You may find the answers to this question on Cross Validated (along with the discussion) to be useful: https://stats.stackexchange.com/questions/35940/simulation-of-logistic-regression-power-analysis-designed-experiments
On Tue, Oct 10, 2017 at 10:09 AM, davide cortellino <davidecortell...@gmail.com> wrote: > Dear All > > > I have run the following GLM binominal model on a dataset composed by the > following variables: > > TRAN_DURING_CAMP_FLG enviados bono_recibido > 0 1 benchmark > 0 1 benchmark > 0 1 benchmark > 0 1 benchmark > 0 1 benchmark > 0 1 benchmark > > > - tran_during_flag= redemption yes/no (1/0) > - enviados= counter variables, all 1's > - bono_recibido= benchmark(control group) or test groups (two type of > test groups) > > The model used has been > > glm(TRAN_DURING_CAMP_FLG~bono_recibido,exp2,family="binomial") > > Estimate Std. Error z value > Pr(>|z|)(Intercept) -1.4924117 0.01372190 -108.761315 > 0.000000e+00 > bono_recibidoBONO3EUROS -0.8727739 0.09931119 -8.788274 1.518758e-18 > bono_recibidoBONO6EUROS 0.1069435 0.02043840 5.232480 1.672507e-07 > > The scope for this model was to test if there was significative difference > in the redemption rate between control group and test groups. Now, applying > the post hoc test: > >> Treat.comp<-glht(mod.binposthoc,mcp(bono_recibido='Tukey'))> >> summary(Treat.comp) # el modelo se encuentra en log odds aqui > > Simultaneous Tests for General Linear Hypotheses > Multiple Comparisons of Means: Tukey Contrasts > > Fit: glm(formula = TRAN_DURING_CAMP_FLG ~ bono_recibido, family = "binomial", > data = exp2) > Linear Hypotheses: > Estimate Std. Error z value Pr(>|z|) > BONO3EUROS - benchmark == 0 -0.87277 0.09931 -8.788 < 1e-09 *** > BONO6EUROS - benchmark == 0 0.10694 0.02044 5.232 3.34e-07 *** > BONO6EUROS - BONO3EUROS == 0 0.97972 0.09952 9.845 < 1e-09 > ***---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ > 1(Adjusted p values reported -- single-step method) > > It confirm that the differences are significatively differents, however, I > would check the power of the model in assessing these differences. I have > checked several time both on cross validates and on the web but it seems > there is no pre-made function which enable the user to compute the power of > glm models. Is it the case? Does anyone know of available packages or > methodologies to achive a power test in a glm binominal model? > > Bests > > [[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. -- Gregory (Greg) L. Snow Ph.D. 538...@gmail.com ______________________________________________ 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.
