Module rgsl::fit

Linear Regression

The functions described in this section can be used to perform least-squares fits to a straight line model, Y(c,x) = c_0 + c_1 x.

Functions

linear

This function computes the best-fit linear regression coefficients (c0,c1) of the model Y = c_0 + c_1 X for the dataset (x, y), two vectors of length n with strides xstride and ystride.

linear_est

This function uses the best-fit linear regression coefficients c0, c1 and their covariance cov00, cov01, cov11 to compute the fitted function y and its standard deviation y_err for the model Y = c_0 + c_1 X at the point x.

mul

This function computes the best-fit linear regression coefficient c1 of the model Y = c_1 X for the datasets (x, y), two vectors of length n with strides xstride and ystride. The errors on y are assumed unknown so the variance of the parameter c1 is estimated from the scatter of the points around the best-fit line and returned via the parameter cov11. The sum of squares of the residuals from the best-fit line is returned in sumsq.

mul_est

This function uses the best-fit linear regression coefficient c1 and its covariance cov11 to compute the fitted function y and its standard deviation y_err for the model Y = c_1 X at the point x.

wlinear

This function computes the best-fit linear regression coefficients (c0,c1) of the model Y = c_0 + c_1 X for the weighted dataset (x, y), two vectors of length n with strides xstride and ystride.

wmul

Returns (Value, c1, cov11, sumsq).