[R] puzzling results from logistic regression

R. Michael Weylandt michael.weylandt at gmail.com
Wed Feb 29 16:20:31 CET 2012


Formally, look at Pr(>|z|). Informally, look at the null and residual
deviances from print(aa).

Michael

On Wed, Feb 29, 2012 at 10:14 AM, Michael <comtech.usa at gmail.com> wrote:
> How did you see it's non-significant?
>
> Thanks!
>
> On Wed, Feb 29, 2012 at 9:10 AM, Sarah Goslee <sarah.goslee at gmail.com>wrote:
>
>> On Wed, Feb 29, 2012 at 10:02 AM, Michael <comtech.usa at gmail.com> wrote:
>> > Hi all,
>> >
>> > As you can see from below, the result is strange...
>>
>> Not really.
>>
>> > I would imagined that the bb result should be much higher and close to 1,
>> > any way to improve the fit?
>> >
>> > Any other classification methods?
>> >
>> > Thank you!
>> >
>> > data=data.frame(y=rep(c(0, 1), times=100), x=1:200)
>> > aa=glm(y~x, data=data, family=binomial(link="logit"))
>> >
>> > newdata=data.frame(x=6, y=100)
>> > bb=predict(aa, newdata=newdata, type="response")
>> > bb
>> >
>> >
>> >> bb
>> >
>> > 1
>> >
>> > 0.4929125
>>
>>
>> What did you expect? Your model is completely nonsignificant; there's no
>> way to predict y from x, and that's what your predicted value tells you.
>>
>> > summary(aa)
>>
>> Call:
>> glm(formula = y ~ x, family = binomial(link = "logit"), data = data)
>>
>> Deviance Residuals:
>>   Min      1Q  Median      3Q     Max
>> -1.190  -1.177   0.000   1.177   1.190
>>
>> Coefficients:
>>             Estimate Std. Error z value Pr(>|z|)
>> (Intercept) -0.030152   0.283924  -0.106    0.915
>> x            0.000300   0.002450   0.122    0.903
>>
>> (Dispersion parameter for binomial family taken to be 1)
>>
>>    Null deviance: 277.26  on 199  degrees of freedom
>> Residual deviance: 277.24  on 198  degrees of freedom
>> AIC: 281.24
>>
>> Number of Fisher Scoring iterations: 3
>>
>>
>> I can only assume that you didn't construct the data frame that
>> you intended to test.
>>
>> --
>> Sarah Goslee
>> http://www.functionaldiversity.org
>>
>
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>
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