Hello, I need an efficient way to performs linear regression because I have to fit several segments in a big for loops which takes time to achieve. In the past, their was a linreg function which has been depreciated. Currently I am using polyfit to do a polynomial fit of order 1, for each segment. …

[image] Paul_Soderlind: I have recently noticed that b=(x'x)\(x'y) is often twice as fast as b = x\y , for instance, when x is 1000x10 and y is 1000x1 . So far, I have no good explanation for it. Sorry to come late to this thread, but I’m surprised that no one seems to have given the correct …

what about? using Random N = 10000 x = rand(N) X = [ones(N) x] y = 10 .+ x .* 0.3 function linreg1(y, X) β_hat = (X' * X) \ X' * y return(β_hat) end function linreg2(y, X) β_hat = X \ y return(β_hat) end using GLM GLM.fit(LinearModel, X, y, true) using DataFrames, GLM data = Da…

First you should decide what you want the function to be. Here you are assuming that the model matrix X has been externally created. I believe the original suggestion was to have two vectors, x and y, as the arguments. In that case it is best to go old school and create X'X and X'y without ever c…

Sounds like a pretty strong case for having an efficient linreg function in the Statistics stdlib.

Wouldn’t you always have to create X externally in a for loop?

I’m not sure I understand the question. If you have the vector x and you want X'X and X'y you don’t need to allocate and populate the N by 2 matrix X. That’s what the section in the code with Symmetric([T(N) sum(x); zero(T) sum(abs2, x)], :U) and [sum(y); dot(x, y)] is doing. Once upon a time…

I just learned so much from just those two lines! Thank you! I didn’t even know there was such a thing…

does x’ mean the derivative of x? and is x independent, while y is dependent?

x’ means transpose of x in this context. If we were talking about odes it’d probably mean x’(t)

Sorry to bring this discussion back. But I think that many times the user is just searching for a function which given two vectors x and y, returns the coefficients of the linear fit and the Pearson correlation coefficient, and perhaps a third vector with the fitted Y values in the same x positions,…

Efficient way of doing linear regression

General Usage Performance

Juan August 12, 2020, 11:26pm 42

Topic		Replies	Views
GLM is slow on large datasets. Using OnlineStats for regressions? MixedModels? Performance glm	25	5326	November 26, 2018
Best way for linear regression problem on product features General Usage performance , regression , qr	28	528	April 11, 2025
[ANN] LinearRegressionKit Package Announcements package , regression , linear-regression	9	2740	November 22, 2022
What is the equivalent Julia code for statsmodels.api.OLS Statistics python	15	583	March 26, 2024
The simplest linear fit with GLM Tooling glm	13	5691	November 11, 2021

Efficient way of doing linear regression

Related topics