[R] standard error of Binary logistic regression coefficient.

[Bessy]

Dear all,

I am struggling with the calculation of standard error of the coefficient in
Binary logistic regression analysis.

I built a binary logsitic regression model as follows and got confused since
the calculation of standard error of coefficients of X1, X2 and X3 are not
the same as the Linear regression.

> fit4 <-glm(Y~X1+X2+X3,data=d4,family=binomial("logit"))
Warning message:
In glm.fit(x = X, y = Y, weights = weights, start = start, etastart =
etastart,  :
  fitted probabilities numerically 0 or 1 occurred
> summary(fit4)

Call:
glm(formula = Y ~ X1 + X2 + X3, family = binomial("logit"), data = d4)

Deviance Residuals: 
          Min             1Q         Median             3Q            Max  
-1.641483e+00  -8.421161e-05   0.000000e+00   1.349398e-03   1.417550e+00  

Coefficients:
               Estimate     Std. Error  z value Pr(>|z|)  
(Intercept) -10.1534523  10.8397717 -0.93669 0.348921  
X1            0.3312469   0.3007324  1.10147 0.270693  
X2            0.1808757   0.1069222  1.69166 0.090711 .
X3            5.0874665   5.0820163  1.00107 0.316792  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 91.4954278  on 65  degrees of freedom
Residual deviance:  5.8129055  on 62  degrees of freedom
AIC: 13.812906

Number of Fisher Scoring iterations: 12


Could somebody suggest the calculation of standard error of X1, X2 and X3 in
the output of my model, please?

Any suggestions will be really appreciated.

Kind Regards

Bessy

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