# Logistic Regression bootstrapping gives 0 bias and standard error

I'm an R newbie and I'm trying to use logistic regression to predict Admission granted using 4 dependent variables - GPA, Gender, International student or not and SOP grade. Since I have only 113 data points, I used bootstrap.

  logit.bootstrap <- function(d, indices,formula) {
d<-d[indices,]
fit <- glm(Admission.Granted ~ GPA + Gender + International.student +
CLIgrade, data = data, family = "binomial")
return(coef(fit))
}

library(boot)
logit.boot <- boot(data=data,sim = "parametric", statistic=logit.bootstrap, R=5000, formula= Admission.Granted ~ GPA + Gender + International.student + CLIgrade)
logit.boot


The result I get has only 0 in Bias and Standard Error field. I don't know what I'm doing wrong.

PARAMETRIC BOOTSTRAP

Call:
boot(data = data, statistic = logit.bootstrap, R = 5000, sim = "parametric",
formula = Admission.Granted ~ GPA + Gender + International.student +

Bootstrap Statistics :
original  bias    std. error
t1*  1.5397795       0           0
t2*  0.5898814       0           0
t3*  0.1148014       0           0
t4* -0.8985390       0           0
t5* -0.1141786       0           0


My original logistic Regression summary is as below:

Call:
glm(formula = Admission.Granted ~ GPA + Gender + International.student +
CLIgrade, family = binomial, data = trainData)

Deviance Residuals:
Min       1Q   Median       3Q      Max
-2.2729   0.2912   0.4194   0.5987   0.9952

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept)                0.8936     5.0611   0.177   0.8599
GPA                        1.2303     1.4029   0.877   0.3805
GenderM                   -1.2121     0.7334  -1.653   0.0984 .
International.studentYes  -1.5696     0.7384  -2.126   0.0335 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 60.542  on 68  degrees of freedom
Residual deviance: 52.832  on 64  degrees of freedom
AIC: 62.832

Number of Fisher Scoring iterations: 5


For the parametric bootstrap it is necessary for the user to specify how the resampling is to be conducted. The best way of accomplishing this is to specify the function ran.gen which will return a simulated data set from the observed data set and a set of parameter estimates specified in mle.