Least squares fitting where we pre-determine the slope
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Hello all,
I have a set of data, say on a scatter plot, and I have a line that has a pre-determined slope (i.e. not determined by the data on the scatter plot but determined by ideal calculations). I would like to move the line through the data until it has the least amount of offset (like least-squares fitting) but without changing the slope of the line. Is there a least-squares fitting function that lets you pre-determine the slope?
Cheers!
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Ameer Hamza
2018 年 5 月 16 日
編集済み: Ameer Hamza
2018 年 5 月 16 日
Yes, you can do that following the pattern of the linear regression fitting with little modification. In linear regression, we fit the equation
a*x+b = y
and in MATLAB we write it as
[x_vector ones(size(x_vector))]\y_vector
to get a and b. But since you already know slope a, your equation become
b = y-a*x
so in MATLAB use
ones(size(x_vector))\[y_vector-a*x_vector]
it will give you the value of b which minimize the least square error.
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Curtis Baden
2018 年 8 月 12 日
Thank you for the thorough, concise explanation! I'm hoping to perform a similar calculation, except I'd like to employ a weighted least squares regression (using inverse variances associated with my dependent variable as weights). How will this calculation change in this case?
Ameer Hamza
2018 年 8 月 13 日
For weighted least square you can use lscov(). In term of the backslash operator, you can use the following
(X.'*W*X)\(X.'*W*y)
where W is a diagonal matrix containing the weights.
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