Commit b75416da by Liam M. Healy

Improved docstring for #'linear-fit

parent 0e427340
;; Linear least squares, or linear regression
;; Liam Healy <2008-01-21 12:41:46EST linear-least-squares.lisp>
;; Time-stamp: <2013-01-01 21:29:30EST linear-least-squares.lisp>
;; Time-stamp: <2013-01-03 10:14:04EST linear-least-squares.lisp>
;;
;; Copyright 2008, 2009, 2010, 2011, 2012, 2013 Liam M. Healy
;; Distributed under the terms of the GNU General Public License
......@@ -51,7 +51,7 @@
(cov11 (:pointer :double)) (sumsq (:pointer :double))))
:inputs (x weight y)
:switch (weight weight-stride)
:documentation ; FDL
:documentation ; GSL texi partial
"Compute the best-fit linear regression coefficients
c0, c1 of the model Y = c_0 + c_1 X for the weighted or unweighted
dataset (x, y), two vectors of equal length with strides
......@@ -66,14 +66,15 @@
unweighted sum of squares of the residuals from the best-fit line,
\chi^2, is returned as the last value.
Returns: c0, c1, cov00, cov01, cov11, sumsq.")
Returns: c0, c1, cov00, cov01, cov11, sumsq.
Returns: intercept, slope, intercept variance, covariance, slope variance, sum square of residuals.")
(defmfun linear-estimate (x c0 c1 cov00 cov01 cov11)
"gsl_fit_linear_est"
((x :double) (c0 :double) (c1 :double)
(cov00 :double) (cov01 :double) (cov11 :double)
(y (:pointer :double)) (y-error (:pointer :double)))
:documentation ; FDL
:documentation ; GSL texi partial
"Use the best-fit linear regression coefficients
c0, c1 and their covariance
cov00, cov01, cov11 to compute the fitted function
......@@ -100,7 +101,7 @@
(sumsq (:pointer :double))))
:inputs (x y weight)
:switch (weight weight-stride)
:documentation ; FDL
:documentation ; GSL texi
"Compute the best-fit linear regression coefficient
c1 of the model Y = c_1 X for the weighted or unweighted datasets
(x, y), two vectors of equal length with strides
......@@ -117,7 +118,7 @@
"gsl_fit_mul_est"
((x :double) (c1 :double) (cov11 :double)
(y (:pointer :double)) (y-error (:pointer :double)))
:documentation ; FDL
:documentation ; GSL texi
"Use the best-fit linear regression coefficient
c1 and its covariance cov11 to compute the fitted function
y and its standard deviation y-error for the model
......@@ -207,7 +208,7 @@
:return (parameters covariance (cffi:mem-ref chisq :double))
:export nil
:index linear-mfit
:documentation ; FDL
:documentation ; GSL texi
"Compute the best-fit parameters c of the weighted or unweighted
model y = X c for the observations y and optional weights
and the model matrix X. The covariance matrix of
......@@ -252,7 +253,7 @@
:return ((cffi:mem-ref chisq :double) (cffi:mem-ref rank ':sizet))
:export nil
:index linear-mfit
:documentation ; FDL
:documentation ; GSL texi
"Compute the best-fit parameters c of the weighted or unweighted
model y = X c for the observations y and weights and the model
matrix X. The covariance matrix of the model parameters is
......@@ -276,7 +277,7 @@
((mpointer covariance) :pointer)
(y (:pointer :double)) (y-error (:pointer :double)))
:inputs (x coefficients covariance)
:documentation ; FDL
:documentation ; GSL texi
"Use the best-fit multilinear regression coefficients
and their covariance matrix to compute the fitted function value
y and its standard deviation for the model y = x.c
......@@ -293,7 +294,7 @@
:inputs (x observations coefficients)
:outputs (residuals)
:gsl-version (1 11)
:documentation ; FDL
:documentation ; GSL texi
"Compute the vector of residuals r = y - X c for the observations y,
coefficients c and matrix of predictor variables X.")
......
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