2

1

I've made a logistic regression to combine two independent variables in `R`

, using `pROC`

package and I obtain this:

```
summary(fit)
Call: glm(formula = Case ~ X + Y, family = "binomial", data = data)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5751 -0.8277 -0.6095 1.0701 2.3080
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.153731 0.538511 -0.285 0.775281
X -0.048843 0.012856 -3.799 0.000145 ***
Y 0.028364 0.009077 3.125 0.001780 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 287.44 on 241 degrees of freedom
Residual deviance: 260.34 on 239 degrees of freedom
AIC: 266.34
Number of Fisher Scoring iterations: 4
> fit
Call: glm(formula = Case ~ X + Y, family = "binomial", data = data)
Coefficients:
(Intercept) X Y
-0.15373 -0.04884 0.02836
Degrees of Freedom: 241 Total (i.e. Null); 239 Residual
Null Deviance: 287.4
Residual Deviance: 260.3 AIC: 266.3
```

Now I need to extract some information from this data and I'm not sure about how to do it. First, I need the model equation: suppose that fit is a combined predictor called `CP`

; could it be `CP=-0.15-0.05X+0.03Y`

?

Then, the resulting combined predictor from the regression should present a median value, so that I can compare median from the two groups `Case`

and `Controls`

which I used to make the regression (in other words, my `X`

and `Y`

variables are N-dimensional with `N = N1+N2`

, where `N1 = Number of Controls`

, for which `Case=0`

, and `N2 = Number of Cases`

, for which `Case=1`

).

IMHO, this question better fits

Cross ValidatedorStackOverflowSE sites. – Aleksandr Blekh – 2015-04-09T15:29:58.890