Calculating Bootstrap Standard Error using the formula
In this method to calculate the bootstrap standard error, the user needs to use the direct formula to get the same, simply without any use of any packages in the R programming language.
Example:
In this example, we will be using the direct formula to get the bootstrap standard error of the vector containing 10 elements in the R programming language.
R
# Create data gfg <- c(244,753,596,645,874,141,639,465,999,654) # Calculating the bootstrap standard error mean(replicate(100, sd(sample( gfg, replace=T))/sqrt(length(gfg)))) |
Output:
[1] 78.53055
How to Calculate a Bootstrap Standard Error in R?
In this article, we will be looking at the different approaches to calculate a bootstrap standard error using various packages and their functionalities in the R programming language.
Bootstrap Standard Error:
The standard deviation of the bootstrap samples (also known as the bootstrap standard error) is an estimate of the standard deviation of the sampling distribution of the mean.
Steps to calculate the bootstrap standard error of given data:
- Take k repeated samples with replacement from a given dataset.
- For each sample, calculate the standard error: s/√n.
- This results in k different estimates for the standard error. To find the bootstrapped standard error, take the mean of the k standard errors.