Many thanks.
Another question:
model<- glm(count ~ drug*result, family = poisson)
> anova(model,test="Chisq")
Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL 3 47.522
drug 1 0.032 2 47.491 0.85858
result 1 41.187 1 6.304 1.383e-10 ***
drug:result 1 6.304 0 0.000 0.01205 *
The pvalue of drug is 0.85858,which indicates that the total count of drug1 and
drug2 are not significantly different at 0.05 level.
But:
> summary(model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.0794 0.3536 5.882 4.06e-09 ***
drug2 0.9163 0.4183 2.190 0.0285 *
result2 1.9095 0.3788 5.041 4.64e-07 ***
drug2:result2 -1.1211 0.4650 -2.411 0.0159 *
The pvalue of drug2 is 0.0285 * ,which indicates that the total count of
drug2 is significantly different from drug1 at 0.05 level,which contradict to
the result of anova.
Can you give me some help about that ?
Many thanks.
Best
At 2013-05-13 18:56:01,"Achim Zeileis" <achim.zeil...@uibk.ac.at> wrote:
>On Mon, 13 May 2013, meng wrote:
>
>> Hi all:
>> I have a question about poisson regression.
>>
>> My data:
>> drug result count
>> 1 1 8
>> 1 2 54
>> 2 1 20
>> 2 2 44
>>
>> My model:
>> model<- glm(count ~ drug*result, family = poisson)
>>
>> My result:
>> summary(model)
>>
>> Coefficients:
>> Estimate Std. Error z value Pr(>|z|)
>> (Intercept) 2.0794 0.3536 5.882 4.06e-09 ***
>> drug2 0.9163 0.4183 2.190 0.0285 *
>> result2 1.9095 0.3788 5.041 4.64e-07 ***
>> drug2:result2 -1.1211 0.4650 -2.411 0.0159 *
>>
>> Calculation of coefficients:
>> Intercept: drug=1 and result=1,the corresponding count is 8.So log(8) is
>> 2.079442 which is Intercept.
>>
>> My question: How is drug2 calculated? log(64)-log(62) or something like
>> that(indicates the difference between drug2 and drug1) ? I've searched
>> and tried many times for all possible solutions,but the result is not
>> 0.9163.So,how is drug2 calculated ?
>
>It's the drug2 effect (on a log-scale) for result=1, i.e.,
>log(20) - log(8).
>
>The four coefficients are:
>
>R> y <- c(8, 20, 54, 44)
>R> c(log(y[1]), log(y[2]) - log(y[1]), log(y[3]) - log(y[1]),
>+ (log(y[4]) - log(y[3])) - (log(y[2]) - log(y[1])))
>[1] 2.0794415 0.9162907 1.9095425 -1.1210851
>
>> Many thanks!
>>
>>
>> Best
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
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>>
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