[R] Power test binominal GLM model

[Greg Snow]

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
<davidecortellino at 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
>
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>
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-- 
Gregory (Greg) L. Snow Ph.D.
538280 at gmail.com