puzzling results from logistic regression

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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