You want to draw 10,000 samples of size 120 from a vector of size 532 with replacement following bootstrap resampling. Using the randsample function in a loop will help you achieve this requirement. Refer the code snippet below:
% Assume 'data' is your 532x1 column vector
data = rand(532, 1); % Example data, replace with your actual data
% Define parameters
num_samples = 10000; % Number of bootstrap samples
sample_size = 120; % Size of each sample
% Preallocate a matrix to store the bootstrap samples
bootstrap_samples = zeros(sample_size, num_samples);
% Perform bootstrap resampling
for i = 1:num_samples
bootstrap_samples(:, i) = randsample(data, sample_size, true);
end
Why is this the correct approach?
- randsample allows you to specify a sample size that is different from the original dataset size.
- Bootstrap resampling requires sampling with replacement to mimic the process of drawing from the same population multiple times. randsample provides a straightforward way to specify this with its true parameter.
- The loop structure allows you to repeat the sampling process a specified number of times (e.g., 10,000). Each iteration of the loop produces one bootstrap sample, effectively simulating the variability you would expect from repeated sampling.
- Each sample drawn in the loop is independent of the others, which is a fundamental property of bootstrap resampling.
