How did you see it's non-significant?

Thanks!

On Wed, Feb 29, 2012 at 9:10 AM, Sarah Goslee <sarah.goslee@gmail.com>wrote:

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