I have the following logistic model
Call:
glm(formula = Y ~ ., family = binomial, data = datasim)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.79670 -1.06758 0.00754 1.08200 1.69251
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.11504 1.02968 -0.112 0.91104
X1 0.17173 0.31899 0.538 0.59034
X2 -0.01573 0.28294 -0.056 0.95567
X3 -0.36905 0.29577 -1.248 0.21212
X4 -0.34710 0.29518 -1.176 0.23965
X5 0.01733 0.36088 0.048 0.96170
X6 -0.33492 0.38217 -0.876 0.38083
X7 0.20179 0.31615 0.638 0.52328
X8 -0.42734 0.26011 -1.643 0.10040
X9 -0.02750 0.29571 -0.093 0.92590
X10 0.76991 0.26840 2.868 0.00412 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 138.63 on 99 degrees of freedom
Residual deviance: 125.09 on 89 degrees of freedom
AIC: 147.09
Number of Fisher Scoring iterations: 4
And I calculate the "score",X of the model for example X=$null.deviance-$deviance. In this case X=138.63-125.09. Now, I want to use bootstrap to calculate for the mean and confidence interval of the score. How can I implement this in R?
Take a look at package boot. Here follows a simulated example, that suits your case:
library(boot)
x <- rnorm(100)
Y <- exp(x + rnorm(100)) > 1
datasim <- data.frame(Y, x)
dDeviance <- function(data, indices){
fit <- glm(formula = Y ~ ., family = binomial, data = data[indices, ])
with(fit, null.deviance - deviance)
}
boot(data = datasim, statistic = dDeviance, R = 100)
With output:
ORDINARY NONPARAMETRIC BOOTSTRAP
Call:
boot(data = datasim, statistic = dDeviance, R = 100)
Bootstrap Statistics :
original bias std. error
t1* 41.02692 1.445287 9.712626