# Power test binominal GLM model

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## Power test binominal GLM model

 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]] ______________________________________________ [hidden email] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.