Logistics regression in R plotting Bootstrap using Titanic Dataset - r

Question

I am working on an exercise for a statistics online course. I need to create a logistic regression in R using the titanic dataset. Therefore I want to apply the bootstrap method to create and plot 95% confidence intervals for the prediction of the logistic regression.
When I run the bootstrap command and want to plot it, I get the error: "All values of t* are equal to 0.0159971772980342". Also, I get a bias and standard error of 0, which cannot be true. I guess there is an error in setting up the bootstrap command, but I unfortunately cannot find it. What can I try?
My Code:
library(boot)
set.seed(50000)
logit_test <- function(data, indices) {
dt <- data[indices,]
fit <- glm(Clean_data$Survived ~ Fare, data = Clean_data, family = "binomial")
return(coef(fit))
}
boot_strap <- boot(
data = Clean_data,
statistic = logit_test,
R = 100)
boot.ci(boot.out = boot_strap,
type = c("basic"))
#Now we look at the results and plot them
boot_strap
plot(boot_strap, index=2)
My Output:
> library(boot)
>
> set.seed(50000)
>
> logit_test <- function(data, indices) {
+ dt <- data[indices,]
+ fit <- glm(Clean_data$Survived ~ Fare, data = Clean_data, family = "binomial")
+ return(coef(fit))
+ }
> boot_strap <- boot(
+ data = Clean_data,
+ statistic = logit_test,
+ R = 100)
>
> boot.ci(boot.out = boot_strap,
+ type = c("basic"))
BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
Based on 100 bootstrap replicates
CALL :
boot.ci(boot.out = boot_strap, type = c("basic"))
Intervals :
Level Basic
95% (-0.8968, -0.8968 )
Calculations and Intervals on Original Scale
Some basic intervals may be unstable
> boot_strap
ORDINARY NONPARAMETRIC BOOTSTRAP
Call:
boot(data = Clean_data, statistic = logit_test, R = 100)
Bootstrap Statistics :
original bias std. error
t1* -0.89682819 0 0
t2* 0.01599718 0 0
> plot(boot_strap, index=2)
[1] "All values of t* are equal to 0.0159971772980342"

Answer 1

The problem is that you're bootstrap function isn't using the bootstrapped data to fit the model. You have this function:
logit_test <- function(data, indices){
dt <- data[indices, ]
fit <- glm(Clean_Travelers$Survived ~ FARE, data=Clean_Travelers,
family=binomial)
return(coef(fit))
}
Note that there are a couple of problems, one is that you should be using dt in the data= argument, but you should also not be using Clean_Travelers$Survived as the dependent variable, it should just be Survived because you want to ensure that you're taking that variable not from the original data, but from the bootstrapped data. Something like this for your bootstrap function should work:
logit_test <- function(data, indices){
dt <- data[indices, ]
fit <- glm(Survived ~ FARE, data=dt, family=binomial)
return(coef(fit))
}