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.

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#### Best Answer

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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