Estimating residuals bootstrap in R

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To predict the residual standard error using bootstrap in R, a linear model has been established with the formula m1<-lm(y~x+I(x^2)). The user seeks guidance on applying the bootstrap method to estimate residuals, noting the challenge of dependent error terms. A helpful resource for understanding bootstrapping is provided, specifically a Wikipedia link. The R function 'sample' is recommended for the resampling process necessary for bootstrapping. This discussion highlights the need for practical examples and further clarification on implementing bootstrap techniques in R.
deanac
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Hello, I am trying to predict the residual standard error using bootstrap since they error terms are not independent. So far, I have found a linear model:
m1<-lm(y~x+I(x^2))
but I'm not sure how to use the boot strap to find the residuals.
I am having trouble finding any information online, so I would really appreciate any input.
Thank you!
 
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deanac said:
Hello, I am trying to predict the residual standard error using bootstrap since they error terms are not independent. So far, I have found a linear model:
m1<-lm(y~x+I(x^2))
but I'm not sure how to use the boot strap to find the residuals.
I am having trouble finding any information online, so I would really appreciate any input.
Thank you!

Bootstraping is very well explained here: http://en.wikipedia.org/wiki/Bootstrapping_(statistics)

in R you have to use the instruction sample for the resampling part of the method.
 

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