bootstrapped Pearson's correlation analysis in matlab

In MATLAB, you can perform bootstrapped Pearson's correlation analysis using the following steps:

1. Load or create your dataset with two continuous variables of interest.

2. Define the number of bootstrap samples you want to generate (e.g., nboot).

3. Initialize an empty array to store the correlation coefficients obtained from each bootstrap sample.

4. Use a loop to generate nboot bootstrap samples and calculate the Pearson's correlation coefficient for each sample. Here's an example code snippet:

% Step 1: Load or create your dataset
data = load('your_dataset.mat'); % Replace 'your_dataset.mat' with your actual dataset file

% Step 2: Define the number of bootstrap samples
nboot = 1000;

% Step 3: Initialize an array to store correlation coefficients
correlations = zeros(nboot, 1);

% Step 4: Perform bootstrapped correlation analysis
for i = 1:nboot
    % Generate a bootstrap sample with replacement
    bootstrap_sample = datasample(data, size(data, 1), 'Replace', true);
    
    % Calculate the Pearson's correlation coefficient for the bootstrap sample
    correlation_coefficient = corr(bootstrap_sample(:, 1), bootstrap_sample(:, 2));
    
    % Store the correlation coefficient in the array
    correlations(i) = correlation_coefficient;
end

5. After the loop, you can calculate the mean, standard deviation, and confidence intervals of the correlation coefficients obtained from the bootstrap samples using MATLAB's built-in functions. Here's an example:

% Calculate statistics from bootstrap results
mean_correlation = mean(correlations);
std_correlation = std(correlations);
confidence_interval = prctile(correlations, [2.5, 97.5]);

% Display the results
disp(['Mean correlation coefficient: ' num2str(mean_correlation)]);
disp(['Standard deviation of correlation coefficients: ' num2str(std_correlation)]);
disp(['95% Confidence interval: [' num2str(confidence_interval(1)) ', ' num2str(confidence_interval(2)) ']']);

Note that you need to replace 'your_dataset.mat' with the actual path and filename of your dataset file. Also, make sure the dataset is properly formatted, with the two variables of interest in separate columns.

These steps should help you perform bootstrapped Pearson's correlation analysis in MATLAB.