How do I view logistic regression coefficients from each bootstrapped iteration using R I am relatively new to both R and bootstrapping.
My data set consists of a large number of variables, with the aim of generating a logistic regression model to predict a binary yes/no outcome.
As an example, I have created a 7-parameter model for which I am looking to bootstrap for a means of internal validation using the code below.
model = glm(outcome ~ parameter1 + parameter2 + etc..., 
            family = "binomial", 
            data = DATA)

logit.bootstrap = function(data, indices){
  d = data[indices, ] 
  fit = glm(formula(as.character(model$call)[2]), data = d, family = "binomial") 
  roc = pROC::roc(response = fit$y, 
                  predictor = fit$fitted.values)
  c = pROC::coords(roc,
                   x = "best", best.method = "youden", best.policy = "random",
                   ret = c("sensitivity", "specificity", "ppv", 
                           "npv"))

bootn = 10
set.seed(123)
logit.boot = boot(data = DATA, statistic = logit.bootstrap, R = bootn, 
                  sim = "ordinary", strata = DATA[,1])

Question:
I understand that for each bootstrapped iteration, the code will randomly sample individuals from my population and call the glm regression using the set parameters in the model. Each bootstrap iteration will deliver slightly different coefficients for each parameter based on the population sampled.
I would like to be able to view the coefficients generated for each bootstrapped iteration for each of the 7 chosen parameters to assess for stability. I essentially want to ensure the sign of each parameter does not change over the bootstrapping process. Obviously, I won't be checking all 1000, but is there a way to output this or at least 10-100 bootstrapped iterations?
Cheers

As per the comments above, your example is not replicable (e.g. you didn't provide the data and the statistic to be provided to boot is not correctly implemented), I'll show you a toy example using the mtcars dataset. If you are interested in checking the bootstrap estimates of the $\beta$'s, this example should be enough; e.g. the object betahat.boot$t below contains what you are looking for.
To run the bootstrap procedure in this example I find it easier to use the car package, which is just a wrapper around the boot
library(car)
m1 <- glm(am~wt + hp, data=mtcars)
  
betahat.boot <- Boot(m1, R=999) # same object as returned by a call to boot

# the object named t, contains the bootstrap estimates
head(betahat.boot$t)

# run a histogram of the bootstrap estimates for the intercept
hist(betahat.boot$t[,1])


