1 ;;; calc-nlfit.el --- nonlinear curve fitting for Calc
3 ;; Copyright (C) 2007-2011 Free Software Foundation, Inc.
5 ;; Maintainer: Jay Belanger <jay.p.belanger@gmail.com>
7 ;; This file is part of GNU Emacs.
9 ;; GNU Emacs is free software: you can redistribute it and/or modify
10 ;; it under the terms of the GNU General Public License as published by
11 ;; the Free Software Foundation, either version 3 of the License, or
12 ;; (at your option) any later version.
14 ;; GNU Emacs is distributed in the hope that it will be useful,
15 ;; but WITHOUT ANY WARRANTY; without even the implied warranty of
16 ;; MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17 ;; GNU General Public License for more details.
19 ;; You should have received a copy of the GNU General Public License
20 ;; along with GNU Emacs. If not, see <http://www.gnu.org/licenses/>.
24 ;; This code uses the Levenberg-Marquardt method, as described in
25 ;; _Numerical Analysis_ by H. R. Schwarz, to fit data to
26 ;; nonlinear curves. Currently, the only the following curves are
28 ;; The logistic S curve, y=a/(1+exp(b*(t-c)))
29 ;; Here, y is usually interpreted as the population of some
30 ;; quantity at time t. So we will think of the data as consisting
31 ;; of quantities q0, q1, ..., qn and their respective times
34 ;; The logistic bell curve, y=A*exp(B*(t-C))/(1+exp(B*(t-C)))^2
35 ;; Note that this is the derivative of the formula for the S curve.
36 ;; We get A=-a*b, B=b and C=c. Here, y is interpreted as the rate
37 ;; of growth of a population at time t. So we will think of the
38 ;; data as consisting of rates p0, p1, ..., pn and their
39 ;; respective times t0, t1, ..., tn.
41 ;; The Hubbert Linearization, y/x=A*(1-x/B)
42 ;; Here, y is thought of as the rate of growth of a population
43 ;; and x represents the actual population. This is essentially
44 ;; the differential equation describing the actual population.
46 ;; The Levenberg-Marquardt method is an iterative process: it takes
47 ;; an initial guess for the parameters and refines them. To get an
48 ;; initial guess for the parameters, we'll use a method described by
49 ;; Luis de Sousa in "Hubbert's Peak Mathematics". The idea is that
50 ;; given quantities Q and the corresponding rates P, they should
51 ;; satisfy P/Q= mQ+a. We can use the parameter a for an
52 ;; approximation for the parameter a in the S curve, and
53 ;; approximations for b and c are found using least squares on the
54 ;; linearization log((a/y)-1) = log(bb) + cc*t of
55 ;; y=a/(1+bb*exp(cc*t)), which is equivalent to the above s curve
56 ;; formula, and then tranlating it to b and c. From this, we can
57 ;; also get approximations for the bell curve parameters.
64 ;; Declare functions which are defined elsewhere.
65 (declare-function calc-get-fit-variables
"calcalg3" (nv nc
&optional defv defc with-y homog
))
66 (declare-function math-map-binop
"calcalg3" (binop args1 args2
))
68 (defun math-nlfit-least-squares (xdata ydata
&optional sdata sigmas
)
69 "Return the parameters A and B for the best least squares fit y=a+bx."
70 (let* ((n (length xdata
))
72 (mapcar 'calcFunc-sqr sdata
)
84 (setq Sx
(math-add Sx
(if s
(math-div x s
) x
)))
85 (setq Sy
(math-add Sy
(if s
(math-div y s
) y
)))
86 (setq Sxx
(math-add Sxx
(if s
(math-div (math-mul x x
) s
)
88 (setq Sxy
(math-add Sxy
(if s
(math-div (math-mul x y
) s
)
91 (setq S
(math-add S
(math-div 1 s
)))))
92 (setq xdata
(cdr xdata
))
93 (setq ydata
(cdr ydata
))
94 (setq s2data
(cdr s2data
)))
95 (setq D
(math-sub (math-mul S Sxx
) (math-mul Sx Sx
)))
96 (let ((A (math-div (math-sub (math-mul Sxx Sy
) (math-mul Sx Sxy
)) D
))
97 (B (math-div (math-sub (math-mul S Sxy
) (math-mul Sx Sy
)) D
)))
99 (let ((C11 (math-div Sxx D
))
100 (C12 (math-neg (math-div Sx D
)))
101 (C22 (math-div S D
)))
102 (list (list 'sdev A
(calcFunc-sqrt C11
))
103 (list 'sdev B
(calcFunc-sqrt C22
))
106 (list 'vec C12 C22
))))
109 ;;; The methods described by de Sousa require the cumulative data qdata
110 ;;; and the rates pdata. We will assume that we are given either
111 ;;; qdata and the corresponding times tdata, or pdata and the corresponding
112 ;;; tdata. The following two functions will find pdata or qdata,
113 ;;; given the other..
