; $Id: //depot/Release/ENVI50_IDL82/idl/idldir/lib/poly_fit.pro#1 $
;
;+
; NAME:
; POLY_FIT
;
; PURPOSE:
; Perform a least-square polynomial fit with optional error estimates.
;
; This routine uses matrix inversion. A newer version of this routine,
; SVDFIT, uses Singular Value Decomposition. The SVD technique is more
; flexible, but slower.
;
; CATEGORY:
; Curve fitting.
;
; CALLING SEQUENCE:
; Result = POLY_FIT(X, Y, Degree)
;
; INPUTS:
; X: The independent variable vector.
;
; Y: The dependent variable vector, should be same length as x.
;
; Degree: The degree of the polynomial to fit.
;
; OUTPUTS:
; POLY_FIT returns a vector of coefficients with a length of NDegree+1.
;
; KEYWORDS:
; CHISQ: Sum of squared errors divided by MEASURE_ERRORS if specified.
;
; COVAR: Covariance matrix of the coefficients.
;
; DOUBLE: if set, force computations to be in double precision.
;
; MEASURE_ERRORS: Set this keyword to a vector containing standard
; measurement errors for each point Y[i]. This vector must be the same
; length as X and Y.
;
; Note - For Gaussian errors (e.g. instrumental uncertainties),
; MEASURE_ERRORS should be set to the standard
; deviations of each point in Y. For Poisson or statistical weighting
; MEASURE_ERRORS should be set to sqrt(Y).
;
; SIGMA: The 1-sigma error estimates of the returned parameters.
;
; Note: if MEASURE_ERRORS is omitted, then you are assuming that
; your model is correct. In this case, SIGMA is multiplied
; by SQRT(CHISQ/(N-M)), where N is the number of points
; in X and M is the number of terms in the fitting function.
; See section 15.2 of Numerical Recipes in C (2nd ed) for details.
;
; STATUS = Set this keyword to a named variable to receive the status
; of the operation. Possible status values are:
; 0 for successful completion, 1 for a singular array (which
; indicates that the inversion is invalid), and 2 which is a
; warning that a small pivot element was used and that significant
; accuracy was probably lost.
;
; Note: if STATUS is not specified then any error messages will be output
; to the screen.
;
; YBAND: 1 standard deviation error estimate for each point.
;
; YERROR: The standard error between YFIT and Y.
;
; YFIT: Vector of calculated Y's. These values have an error
; of + or - YBAND.
;
; COMMON BLOCKS:
; None.
;
; SIDE EFFECTS:
; None.
;
; MODIFICATION HISTORY:
; Written by: George Lawrence, LASP, University of Colorado,
; December, 1981.
;
; Adapted to VAX IDL by: David Stern, Jan, 1982.
; Modified: GGS, RSI, March 1996
; Corrected a condition which explicitly converted all
; internal variables to single-precision float.
; Added support for double-precision inputs.
; Added a check for singular array inversion.
; SVP, RSI, June 1996
; Changed A to Corrm to match IDL5.0 docs.
; S. Lett, RSI, December 1997
; Changed inversion status check to check only for
; numerically singular matrix.
; S. Lett, RSI, March 1998
; Initialize local copy of the independent variable
; to be of type DOUBLE when working in double precision.
; CT, RSI, March 2000: Changed to call POLYFITW.
; CT, RSI, July-Aug 2000: Removed call to POLYFITW,
; added MEASURE_ERRORS keyword,
; added all other keywords (except DOUBLE),
; made output arguments obsolete.
; CT, RSI, Jan 2003: Combine some vector math expressions,
; general code cleanup. About 50% faster.
; CT, VIS, Dec 2007: If n==m then avoid divide by zero, return sigma=0.
; CT, VIS, Dec 2011: Per George Lawrence, for YBAND, if MEASURE_ERRORS is
; omitted, then multiply YBAND by the sqrt(variance). Otherwise, if
; MEASURE_ERRORS is present, do not multiply.
