Hello,

There were several errors with your code. The following works but with the other CI types.


library(ISLR)
library(mgcv)
library(boot)

# function to obtain MSE
MSE <- function(data, indices, formula) {
  d <- data[indices, ] # allows boot to select sample
  fit <- gam(formula, data = d)
  ypred <- predict(fit)
  mean((d[["wage"]] - ypred)^2)
}

data(Wage)

# Make the results reproducible
set.seed(1234)

# bootstrapping with 1000 replications
results <- boot(data = Wage, statistic = MSE,
R = 1000, formula = wage ~ education + s(age, bs = "ps") + year))

# get 95% confidence intervals
# type = "bca" is throwing an error
ci.type <- c("norm","basic", "stud", "perc")
boot.ci(results, type = ci.type)



Hope this helps,

Rui Barradas

Às 20:36 de 22/11/2018, varin sacha via R-help escreveu:
Dear R-experts,

I am trying to get the bootstrapped confidence intervals of Mean squared error 
(MSE) for a (G)AM model. I get an error message.
Here below the reproducible R code. Many thanks for your response.

####################


install.packages("ISLR")

library(ISLR)

install.packages("mgcv")

library(mgcv)

install.packages("boot")

library(boot)

#MSE calculation

n=dim(Wage)[1]

p=0.667

GAM1<-gam(wage ~education+s(age,bs="ps")+year,data=Wage)

sam=sample(1 :n,floor(p*n),replace=FALSE)

Training =Wage [sam,]

Testing = Wage [-sam,]

ypred=predict(GAM1,newdata=Testing)

y=Testing$wage

MSE = mean((y-ypred)^2)


# Bootstrap 95% CI for MSE

# function to obtain MSE
MSE <- function(formula, data, indices) {
   d <- data[indices,] # allows boot to select sample
   fit <- gam(formula, data=d)
   return(MSE(fit))

} # bootstrapping with 1000 replications
results <- boot(data=Wage, statistic=MSE,
    R=1000, formula=gam(wage ~education+s(age,bs="ps")+year,data=Wage)

)
# get 95% confidence intervals
boot.ci(results, type="bca")

##########################

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