115 ;;; First, given two lists; one of values q0, q1, ..., qn and one of
116 ;;; corresponding times t0, t1, ..., tn; return a list
117 ;;; p0, p1, ..., pn of the rates of change of the qi with respect to t.
118 ;;; p0 is the right hand derivative (q1 - q0)/(t1 - t0).
119 ;;; pn is the left hand derivative (qn - q(n-1))/(tn - t(n-1)).
120 ;;; The other pis are the averages of the two:
121 ;;; (1/2)((qi - q(i-1))/(ti - t(i-1)) + (q(i+1) - qi)/(t(i+1) - ti)).
123 (defun math-nlfit-get-rates-from-cumul (tdata qdata
)
126 (math-sub (nth 1 qdata
)
128 (math-sub (nth 1 tdata
)
130 (while (> (length qdata
) 2)
137 (math-sub (nth 2 qdata
)
139 (math-sub (nth 2 tdata
)
142 (math-sub (nth 1 qdata
)
144 (math-sub (nth 1 tdata
)
147 (setq qdata
(cdr qdata
)))
151 (math-sub (nth 1 qdata
)
153 (math-sub (nth 1 tdata
)
158 ;;; Next, given two lists -- one of rates p0, p1, ..., pn and one of
159 ;;; corresponding times t0, t1, ..., tn -- and an initial values q0,
160 ;;; return a list q0, q1, ..., qn of the cumulative values.
161 ;;; q0 is the initial value given.
162 ;;; For i>0, qi is computed using the trapezoid rule:
163 ;;; qi = q(i-1) + (1/2)(pi + p(i-1))(ti - t(i-1))
165 (defun math-nlfit-get-cumul-from-rates (tdata pdata q0
)
166 (let* ((qdata (list q0
)))
170 (math-add (car qdata
)
174 (math-add (nth 1 pdata
) (nth 0 pdata
)))
175 (math-sub (nth 1 tdata
)
178 (setq pdata
(cdr pdata
))
179 (setq tdata
(cdr tdata
)))
182 ;;; Given the qdata, pdata and tdata, find the parameters
183 ;;; a, b and c that fit q = a/(1+b*exp(c*t)).
184 ;;; a is found using the method described by de Sousa.
185 ;;; b and c are found using least squares on the linearization
186 ;;; log((a/q)-1) = log(b) + c*t
187 ;;; In some cases (where the logistic curve may well be the wrong
188 ;;; model), the computed a will be less than or equal to the maximum
189 ;;; value of q in qdata; in which case the above linearization won't work.
190 ;;; In this case, a will be replaced by a number slightly above
191 ;;; the maximum value of q.
193 (defun math-nlfit-find-qmax (qdata pdata tdata
)
194 (let* ((ratios (math-map-binop 'math-div pdata qdata
))
195 (lsdata (math-nlfit-least-squares ratios tdata
))
196 (qmax (math-max-list (car qdata
) (cdr qdata
)))
197 (a (math-neg (math-div (nth 1 lsdata
) (nth 0 lsdata
)))))
198 (if (math-lessp a qmax
)
199 (math-add '(float 5 -
1) qmax
)
202 (defun math-nlfit-find-logistic-parameters (qdata pdata tdata
)
203 (let* ((a (math-nlfit-find-qmax qdata pdata tdata
))
205 (mapcar (lambda (q) (calcFunc-ln (math-sub (math-div a q
) 1)))
207 (bandc (math-nlfit-least-squares tdata newqdata
)))
210 (calcFunc-exp (nth 0 bandc
))
213 ;;; Next, given the pdata and tdata, we can find the qdata if we know q0.
