[R] 95% bootstrap CIs

[varin sacha]

Dear R-experts,

Below the reproducible example. I have tried to write a function that returns the statistic of interest (MSE in my case). I have run boot( ) where the function is included in the statistic argument. I have run boot.ci with the result from boot( ). I guess the error comes from the data : bootResults <- boot(data=?????,statistic=mse, R=1000)
Many thanks for your help.

##################################################
library(mgcv)

library(earth)

library(boot)

 
n<-2000

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 + 10

y_obs<-rnorm(n, y_model, 0.1)

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)

 
MSE_GAM

MSE_MARS

 

mse <- function(data,i) {

boot.gam <- gam(y_obs~s(x)+s(z)+s(a),data=data[i,])

return(mean(boot.gam$residuals^2))

}

bootResults <-boot(data=data,statistic=mse,R=1000)

 

mse <- function(data,i) {

boot.earth <- earth((y_obs~x+z+a),data=data[i,])

return(mean(boot.earth$residuals^2))

}

bootResults <-boot(data=data,statistic=mse,R=1000)
##################################################

 









Le lundi 23 septembre 2019 à 21:42:56 UTC+2, varin sacha via R-help <r-help using r-project.org> a écrit : 





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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