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antik
gsll
Commits
b75416da
Commit
b75416da
authored
Jan 03, 2013
by
Liam M. Healy
Browse files
Improved docstring for #'linear-fit
parent
0e427340
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solve-minimize-fit/linear-least-squares.lisp
solve-minimize-fit/linear-least-squares.lisp
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solve-minimize-fit/linear-least-squares.lisp
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b75416da
;; Linear least squares, or linear regression
;; Liam Healy <2008-01-21 12:41:46EST linear-least-squares.lisp>
;; Time-stamp: <2013-01-0
1 21:29:30
EST linear-least-squares.lisp>
;; Time-stamp: <2013-01-0
3 10:14:04
EST 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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