自变量与因变量数据 二、计算自变量和因变量之间的相关性 correlation_matrix=corr(data(:,1:end-1),data(:,end),'type','Pearson');三、绘制相关性图 figure;heatmap(correlation_matrix,'Colormap',bone,...'XLabel','因变量','YLabel','自变量',...'YDisplay
MATLAB提供了 corrcoef 函数来计算两个变量之间的相关系数,对其求平方即可得到R平方值。 correlation_matrix = corrcoef(x, y); correlation = correlation_matrix(1, 2); r_squared = correlation^2; disp(['R-squared: ', num2str(r_squared)]); 运行结果为: R-squared: 0.73812 R^2越接近1说明误差越小...
Correlation Matrix Scatterplot (https://www.mathworks.com/matlabcentral/fileexchange/53043-correlation-matrix-scatterplot), MATLAB Central File Exchange. Retrieved May 19, 2025. Requires MATLAB None. MATLAB Release Compatibility Created with R2015a Compatible with any release Platform Compatibility...
correlation_matrix=abs(corr(standardized_data));```4.计算灰色关联度：利用关联系数计算灰色关联度。灰色关联度的计算方式可以根据具体的算法来确定，例如可以使用灰色关联度分析法中的一种。假设你采用灰色关联度分析法中的一种算法，比如最小值法：```matlab %假设correlation_matrix是关联系数矩阵 gray_relation_...
Matrix weights put weight on individual entries of the correlation matrix. A full matrix must be specified, but you can control which entries are more important to match. Alternatively, vector weights put weight on a full column (and the corresponding row). Fewer weights need to be specified ...
0.004 -0.082 0.08 -0.4 0.279 1]; % correlation matrix COV = zeros(k, k); for i = 1:k for j = 1:k COV(i, j) = corr(i, j) * Std(i) * Std(j); end end First_order_double_loop_2 = GSA_FirstOrder_mvn(Mu', COV, @func_beam, 1000); ...
% Load the example dataset and compute correlation matrix D = load('patients.mat'); D.IsFemale = ismember(D.Gender, 'Female'); [~, D.HealthStatus] = ismember(D.SelfAssessedHealthStatus, {'Poor', 'Fair', 'Good', 'Excellent'}); ...
% Calculate eigenvectors and eigenvalues of correlation matrix. [V, D]=eig(CM); % Get the eigenvalue sequence according to descending and the corrosponding % attribution rates and accumulation rates. for j=1:b DS(j,1)=D(b+1-j, b+1-j); ...
CorrelationHello who i can do the correlation between two numbers? thank youwhen called with two scalars or vectors produces a 2x2 matrix. The diagonal of the matrix is the correlation of the two vectors to themselves. These should always be 1 (unless there is a nan or an inf).ok...
All correlations in the plot are within the 2-standard-error confidence bounds. Therefore, none are significant. Plot the XCF for 60 lags on both sides of lag 0. Specify 3 standard errors for the confidence bounds. Get crosscorr(y1,y2,NumLags=60,NumSTD=3) ...