214 ;;; We first try to find q0, using the fact that when p takes on its largest
215 ;;; value, q is half of its maximum value. So we'll find the maximum value
216 ;;; of q given various q0, and use bisection to approximate the correct q0.
218 ;;; First, given pdata and tdata, find what half of qmax would be if q0=0.
220 (defun math-nlfit-find-qmaxhalf (pdata tdata
)
221 (let ((pmax (math-max-list (car pdata
) (cdr pdata
)))
223 (while (math-lessp (car pdata
) pmax
)
229 (math-add (nth 1 pdata
) (nth 0 pdata
)))
230 (math-sub (nth 1 tdata
)
232 (setq pdata
(cdr pdata
))
233 (setq tdata
(cdr tdata
)))
236 ;;; Next, given pdata and tdata, approximate q0.
238 (defun math-nlfit-find-q0 (pdata tdata
)
239 (let* ((qhalf (math-nlfit-find-qmaxhalf pdata tdata
))
240 (q0 (math-mul 2 qhalf
))
241 (qdata (math-nlfit-get-cumul-from-rates tdata pdata q0
)))
242 (while (math-lessp (math-nlfit-find-qmax
244 (lambda (q) (math-add q0 q
))
252 (setq q0
(math-add q0 qhalf
)))
253 (let* ((qmin (math-sub q0 qhalf
))
255 (qt (math-nlfit-find-qmax
257 (lambda (q) (math-add q0 q
))
262 (setq q0
(math-mul '(float 5 -
1) (math-add qmin qmax
)))
264 (math-nlfit-find-qmax
266 (lambda (q) (math-add q0 q
))
269 (math-mul '(float 5 -
1) (math-add qhalf q0
)))
273 (math-mul '(float 5 -
1) (math-add qmin qmax
)))))
275 ;;; To improve the approximations to the parameters, we can use
276 ;;; Marquardt method as described in Schwarz's book.
278 ;;; Small numbers used in the Givens algorithm
279 (defvar math-nlfit-delta
'(float 1 -
8))
281 (defvar math-nlfit-epsilon
'(float 1 -
5))
283 ;;; Maximum number of iterations
284 (defvar math-nlfit-max-its
100)
286 ;;; Next, we need some functions for dealing with vectors and
287 ;;; matrices. For convenience, we'll work with Emacs lists
288 ;;; as vectors, rather than Calc's vectors.
290 (defun math-nlfit-set-elt (vec i x
)
291 (setcar (nthcdr (1- i
) vec
) x
))
293 (defun math-nlfit-get-elt (vec i
)
296 (defun math-nlfit-make-matrix (i j
)
297 (let ((row (make-list j
0))
301 (setq mat
(cons (copy-sequence row
) mat
))
305 (defun math-nlfit-set-matx-elt (mat i j x
)
306 (setcar (nthcdr (1- j
) (nth (1- i
) mat
)) x
))
308 (defun math-nlfit-get-matx-elt (mat i j
)
309 (nth (1- j
) (nth (1- i
) mat
)))
311 ;;; For solving the linearized system.
312 ;;; (The Givens method, from Schwarz.)
314 (defun math-nlfit-givens (C d
)
315 (let* ((C (copy-tree C
))
329 (let ((cij (math-nlfit-get-matx-elt C i j
))
330 (cjj (math-nlfit-get-matx-elt C j j
)))
331 (when (not (math-equal 0 cij
))
332 (if (math-lessp (calcFunc-abs cjj
)
333 (math-mul math-nlfit-delta
(calcFunc-abs cij
)))
334 (setq w
(math-neg cij
)
343 (math-mul cij cij
))))
344 gamma
(math-div cjj w
)
345 sigma
(math-neg (math-div cij w
)))
346 (if (math-lessp (calcFunc-abs sigma
) gamma
)
348 (setq rho
(math-div (calcFunc-sign sigma
) gamma
))))
351 (math-nlfit-set-matx-elt C j j w
)
352 (math-nlfit-set-matx-elt C i j rho
)
355 (let* ((cjk (math-nlfit-get-matx-elt C j k
))
356 (cik (math-nlfit-get-matx-elt C i k
))
358 (math-mul gamma cjk
) (math-mul sigma cik
))))
361 (math-mul gamma cik
)))
363 (math-nlfit-set-matx-elt C i k cik
)
364 (math-nlfit-set-matx-elt C j k cjk
)
366 (let* ((di (math-nlfit-get-elt d i
))
367 (dj (math-nlfit-get-elt d j
))
