bootstrapped Pearson's correlation analysis matlab code

Certainly! Here's an example MATLAB code for performing bootstrapped Pearson's analysis:

% 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

% Step 5: 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)) ']']);

Please remember to replace 'your_dataset.mat' with the actual path and filename of your dataset file. Also, ensure that the dataset is properly formatted with the two variables of interest in separate columns.

This code generates nboot bootstrap samples by resampling from the original dataset with replacement. For each bootstrap sample, the Pearson's correlation coefficient is calculated using the `corr` function. The resulting correlation coefficients are stored in the `correlations` array.

After the loop, the mean, standard deviation, and 95% confidence interval of the correlation coefficients are computed using MATLAB's built-in functions (`mean`, `std`, `prctile`) and displayed as output.

Make sure to adapt this code to your specific dataset and requirements.