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Selwyn Simsek
gsll
Commits
b75416da
Commit
b75416da
authored
Jan 03, 2013
by
Liam M. Healy
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Improved docstring for #'linear-fit
parent
0e427340
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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
;; Linear least squares, or linear regression
;; Liam Healy <2008-01-21 12:41:46EST linear-least-squares.lisp>
;; 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
;; Copyright 2008, 2009, 2010, 2011, 2012, 2013 Liam M. Healy
;; Distributed under the terms of the GNU General Public License
;; Distributed under the terms of the GNU General Public License
...
@@ -51,7 +51,7 @@
...
@@ -51,7 +51,7 @@
(
cov11
(
:pointer
:double
))
(
sumsq
(
:pointer
:double
))))
(
cov11
(
:pointer
:double
))
(
sumsq
(
:pointer
:double
))))
:inputs
(
x
weight
y
)
:inputs
(
x
weight
y
)
:switch
(
weight
weight-stride
)
:switch
(
weight
weight-stride
)
:documentation
;
FDL
:documentation
;
GSL texi partial
"Compute the best-fit linear regression coefficients
"Compute the best-fit linear regression coefficients
c0, c1 of the model Y = c_0 + c_1 X for the weighted or unweighted
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
dataset (x, y), two vectors of equal length with strides
...
@@ -66,14 +66,15 @@
...
@@ -66,14 +66,15 @@
unweighted sum of squares of the residuals from the best-fit line,
unweighted sum of squares of the residuals from the best-fit line,
\chi^2, is returned as the last value.
\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
)
(
defmfun
linear-estimate
(
x
c0
c1
cov00
cov01
cov11
)
"gsl_fit_linear_est"
"gsl_fit_linear_est"
((
x
:double
)
(
c0
:double
)
(
c1
:double
)
((
x
:double
)
(
c0
:double
)
(
c1
:double
)
(
cov00
:double
)
(
cov01
:double
)
(
cov11
:double
)
(
cov00
:double
)
(
cov01
:double
)
(
cov11
:double
)
(
y
(
:pointer
:double
))
(
y-error
(
:pointer
:double
)))
(
y
(
:pointer
:double
))
(
y-error
(
:pointer
:double
)))
:documentation
;
FDL
:documentation
;
GSL texi partial
"Use the best-fit linear regression coefficients
"Use the best-fit linear regression coefficients
c0, c1 and their covariance
c0, c1 and their covariance
cov00, cov01, cov11 to compute the fitted function
cov00, cov01, cov11 to compute the fitted function
...
@@ -100,7 +101,7 @@
...
@@ -100,7 +101,7 @@
(
sumsq
(
:pointer
:double
))))
(
sumsq
(
:pointer
:double
))))
:inputs
(
x
y
weight
)
:inputs
(
x
y
weight
)
:switch
(
weight
weight-stride
)
:switch
(
weight
weight-stride
)
:documentation
;
FDL
:documentation
;
GSL texi
"Compute the best-fit linear regression coefficient
"Compute the best-fit linear regression coefficient
c1 of the model Y = c_1 X for the weighted or unweighted datasets
c1 of the model Y = c_1 X for the weighted or unweighted datasets
(x, y), two vectors of equal length with strides
(x, y), two vectors of equal length with strides
...
@@ -117,7 +118,7 @@
...
@@ -117,7 +118,7 @@
"gsl_fit_mul_est"
"gsl_fit_mul_est"
((
x
:double
)
(
c1
:double
)
(
cov11
:double
)
((
x
:double
)
(
c1
:double
)
(
cov11
:double
)
(
y
(
:pointer
:double
))
(
y-error
(
:pointer
:double
)))
(
y
(
:pointer
:double
))
(
y-error
(
:pointer
:double
)))
:documentation
;
FDL
:documentation
;
GSL texi
"Use the best-fit linear regression coefficient
"Use the best-fit linear regression coefficient
c1 and its covariance cov11 to compute the fitted function
c1 and its covariance cov11 to compute the fitted function
y and its standard deviation y-error for the model
y and its standard deviation y-error for the model
...
@@ -207,7 +208,7 @@
...
@@ -207,7 +208,7 @@
:return
(
parameters
covariance
(
cffi:mem-ref
chisq
:double
))
:return
(
parameters
covariance
(
cffi:mem-ref
chisq
:double
))
:export
nil
:export
nil
:index
linear-mfit
:index
linear-mfit
:documentation
;
FDL
:documentation
;
GSL texi
"Compute the best-fit parameters c of the weighted or unweighted
"Compute the best-fit parameters c of the weighted or unweighted
model y = X c for the observations y and optional weights
model y = X c for the observations y and optional weights
and the model matrix X. The covariance matrix of
and the model matrix X. The covariance matrix of
...
@@ -252,7 +253,7 @@
...
@@ -252,7 +253,7 @@
:return
((
cffi:mem-ref
chisq
:double
)
(
cffi:mem-ref
rank
'
:sizet
))
:return
((
cffi:mem-ref
chisq
:double
)
(
cffi:mem-ref
rank
'
:sizet
))
:export
nil
:export
nil
:index
linear-mfit
:index
linear-mfit
:documentation
;
FDL
:documentation
;
GSL texi
"Compute the best-fit parameters c of the weighted or unweighted
"Compute the best-fit parameters c of the weighted or unweighted
model y = X c for the observations y and weights and the model
model y = X c for the observations y and weights and the model
matrix X. The covariance matrix of the model parameters is
matrix X. The covariance matrix of the model parameters is
...
@@ -276,7 +277,7 @@
...
@@ -276,7 +277,7 @@
((
mpointer
covariance
)
:pointer
)
((
mpointer
covariance
)
:pointer
)
(
y
(
:pointer
:double
))
(
y-error
(
:pointer
:double
)))
(
y
(
:pointer
:double
))
(
y-error
(
:pointer
:double
)))
:inputs
(
x
coefficients
covariance
)
:inputs
(
x
coefficients
covariance
)
:documentation
;
FDL
:documentation
;
GSL texi
"Use the best-fit multilinear regression coefficients
"Use the best-fit multilinear regression coefficients
and their covariance matrix to compute the fitted function value
and their covariance matrix to compute the fitted function value
y and its standard deviation for the model y = x.c
y and its standard deviation for the model y = x.c
...
@@ -293,7 +294,7 @@
...
@@ -293,7 +294,7 @@
:inputs
(
x
observations
coefficients
)
:inputs
(
x
observations
coefficients
)
:outputs
(
residuals
)
:outputs
(
residuals
)
:gsl-version
(
1
11
)
:gsl-version
(
1
11
)
:documentation
;
FDL
:documentation
;
GSL texi
"Compute the vector of residuals r = y - X c for the observations y,
"Compute the vector of residuals r = y - X c for the observations y,
coefficients c and matrix of predictor variables X."
)
coefficients c and matrix of predictor variables X."
)
...
...
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