[R] Bootstrapped CIs of MSE for (G)AM model

Rui Barradas

2018-11-22 22:45:14 UTC

Hello,

Sorry, there's a close parenthesis too many in the call to boot, the
very last one.
Delete it and it runs with no errors.

Rui Barradas

Post by Rui Barradas
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

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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