On Tue, Sep 27, 2011 at 2:59 PM, Patrick Breheny <patrick.breheny@uky.edu>wrote:

> On 09/27/2011 07:53 AM, majesty wrote:
>
>> Dear subscribers,
>>
>> I am looking for a function which would allow me to model the dependent
>> variable as the number of successes in a series of Bernoulli trials. My
>> data
>> looks like this
>>
>> ID  TRIALS     SUCCESSESS   INDEP1 INDEP2 INDEP3
>> 1      4444        0                0.273     0.055  0.156
>> 2      98170      74               0.123     0.456  0.789
>> 3      145486    30               0.124     0.235  0.007
>> 4      147149    49               0.888     0.357  0.321
>> 5      60585      11               0.484     0.235  0.235
>> 6      198953    43               0.295     0.123  0.856
>>
>> I want to find out how independent variables influence the number of
>> successful trials (dependent variable)
>> I had tried to use glm formula
>>
>> regression.glm<- glm( SUCCESSESS ~ INDEP1 + INDEP2 + INDEP3, data = data,
>> family = binomial, weights= TRIALS)
>>
>
> Try
>
> regression.glm<- glm(cbind(SUCCESSESS,TRIALS) ~ INDEP1 + INDEP2 + INDEP3,
> data = data,family = binomial)
>
> That should be

regression.glm <-  glm(cbind(SUCCESSESS,TRIALS - SUCCESSESS) ~ INDEP1 +
INDEP2 + INDEP3, data = data,family = binomial)

i.e., the second column in the response is the number of failures.

as is specified in the details of ?glm.
>
> --
> Patrick Breheny
> Assistant Professor
> Department of Biostatistics
> Department of Statistics
> University of Kentucky
>
>
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-- 
Göran Broström

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