How do you use R to create a bootstrap sample from a dataset?
Bootstrap sampling is a resampling technique used to estimate the distribution of a statistic by repeatedly sampling with replacement from the original dataset. This allows us to make inferences about the population from which the sample was drawn.
In R, we can use the sample() function to create a bootstrap sample. The sample() function takes two arguments: the vector or data frame from which to sample, and the number of samples to draw. For example, to create a bootstrap sample of size 100 from a vector of values called data, we would use the following code:
bootstrap_sample <- sample(data, 100, replace = TRUE)
We can then use the bootstrap sample to calculate the statistic of interest. For example, to calculate the mean of the bootstrap sample, we would use the following code:
mean_bootstrap <- mean(bootstrap_sample)
We can repeat this process multiple times to create a distribution of the statistic. This distribution can then be used to estimate the standard error of the statistic and make inferences about the population.
Related Questions
- What is the purpose of bootstrap sampling?
- What is the difference between bootstrapping and resampling?
- How do you calculate the standard error of a statistic using bootstrapping?
- What are the advantages and disadvantages of bootstrapping?
- Can bootstrapping be used to estimate the distribution of a categorical variable?
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