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MATLAB:plotmatrix中散乱数据的曲线拟合_Matlab_Curve Fitting_Plotmatrix - Fatal编程技术网

MATLAB:plotmatrix中散乱数据的曲线拟合

MATLAB:plotmatrix中散乱数据的曲线拟合,matlab,curve-fitting,plotmatrix,Matlab,Curve Fitting,Plotmatrix,下图是用[H,AX,BigAx,p]=plotmatrix(x)创建的在MATLAB中。是否可以有一条近似曲线来代替非对角线上的分散点? 阅读,该函数似乎只对散射有用,这是有意义的,因为通常矩阵中的点可能都是满的,拟合曲线没有意义。也许使用subplot()会更合适,并允许更多的通用性?使用创建图后,您可以在每个非对角散点图上循环,获取相关的和数据,然后如下所示: data = randn(50,3); % Random sample data [hScatter, hAxes] = plot

下图是用
[H,AX,BigAx,p]=plotmatrix(x)创建的在MATLAB中。是否可以有一条近似曲线来代替非对角线上的分散点?

阅读,该函数似乎只对散射有用,这是有意义的,因为通常矩阵中的点可能都是满的,拟合曲线没有意义。也许使用subplot()会更合适,并允许更多的通用性?

使用创建图后,您可以在每个非对角散点图上循环,获取相关的和数据,然后如下所示:

data = randn(50,3);  % Random sample data
[hScatter, hAxes] = plotmatrix(data);

for index = find(~eye(size(hScatter))).'  % Loop over off-diagonal plots
  X = get(hScatter(index), 'XData');      % Get X data
  Y = get(hScatter(index), 'YData');      % Get Y data
  betas = [ones(numel(X), 1) X(:)]\Y(:);  % Simple linear regression
  xLine = get(hAxes(index), 'XLim');      % Use axes limits for X data
  yLine = betas(1)+xLine.*betas(2);       % Compute regression line
  line(hAxes(index), xLine, yLine, 'Color', 'r');  % Plot red regression line
end
下面是结果图: