Estimating residuals bootstrap in R

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In summary, the conversation discusses the use of bootstrap to predict residual standard error in a linear model. The individual is having trouble finding information online and is seeking input on how to use bootstrap in R. The resampling aspect of bootstrap is mentioned and a helpful resource is provided.
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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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  • #2
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.
 

1. What is the purpose of estimating residuals bootstrap in R?

The purpose of estimating residuals bootstrap in R is to assess the accuracy and variability of a statistical model by generating new datasets through sampling with replacement from the original data and calculating the residuals for each sample.

2. How is residuals bootstrap performed in R?

Residuals bootstrap is performed in R by using the boot() function from the boot package, which takes in the original data, a function to calculate the residuals, and the number of bootstrap samples to generate.

3. What is the difference between parametric and non-parametric bootstrap?

The difference between parametric and non-parametric bootstrap lies in how the bootstrap samples are generated. Parametric bootstrap assumes that the data follows a specific distribution, such as a normal distribution, and samples from that distribution to generate new datasets. Non-parametric bootstrap, on the other hand, makes no assumptions about the underlying distribution and samples from the observed data itself.

4. How do you interpret the results of a residuals bootstrap in R?

The results of a residuals bootstrap in R can be interpreted by looking at the distribution of the bootstrap residuals. If the distribution is centered around zero with a low standard deviation, it indicates that the model is a good fit for the data. A wider distribution with a larger standard deviation suggests that the model may be overfitting the data.

5. What are the advantages of using R for estimating residuals bootstrap?

R is a popular and powerful statistical programming language that offers a wide range of functions and packages for data analysis and modeling. Using R for estimating residuals bootstrap allows for a more customizable and efficient approach, as well as the ability to perform additional analyses and visualizations on the bootstrap samples and results.

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