370 (math-mul sigma di
))))
373 (math-mul gamma di
)))
375 (math-nlfit-set-elt d i di
)
376 (math-nlfit-set-elt d j dj
))))
382 (math-nlfit-set-elt r i
0)
383 (setq s
(math-nlfit-get-elt d i
))
386 (setq s
(math-add s
(math-mul (math-nlfit-get-matx-elt C i k
)
387 (math-nlfit-get-elt x k
))))
389 (math-nlfit-set-elt x i
392 (math-nlfit-get-matx-elt C i i
))))
396 (math-nlfit-set-elt r i
(math-nlfit-get-elt d i
))
402 (setq rho
(math-nlfit-get-matx-elt C i j
))
403 (if (math-equal rho
1)
406 (if (math-lessp (calcFunc-abs rho
) 1)
409 (math-sub 1 (math-mul sigma sigma
))))
410 (setq gamma
(math-div 1 (calcFunc-abs rho
))
411 sigma
(math-mul (calcFunc-sign rho
)
413 (math-sub 1 (math-mul gamma gamma
)))))))
414 (let ((ri (math-nlfit-get-elt r i
))
415 (rj (math-nlfit-get-elt r j
))
417 (setq h
(math-add (math-mul gamma rj
)
418 (math-mul sigma ri
)))
421 (math-mul sigma rj
)))
423 (math-nlfit-set-elt r i ri
)
424 (math-nlfit-set-elt r j rj
))
430 (defun math-nlfit-jacobian (grad xlist parms
&optional slist
)
433 (let ((row (apply grad
(car xlist
) parms
)))
437 (mapcar (lambda (x) (math-div x
(car slist
))) row
)
440 (setq slist
(cdr slist
))
441 (setq xlist
(cdr xlist
)))
444 (defun math-nlfit-make-ident (l n
)
445 (let ((m (math-nlfit-make-matrix n n
))
448 (math-nlfit-set-matx-elt m i i l
)
452 (defun math-nlfit-chi-sq (xlist ylist parms fn
&optional slist
)
457 (apply fn
(car xlist
) parms
)
460 (setq c
(math-div c
(car slist
))))
464 (setq xlist
(cdr xlist
))
465 (setq ylist
(cdr ylist
))
466 (setq slist
(cdr slist
)))
469 (defun math-nlfit-init-lambda (C)
476 (setq l
(math-add l
(math-mul (car row
) (car row
))))
477 (setq row
(cdr row
))))
479 (calcFunc-sqrt (math-div l
(math-mul n N
)))))
481 (defun math-nlfit-make-Ctilda (C l
)
482 (let* ((n (length (car C
)))
483 (bot (math-nlfit-make-ident l n
)))
486 (defun math-nlfit-make-d (fn xdata ydata parms
&optional sdata
)
490 (let ((dd (math-sub (apply fn
(car xdata
) parms
)
492 (if sdata
(math-div dd
(car sdata
)) dd
))
494 (setq xdata
(cdr xdata
))
495 (setq ydata
(cdr ydata
))
496 (setq sdata
(cdr sdata
)))
499 (defun math-nlfit-make-dtilda (d n
)
500 (append d
(make-list n
0)))
502 (defun math-nlfit-fit (xlist ylist parms fn grad
&optional slist
)
504 ((C (math-nlfit-jacobian grad xlist parms slist
))
505 (d (math-nlfit-make-d fn xlist ylist parms slist
))
506 (chisq (math-nlfit-chi-sq xlist ylist parms fn slist
))
507 (lambda (math-nlfit-init-lambda C
))
512 (< iters math-nlfit-max-its
))
513 (setq iters
(1+ iters
))
516 (let* ((Ctilda (math-nlfit-make-Ctilda C lambda
))
517 (dtilda (math-nlfit-make-dtilda d
(length (car C
))))
518 (zeta (math-nlfit-givens Ctilda dtilda
))
519 (newparms (math-map-binop 'math-add
(copy-tree parms
) zeta
))
520 (newchisq (math-nlfit-chi-sq xlist ylist newparms fn slist
)))
521 (if (math-lessp newchisq chisq
)
525 (math-sub chisq newchisq
) newchisq
) math-nlfit-epsilon
)
526 (setq really-done t
))
527 (setq lambda
(math-div lambda
10))
528 (setq chisq newchisq
)
529 (setq parms newparms
)
531 (setq lambda
(math-mul lambda
10)))))
532 (setq C
(math-nlfit-jacobian grad xlist parms slist
))
533 (setq d
(math-nlfit-make-d fn xlist ylist parms slist
))))
536 ;;; The functions that describe our models, and their gradients.
538 (defun math-nlfit-s-logistic-fn (x a b c
)
539 (math-div a
(math-add 1 (math-mul b
(calcFunc-exp (math-mul c x
))))))
541 (defun math-nlfit-s-logistic-grad (x a b c
)
542 (let* ((ep (calcFunc-exp (math-mul c x
)))
543 (d (math-add 1 (math-mul b ep
)))
547 (math-neg (math-div (math-mul a ep
) d2
))
548 (math-neg (math-div (math-mul a
(math-mul b
(math-mul x ep
))) d2
)))))
550 (defun math-nlfit-b-logistic-fn (x a c d
)
551 (let ((ex (calcFunc-exp (math-mul c
(math-sub x d
)))))
558 (defun math-nlfit-b-logistic-grad (x a c d
)
559 (let* ((ex (calcFunc-exp (math-mul c
(math-sub x d
))))
560 (ex1 (math-add 1 ex
))
568 (math-mul a
(math-mul xd ex
))
571 (math-mul 2 (math-mul a
(math-mul xd
(math-sqr ex
))))
575 (math-mul 2 (math-mul a
(math-mul c
(math-sqr ex
))))
578 (math-mul a
(math-mul c ex
))
581 ;;; Functions to get the final covariance matrix and the sdevs
583 (defun math-nlfit-find-covar (grad xlist pparms
)
586 (setq j
(cons (cons 'vec
(apply grad
(car xlist
) pparms
)) j
))
587 (setq xlist
(cdr xlist
)))
588 (setq j
(cons 'vec
(reverse j
)))
594 (defun math-nlfit-get-sigmas (grad xlist pparms chisq
)
596 (covar (math-nlfit-find-covar grad xlist pparms
))
597 (n (1- (length covar
)))
602 (setq sgs
(cons (calcFunc-sqrt (nth i
(nth i covar
))) sgs
))
604 (setq sgs
(reverse sgs
)))
607 ;;; Now the Calc functions
609 (defun math-nlfit-s-logistic-params (xdata ydata
)
610 (let ((pdata (math-nlfit-get-rates-from-cumul xdata ydata
)))
611 (math-nlfit-find-logistic-parameters ydata pdata xdata
)))
613 (defun math-nlfit-b-logistic-params (xdata ydata
)
614 (let* ((q0 (math-nlfit-find-q0 ydata xdata
))
615 (qdata (math-nlfit-get-cumul-from-rates xdata ydata q0
))
616 (abc (math-nlfit-find-logistic-parameters qdata ydata xdata
))
623 (D (math-neg (math-div (calcFunc-ln B
) C
)))
627 ;;; Some functions to turn the parameter lists and variables
628 ;;; into the appropriate functions.
630 (defun math-nlfit-s-logistic-solnexpr (pms var
)
631 (let ((a (nth 0 pms
))
644 (defun math-nlfit-b-logistic-solnexpr (pms var
)
645 (let ((a (nth 0 pms
))
664 (defun math-nlfit-enter-result (n prefix vals
)
665 (setq calc-aborted-prefix prefix
)
666 (calc-pop-push-record-list n prefix vals
)
669 (defun math-nlfit-fit-curve (fn grad solnexpr initparms
&optional sdv
)
671 (let* ((sdevv (or (eq sdv
'calcFunc-efit
) (eq sdv
'calcFunc-xfit
)))
672 (calc-display-working-message nil
)
674 (xdata (cdr (car (cdr data
))))
675 (ydata (cdr (car (cdr (cdr data
)))))
676 (sdata (if (math-contains-sdev-p ydata
)
677 (mapcar (lambda (x) (math-get-sdev x t
)) ydata
)
679 (ydata (mapcar (lambda (x) (math-get-value x
)) ydata
))
680 (calc-curve-varnames nil
)
681 (calc-curve-coefnames nil
)
683 (fitvars (calc-get-fit-variables 1 3))
684 (var (nth 1 calc-curve-varnames
))
685 (parms (cdr calc-curve-coefnames
))
687 (funcall initparms xdata ydata
))
688 (fit (math-nlfit-fit xdata ydata parmguess fn grad sdata
))
689 (finalparms (nth 1 fit
))
692 (math-nlfit-get-sigmas grad xdata finalparms
(nth 0 fit
))))
699 (lambda (x y
) (list 'sdev x y
)) finalparms sigmas
)
701 (soln (funcall solnexpr finalparms var
)))
702 (let ((calc-fit-to-trail t
)
705 (setq traillist
(cons (list 'calcFunc-eq
(car parms
) (car finalparms
))
707 (setq finalparms
(cdr finalparms
))
708 (setq parms
(cdr parms
)))
709 (setq traillist
(calc-normalize (cons 'vec
(nreverse traillist
))))
710 (cond ((eq sdv
'calcFunc-efit
)
711 (math-nlfit-enter-result 1 "efit" soln
))
712 ((eq sdv
'calcFunc-xfit
)
721 (let ((n (length xdata
))
722 (m (length finalparms
)))
723 (if (and sdata
(> n m
))
724 (calcFunc-utpc (nth 0 fit
)
726 '(var nan var-nan
)))))
727 (math-nlfit-enter-result 1 "xfit" sln
)))
729 (math-nlfit-enter-result 1 "fit" soln
)))
730 (calc-record traillist
"parm")))))
732 (defun calc-fit-s-shaped-logistic-curve (arg)
734 (math-nlfit-fit-curve 'math-nlfit-s-logistic-fn
735 'math-nlfit-s-logistic-grad
736 'math-nlfit-s-logistic-solnexpr
737 'math-nlfit-s-logistic-params
740 (defun calc-fit-bell-shaped-logistic-curve (arg)
742 (math-nlfit-fit-curve 'math-nlfit-b-logistic-fn
743 'math-nlfit-b-logistic-grad
744 'math-nlfit-b-logistic-solnexpr
745 'math-nlfit-b-logistic-params
748 (defun calc-fit-hubbert-linear-curve (&optional sdv
)
750 (let* ((sdevv (or (eq sdv
'calcFunc-efit
) (eq sdv
'calcFunc-xfit
)))
751 (calc-display-working-message nil
)
753 (qdata (cdr (car (cdr data
))))
754 (pdata (cdr (car (cdr (cdr data
)))))
755 (sdata (if (math-contains-sdev-p pdata
)
756 (mapcar (lambda (x) (math-get-sdev x t
)) pdata
)
758 (pdata (mapcar (lambda (x) (math-get-value x
)) pdata
))
759 (poverqdata (math-map-binop 'math-div pdata qdata
))
760 (parmvals (math-nlfit-least-squares qdata poverqdata sdata sdevv
))
761 (finalparms (list (nth 0 parmvals
)
763 (math-div (nth 0 parmvals
)
765 (calc-curve-varnames nil
)
766 (calc-curve-coefnames nil
)
768 (fitvars (calc-get-fit-variables 1 2))
769 (var (nth 1 calc-curve-varnames
))
770 (parms (cdr calc-curve-coefnames
))
771 (soln (list '* (nth 0 finalparms
)
773 (list '/ var
(nth 1 finalparms
))))))
774 (let ((calc-fit-to-trail t
)
778 (list 'calcFunc-eq
(nth 0 parms
) (nth 0 finalparms
))
779 (list 'calcFunc-eq
(nth 1 parms
) (nth 1 finalparms
))))
780 (cond ((eq sdv
'calcFunc-efit
)
781 (math-nlfit-enter-result 1 "efit" soln
))
782 ((eq sdv
'calcFunc-xfit
)
787 (list (nth 1 (nth 0 finalparms
))
788 (nth 1 (nth 1 finalparms
)))
802 '(calcFunc-fitdummy 1)
805 '(calcFunc-fitdummy 1)
806 '(calcFunc-fitdummy 2))))
808 (let ((n (length qdata
)))
809 (if (and sdata
(> n
2))
813 '(var nan var-nan
)))))
814 (math-nlfit-enter-result 1 "xfit" sln
)))
816 (math-nlfit-enter-result 1 "fit" soln
)))
817 (calc-record traillist
"parm")))))
819 (provide 'calc-nlfit
)