me; you've fit the data perfectly by fitting a saturated model - there are no residual degrees of freedom and (effectively) zero residual deviance. Things are clearly amiss because you have huge standard errors. You have 24 data points and fit a model with 23 coefficient plus the intercept; you just replaced your data with 24 new data points (the values in the Estimate column of the summary() output)
I really wouldn't bother interpreting it any further. HTH G On 21 October 2014 18:21, Andrew Halford <andrew.half...@gmail.com> wrote: > Hi Thierry, > > The multiple comparisons ran just fine but there was a ridiculous amount of > interaction combinations all of which were non-significant even though > there was a highly significant interaction term. I decided to remove test > as a variable to simplify the analysis and run separate single explanatory > variable logistic regressions. I have included a result below which is > still producing an outcome I cant explain. Namely, why am I getting such a > significant result for the ANOVA but when I do the tukey tests nothing is > significant? > > > sg_habitat > Age Prefer Avoid > 1 1 17 14 > 2 2 20 10 > 3 3 14 9 > 4 4 13 12 > 5 5 0 18 > 6 6 0 5 > > > model_sg <- glm(cbind(Prefer,Avoid) ~ Age, data=sg_habitat, > family=binomial) > > > anova(model_sg, 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 5 36.588 > Age 5 36.588 0 0.000 7.243e-07 *** > > > > mc_sg <- glht(model_sg, mcp(Age = "Tukey")) > > > summary(mc_sg) > > Simultaneous Tests for General Linear Hypotheses > > Multiple Comparisons of Means: Tukey Contrasts > > > Fit: glm(formula = cbind(Prefer, Avoid) ~ Age, family = binomial, > data = sg_habitat) > > Linear Hypotheses: > Estimate Std. Error z value Pr(>|z|) > 2 - 1 == 0 0.4990 0.5294 0.943 0.912 > 3 - 1 == 0 0.2477 0.5593 0.443 0.997 > 4 - 1 == 0 -0.1141 0.5390 -0.212 1.000 > 5 - 1 == 0 -25.8473 53178.5362 0.000 1.000 > 6 - 1 == 0 -24.7307 57729.9299 0.000 1.000 > 3 - 2 == 0 -0.2513 0.5767 -0.436 0.997 > 4 - 2 == 0 -0.6131 0.5570 -1.101 0.844 > 5 - 2 == 0 -26.3463 53178.5362 0.000 1.000 > 6 - 2 == 0 -25.2296 57729.9299 0.000 1.000 > 4 - 3 == 0 -0.3618 0.5855 -0.618 0.985 > 5 - 3 == 0 -26.0950 53178.5362 0.000 1.000 > 6 - 3 == 0 -24.9783 57729.9299 0.000 1.000 > 5 - 4 == 0 -25.7332 53178.5362 0.000 1.000 > 6 - 4 == 0 -24.6165 57729.9299 0.000 1.000 > 6 - 5 == 0 1.1167 78490.1364 0.000 1.000 > (Adjusted p values reported -- single-step method) > > > On 21 October 2014 22:53, ONKELINX, Thierry <thierry.onkel...@inbo.be> > wrote: > > > 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. > > > > > > -- > 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 > > [[alternative HTML version deleted]] > > _______________________________________________ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > -- Gavin Simpson, PhD [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology