[R] logistic regression (glm binary)

[Liaw, Andy]

> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch 
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of 
> Stephen Choularton
> Sent: Tuesday, June 07, 2005 11:49 PM
> To: R Help
> Subject: [R] logistic regression (glm binary)
> 
> 
> Hi
>  
> I am looking for a couple of pointers using glm (family = binary).

Do you mean "binomial" instead of "binary"?
  
> 1.  I want to add all the products of my predictive features as
> additional features (and I have 23 of them). Is there some easy way to
> add them?

Probably something along the line:

> dat <- data.frame(y=sample(0:1, 100, replace=TRUE), matrix(runif(300),
ncol=3))
> fm <- glm(y ~ .^2, family="binomial", data=dat)
> summary(fm)

Call:
glm(formula = y ~ .^2, family = "binomial", data = dat)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.654  -1.175   0.608   1.116   1.651  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)  
(Intercept)    3.264      1.536   2.125   0.0336 *
X1            -3.379      2.026  -1.668   0.0953 .
X2            -4.659      2.244  -2.077   0.0378 *
X3            -3.531      2.060  -1.714   0.0865 .
X1:X2          4.535      2.775   1.634   0.1022  
X1:X3          2.123      2.639   0.804   0.4212  
X2:X3          4.315      2.746   1.571   0.1161  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 137.99  on 99  degrees of freedom
Residual deviance: 131.84  on 93  degrees of freedom
AIC: 145.84

Number of Fisher Scoring iterations: 3

  
> 2. I want to drop each feature in turn and get the most significant,
> then drop two and get the next most significant, etc.  Is there some
> function that allows me to do this?

Not that I know of, and most likely for a very, very good reason...

Andy

> Thanks
>  
> Stephen
> 
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