On Wed, Feb 29, 2012 at 10:02 AM, Michael <comtech....@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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