Dear Peter and Eik, I am very grateful to you for your replies. My current understanding is that from the GLM analysis I can indeed conclude that the response predicted by System A is significantly different from that of System B, while the pairwise comparison A vs C leads to non significance. Now the Wald test seems to be correct only for Systems B vs C, indicating that the pairwise System B vs System C is significant. Am I correct?
However, my current understanding is also that I should use contrasts instead of the wald test. So the default contrasts is with the System A, now I should re-perform the GLM with another base. I tried to use the option "contrasts" of the glm: > fit1 <- glm(Response ~ System, data = scrd, family = "binomial", contrasts = contr.treatment(3, base=1,contrasts=TRUE)) > summary(fit1) > fit2 <- glm(Response ~ System, data = scrd, family = "binomial", contrasts = contr.treatment(3, base=2,contrasts=TRUE)) > summary(fit2) > fit3 <- glm(Response ~ System, data = scrd, family = "binomial", contrasts = contr.treatment(3, base=3,contrasts=TRUE)) > summary(fit3) However, the output of these three summary functions are identical. Why? That option should have changed the base, but apparently this is not the case. Another analysis I found online (at this link https://stats.stackexchange.com/questions/60352/comparing-levels-of-factors-after-a-glm-in-r ) to understand the differences between the 3 levels is to use glth with Tuckey. I performed the following: > library(multcomp) > summary(glht(fit, mcp(System="Tukey"))) Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: glm(formula = Response ~ System, family = "binomial", data = scrd) Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) B - A == 0 -1.2715 0.3379 -3.763 0.000445 *** C - A == 0 0.8588 0.4990 1.721 0.192472 C - B == 0 2.1303 0.4512 4.722 < 1e-04 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Adjusted p values reported -- single-step method) Is this Tukey analysis correct? I am a bit confused on what analysis I should do. I am doing my very best to study all resources I can find, but I would really need some help from experts, especially in using R. Best wishes FJ On Mon, Nov 12, 2018 at 1:46 PM peter dalgaard <pda...@gmail.com> wrote: > Yes, only one of the pairwise comparisons (B vs. C) is right. Also, the > overall test has 3 degrees of freedom whereas a comparison of 3 groups > should have 2. You (meaning Frodo) are testing that _all 3_ regression > coefficients are zero, intercept included. That would imply that all three > systems have response probablilities og 0.5, which is not likely what you > want. > > This all suggests that you are struggling with the interpretation of the > regression coefficients and their role in the linear predictor. This should > be covered by any good book on logistic regression. > > -pd > > > On 12 Nov 2018, at 14:15 , Eik Vettorazzi <e.vettora...@uke.de> wrote: > > > > Dear Jedi, > > please use the source carefully. A and C are not statistically different > at the 5% level, which can be inferred from glm output. Your last two > wald.tests don't test what you want to, since your model contains an > intercept term. You specified contrasts which tests A vs B-A, ie A- > (B-A)==0 <-> 2*A-B==0 which is not intended I think. Have a look at > ?contr.treatment and re-read your source doc to get an idea what dummy > coding and indicatr variables are about. > > > > Cheers > > > > > > Am 12.11.2018 um 02:07 schrieb Frodo Jedi: > >> Dear list members, > >> I need some help in understanding whether I am doing correctly a > binomial > >> logistic regression and whether I am interpreting the results in the > >> correct way. Also I would need an advice regarding the reporting of the > >> results from the R functions. > >> I want to report the results of a binomial logistic regression where I > want > >> to assess difference between the 3 levels of a factor (called System) on > >> the dependent variable (called Response) taking two values, 0 and 1. My > >> goal is to understand if the effect of the 3 systems (A,B,C) in System > >> affect differently Response in a significant way. I am basing my > analysis > >> on this URL: https://stats.idre.ucla.edu/r/dae/logit-regression/ > >> This is the result of my analysis: > >>> fit <- glm(Response ~ System, data = scrd, family = "binomial") > >>> summary(fit) > >> Call: > >> glm(formula = Response ~ System, family = "binomial", data = scrd) > >> Deviance Residuals: > >> Min 1Q Median 3Q Max > >> -2.8840 0.1775 0.2712 0.2712 0.5008 > >> Coefficients: > >> Estimate Std. Error z value Pr(>|z|) > >> (Intercept) 3.2844 0.2825 11.626 < 2e-16 *** > >> SystemB -1.2715 0.3379 -3.763 0.000168 *** > >> SystemC 0.8588 0.4990 1.721 0.085266 . > >> --- > >> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > >> (Dispersion parameter for binomial family taken to be 1) > >> Null deviance: 411.26 on 1023 degrees of freedom > >> Residual deviance: 376.76 on 1021 degrees of freedom > >> AIC: 382.76 > >> Number of Fisher Scoring iterations: 6 > >> Following this analysis I perform the wald test in order to understand > >> whether there is an overall effect of System: > >> library(aod) > >>> wald.test(b = coef(fit), Sigma = vcov(fit), Terms = 1:3) > >> Wald test: > >> ---------- > >> Chi-squared test: > >> X2 = 354.6, df = 3, P(> X2) = 0.0 > >> The chi-squared test statistic of 354.6, with 3 degrees of freedom is > >> associated with a p-value < 0.001 indicating that the overall effect of > >> System is statistically significant. > >> Now I check whether there are differences between the coefficients using > >> again the wald test: > >> # Here difference between system B and C: > >>> l <- cbind(0, 1, -1) > >>> wald.test(b = coef(fit), Sigma = vcov(fit), L = l) > >> Wald test: > >> ---------- > >> Chi-squared test: > >> X2 = 22.3, df = 1, P(> X2) = 2.3e-06 > >> # Here difference between system A and C: > >>> l <- cbind(1, 0, -1) > >>> wald.test(b = coef(fit), Sigma = vcov(fit), L = l) > >> Wald test: > >> ---------- > >> Chi-squared test: > >> X2 = 12.0, df = 1, P(> X2) = 0.00052 > >> # Here difference between system A and B: > >>> l <- cbind(1, -1, 0) > >>> wald.test(b = coef(fit), Sigma = vcov(fit), L = l) > >> Wald test: > >> ---------- > >> Chi-squared test: > >> X2 = 58.7, df = 1, P(> X2) = 1.8e-14 > >> My understanding is that from this analysis I can state that the three > >> systems lead to a significantly different Response. Am I right? If so, > how > >> should I report the results of this analysis? What is the correct way? > >> Thanks in advance > >> Best wishes > >> FJ > >> [[alternative HTML version deleted]] > >> ______________________________________________ > >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > >> 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. > > > > -- > > Eik Vettorazzi > > > > Department of Medical Biometry and Epidemiology > > University Medical Center Hamburg-Eppendorf > > > > Martinistrasse 52 > > building W 34 > > 20246 Hamburg > > > > Phone: +49 (0) 40 7410 - 58243 > > Fax: +49 (0) 40 7410 - 57790 > > Web: www.uke.de/imbe > > -- > > > > _____________________________________________________________________ > > > > Universitätsklinikum Hamburg-Eppendorf; Körperschaft des öffentlichen > Rechts; Gerichtsstand: Hamburg | www.uke.de > > Vorstandsmitglieder: Prof. Dr. Burkhard Göke (Vorsitzender), Prof. Dr. > Dr. Uwe Koch-Gromus, Joachim Prölß, Marya Verdel > > _____________________________________________________________________ > > > > SAVE PAPER - THINK BEFORE PRINTING > > ______________________________________________ > > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > > 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. > > -- > Peter Dalgaard, Professor, > Center for Statistics, Copenhagen Business School > Solbjerg Plads 3, 2000 Frederiksberg, Denmark > Phone: (+45)38153501 > Office: A 4.23 > Email: pd....@cbs.dk Priv: pda...@gmail.com > > > > > > > > > > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.