# [R] 95% bootstrap CIs

Rui Barradas ru|pb@rr@d@@ @end|ng |rom @@po@pt
Mon Sep 23 21:51:26 CEST 2019

```Hello,

Is this what you are looking for?

ci95 <- apply(my.data, 2, quantile, probs = c(0.025, 0.975))

Hope this helps,

Às 20:42 de 23/09/19, varin sacha via R-help escreveu:
> Dear R-Experts,
>
> Here is my reproducible R code to get the Mean squared error of GAM and MARS after I = 50 iterations/replications.
> If I want to get the 95% bootstrap CIs around the MSE of GAM and around the MSE of MARS, how can I complete/modify my R code ?
>
> Many thanks for your precious help.
>
> ##################
>
> library(mgcv)
>   library(earth)
> my.experiment <- function() {
> n<-500
> x <-runif(n, 0, 5)
>   z <- rnorm(n, 2, 3)
> a <- runif(n, 0, 5)
> y_model <- 0.1*x^3 - 0.5*z^2 - a + x*z + x*a + 3*x*a*z + 10
>   y_obs <- y_model +c( rnorm(n*0.97, 0, 0.1), rnorm(n*0.03, 0, 0.5) )
>   gam_model<- gam(y_obs~s(x)+s(z)+s(a))
> mars_model<-earth(y_obs~x+z+a)
> MSE_GAM<-mean((gam_model\$fitted.values - y_model)^2)
>   MSE_MARS<-mean((mars_model\$fitted.values - y_model)^2)
>   return( c(MSE_GAM, MSE_MARS) )
> }
> my.data = t(replicate( 50, my.experiment() ))
> colnames(my.data) <- c("MSE_GAM", "MSE_MARS")
> summary(my.data)
>
> ##################
>
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