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?


Perhaps you could bootstrap your data? For example:

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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