See this example. It does not use lsline. It uses matrix operation to find the least square estimation for the data. It specifies the point to partition the dataset.
% Example dataset
x = rand(1, 10000)*3000;
y = randn(1, 10000)*1 - ((x-1000)/1000).^2;
x_partition = 1500;
x1 = x(x <= x_partition);
y1 = y(x <= x_partition);
x2 = x(x > x_partition);
y2 = y(x > x_partition);
X1 = [x1(:) ones(size(x1(:)))];
Y1 = y1(:);
X2 = [x2(:) ones(size(x2(:)))];
Y2 = y2(:);
coeff1 = X1\Y1; % least suare estimation of line
coeff2 = X2\Y2;
figure;
ax = axes();
hold(ax);
scatter(x1, y1, 'b.')
scatter(x2, y2, 'b.')
plot(x1, X1*coeff1, 'r', 'LineWidth', 3);
plot(x2, X2*coeff2, 'r', 'LineWidth', 3);
