R help - Jun 2005 - logistic regression (glm binary)

Hi
 
I am looking for a couple of pointers using glm (family = 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?
 
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?
 
Thanks
 
Stephen

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> -----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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