Hi @Kahyun Choi
To automate the process of drawing multiple graphs based on a p-value threshold, you can modify your code to loop through the columns of your data, perform the regression, and then conditionally format and display the graphs that meet the p-value criterion.
% Assuming 'data' is a matrix with 91 rows and 99 columns of y-values
% and 'age' is a column vector with 91 elements
% and 'significant_columns' is a cell array with the names of the columns that have p < 0.05
significant_columns = {'STAI_T_1', 'STAI_T_2', ...}; % Add all significant column names here
% Determine the number of rows and columns for the subplot grid
num_graphs = length(significant_columns);
num_rows = floor(sqrt(num_graphs));
num_cols = ceil(num_graphs / num_rows);
% Create a figure to hold all subplots
f = figure;
% Loop through the significant columns and create subplots
for i = 1:num_graphs
subplot(num_rows, num_cols, i);
% Get the y-data for the current column
y_data = data(:, i); % Replace 'data(:, i)' with the actual way to index your y-data
% Perform the regression
[r, p, fig_scatter, fig_line] = func_regress(age, y_data);
% Check the p-value and adjust the line color if significant
if p < 0.05
fig_line.Color = [1 0.5 0.5];
end
% Set the x-ticks, x-limits, labels, and title
xticks(11:2:17); xlim([10 18]);
xlabel('age', 'FontSize', 14); ylabel(significant_columns{i}, 'FontSize', 14);
title(sprintf('r = %.2f, p = %.3f', r, p), 'FontSize', 12);
end
Assumptions:
- data(:, i) as a placeholder for how you might retrieve the y-data for each column. You'll need to replace this with the actual command that retrieves the y-values for each significant column in your dataset.
- significant_columns is a cell array containing the names of the columns that you've determined to have a p-value lower than 0.05. You would need to fill this array with the actual column names that meet your significance criteria.
Thanks,
Ayush