;-
FUNCTION POLY_FIT, x, y, ndegree, $
yfit_old, yband_old, yerror_old, corrm_old, $ ; obsolete arguments
CHISQ=chisq, $
COVAR=covar, $
DOUBLE=double, $
MEASURE_ERRORS=measure_errors, $
SIGMA=sigma, $
STATUS=status, $
YBAND=yband, $
YERROR=yerror, $
YFIT=yfit
COMPILE_OPT idl2
ON_ERROR,2 ;RETURN TO CALLER IF ERROR
n = N_ELEMENTS(x)
IF (n NE N_ELEMENTS(y)) THEN MESSAGE, $
'X and Y must have same number of elements.'
m = ndegree + 1 ; # of elements in coeff vec
double = (N_ELEMENTS(double) GT 0) ? KEYWORD_SET(double) : $
(SIZE(x,/TNAME) EQ 'DOUBLE') OR (SIZE(y,/TNAME) EQ 'DOUBLE')
haveMeasureError = (N_ELEMENTS(measure_errors) gt 0)
sdev = 1d
if (haveMeasureError) then $
sdev *= measure_errors
sdev2 = sdev^2
haveYband = ARG_PRESENT(yband) || ARG_PRESENT(yband_old)
; construct work arrays
covar = DBLARR(m,m) ; least square matrix, weighted matrix
b = DBLARR(m) ; will contain sum weights*y*x^j
z = 1d ; polynomial term (guarantees double precision calc)
wy = DOUBLE(y)
if (haveMeasureError) then $
wy /= sdev2
; Just fill in some values in case we fail.
yfit = !VALUES.D_NAN
yband = !VALUES.D_NAN
yerror = !VALUES.D_NAN
covar[0,0] = haveMeasureError ? TOTAL(1d/sdev2) : n
b[0] = TOTAL(wy)
FOR p = 1L,2*ndegree DO BEGIN ; power loop
z *= x ; z is now x^p
IF p LT m THEN b[p] = TOTAL(wy*z) ; b is sum weights*y*x^j
sum = haveMeasureError ? TOTAL(z/sdev2) : TOTAL(z)
for j = 0 > (p-ndegree), ndegree < p do $
covar[j,p-j] = sum
ENDFOR ; end of p loop, construction of covar and b
covar = INVERT(TEMPORARY(covar), status)
IF NOT ARG_PRESENT(status) THEN BEGIN
CASE status OF
1: MESSAGE, "Singular matrix detected."
2: MESSAGE,/INFO, "Warning: Invert detected a small pivot element."
ELSE:
ENDCASE
ENDIF
if (status eq 1) then begin
result = !VALUES.D_NAN
goto, done
endif
result = (TEMPORARY(b) # covar) ; construct coefficients
; compute optional output parameters.
; one-standard deviation error estimates, init
yfit = result[ndegree]
for k = ndegree-1L, 0, -1 do $
yfit = result[k] + TEMPORARY(yfit)*x ; sum basis vectors
; Vector of parameter errors.
sigma = SQRT(ABS(covar[lindgen(M)*(M+1)]))
if (haveMeasureError) then begin
; Only do this computation once.
diff = (yfit - y)^2
chisq = TOTAL(diff/sdev2)
; Experimental variance estimate, unbiased.
var = (n gt m) ? TOTAL(diff)/(n-m) : 0d
endif else begin
; No weighting, don't need to divide by sdev2.
chisq = TOTAL((yfit - y)^2)
; Experimental variance estimate, unbiased.
var = (n gt m) ? chisq/(n-m) : 0d
; If MEASURE_ERRORS is omitted, then you are assuming that your model
; is correct. In this case, SIGMA is multiplied by SQRT(chisq/(n-m)).
; See section 15.2 of Numerical Recipes in C (Second Edition) for details.
sigma *= (n gt m) ? SQRT(chisq/(n-m)) : 0
endelse
; Overall fit error.
yerror = SQRT(var)
; Only do this computation if user wants YBAND.
if (haveYband) then begin
z = REPLICATE(1d, n)
yband = REPLICATE(covar[0,0], n)
FOR p=1L,2*ndegree DO BEGIN ; compute correlated error estimates on y
z *= x ; z is now x^p
sum = 0
for j=0 > (p - ndegree), ndegree