Minimum least square fitting with multiple variable

4 次查看（过去 30 天）
Darlington Mensah
Darlington Mensah 2018-12-25
编辑： Stephan 2018-12-25
Hello
I am trying to fit my data with a linear trend using MLS fitting. However, I don't understand how the initial guess affects the final results. I realized that by changing the initial guess from x0 = [1 1], the result of my variable change and I'm confused about deciding which one gives me the best fitting.
My working code and data are found below
data = readtable('data.xlsx');
xdata = table2array(data(:,1));
ydata = table2array(data(:,3));
x = linspace(min(xdata), max(xdata));
fun = @(a)a(1).*xdata + a(2) - ydata;
x0 = [1 1];
x1 = lsqnonlin(fun, x0);
figure
plot(xdata,ydata,'o')
hold on
plot(x, x1(1).*x + x1(2))
hold off

请先登录，再回答此问题。

回答（1 个）

Stephan
Stephan 2018-12-25
编辑：Stephan 2018-12-25
Hi,
you could use the resnorm to compare the quality of different approaches:
[x, resnorm] = lsqnonlin(...)
But the question is, why do you use a nonlinear approach for a linear problem? The kind of problem you have is usually solved optimal by mldivide (optimal in sense of least squares):
data = sortrows(readtable('data.xlsx'));
xdata = table2array(data(:,1));
ydata = table2array(data(:,3));
xdata(:,2)=1;
x = xdata\ydata;
scatter(xdata(:,1),ydata,'or')
hold on
plot(xdata(:,1), x(1).*xdata(:,1)+x(2))
hold off
fprintf('Results:\nx(1)=%.15f\nx(2)=%.9f',x(1),x(2))
Best regards
Stephan

类别

在 Help Center 和 File Exchange 中查找有关 Linear and Nonlinear Regression 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by