Dear Bert, I understand and thanks for your recommendation. Unfortunately I do not have any possibility to contact a statistical expert at the moment. So this forum experts' recommendation would be crucial to me to understand how R works in relation to my question. I hope that someone could reply to my last questions.
Best regards FJ On Mon, Nov 12, 2018 at 7:48 PM Bert Gunter <bgunter.4...@gmail.com> wrote: > Generally speaking, this list is about questions on R programming, not > statistical issues. However, I grant you that your queries are in something > of a gray area intersecting both. > > Nevertheless, based on your admitted confusion, I would recommend that you > find a local statistical expert with whom you can consult 1-1 if at all > possible. As others have already noted, you statistical understanding is > muddy, and it can be quite difficult to resolve such confusion in online > forums like this that cannot provide the close back and forth that may be > required (as well as further appropriate study). > > Best, > Bert > > On Mon, Nov 12, 2018 at 11:09 AM Frodo Jedi < > frodojedi.mailingl...@gmail.com> wrote: > >> 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. >> > [[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.
