I am trying to run a bootstrapped binary logistic regression. I have loaded the dataset manually into Rstudio (don't know the syntax yet! actually the syntax I learned from Rcommander didn't work in Rstudio); and here is my code for the bootstrapped regression (thanks to this site):
(the variable names are masked for confidentiality)
When I ran it, it gave me these warnings:
I should add that the dataset was mean-centered for all the X variables. For the Y variables, the mean-centered variables was later converted to zeros and 1s (in the reverse format of the original zeros and 1s in the non-centered Y).
Now I am doing a
but it gives me a single confidence interval (possibly for the R-squared(?)) but I need the CIs for all the estimates. I am now working on that, but would appreciate any nice suggestions.
Besides, why it doesn't give any P values?! (just gives betas and biases ans SEs)
(the variable names are masked for confidentiality)
Code:
library (boot)
#defining a function which will be later used within the boot() function
GLM <- function(formula, data, indices) {
d <- data[indices, ] # allows boot to select sample
fit <- glm(Y ~ X1 +X2 +X3 +X4 +X5
+ [I]All the two-sided interactions of the X variables[/I]
family=binomial(logit), data=d)
return(coef(fit))
}
results <- boot(data=CenteredY01 , statistic = GLM, R= 100, formula= Y ~ X1 +X2 +X3 +X4 +X5 )
results
plot(results, index=1) # intercept
plot(results, index=2) # wt
plot(results, index=3) # disp
Code:
1: glm.fit: fitted probabilities numerically 0 or 1 occurred
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50: glm.fit: fitted probabilities numerically 0 or 1 occurredNow I am doing a
Code:
boot.ci(results, conf = 0.95, type = "basic")Besides, why it doesn't give any P values?! (just gives betas and biases ans SEs)
