Solved – Extracting Standard Errors Caret Model

I have tuned a glm net model with caret using the train function. I am trying to extract the coefficients and standard errors of those coefficients for the best tuned model. Following this CV post I figured out how extract the coefficients. As such, I use

coef <- as.matrix(coef(model$finalModel, model$bestTune$lambda)) 

to extract the coefficients. However, I cannot figure out how to get the standard errors. I tried se.coef (se.coef <- as.matrix(se.coef(model$finalModel, model$bestTune$lambda)) from the "arm" library but it threw the following error:

Error in as.matrix(se.coef(model$finalModel, model$bestTune$lambda)) :    error in evaluating the argument 'x' in selecting a method for function 'as.matrix': Error in (function (classes, fdef, mtable)  :    unable to find an inherited method for function ‘se.coef’ for signature ‘"lognet"’ 

Could anyone add any insight please? I understand that the se.coef may not work with the train function. However, is there another way to obtain the standard errors for the coefficients?

Thanks.

library(caret) library(boot)  n <- 500L  x1 <- rnorm(n, 2.0, 0.5) x2 <- rnorm(n, -1.0, 2) y <- factor(rbinom(n, 1L, plogis(-0.6 + 1.0 * x1 - 0.8 * x2)))  dat <- data.frame(y, x1, x2)  caretMod <- train(y ~ .,          data = dat,          method = "glmnet",         trControl = trainControl(method = "CV"))  bootSamples <- boot(dat, function(data, idx) {             bootstrapData <- data[idx, ]              bootstrapMod <- train(y ~ .,                      data = bootstrapData,                      method = "glmnet",                      trControl = trainControl(method = "none"),                     tuneGrid = caretMod$bestTune)              as.vector(coef(bootstrapMod$finalModel, caretMod$bestTune$lambda))         }, 100L)  Bootstrap Statistics :       original        bias    std. error t1*  0.1771481 -0.0232223436  0.27816559 t2*  0.5062797  0.0101922882  0.11641296 t3* -0.3857333  0.0002638111  0.02492466 

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