The LINFIT function fits the paired data {xi, yi} to the linear model, y = A + Bx, by minimizing the chi-square error statistic.
This routine is written in the IDL language. Its source code can be found in the file linfit.pro in the lib subdirectory of the IDL distribution.
Result = LINFIT( X, Y [, CHISQ=variable] [, COVAR=variable] [, /DOUBLE] [, MEASURE_ERRORS=vector] [, PROB=variable] [, SIGMA=variable] [, YFIT=variable] )
The result is a two-element vector containing the linear model parameters [A, B].
An n-element integer, single-, or double-precision floating-point vector.
An n-element integer, single-, or double-precision floating-point vector.
Set this keyword to a named variable that will contain the value of the unreduced chi-square goodness-of-fit statistic.
Set this keyword to a named variable that will contain the covariance matrix of the coefficients.
| Note |
Set this keyword to force the computation to be done in double-precision arithmetic.
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 |
Set this keyword to a named variable that will contain the probability that the computed fit would have a value of CHISQ or greater. If PROB is greater than 0.1, the model parameters are "believable". If PROB is less than 0.1, the accuracy of the model parameters is questionable.
The SDEV keyword is obsolete and has been replaced by the MEASURE_ERRORS keyword. Code that uses the SDEV keyword will continue to work as before, but new code should use the MEASURE_ERRORS keyword. The definition of the MEASURE_ERRORS keyword is identical to that of the SDEV keyword.
Set this keyword to a named variable that will contain the 1-sigma uncertainty estimates for the returned parameters
| Note |
Set this keyword equal to a named variable that will contain the vector of calculated Y values.
; Define two n-element vectors of paired data:
X = [-3.20, 4.49, -1.66, 0.64, -2.43, -0.89, -0.12, 1.41, $
2.95, 2.18, 3.72, 5.26]
Y = [-7.14, -1.30, -4.26, -1.90, -6.19, -3.98, -2.87, -1.66, $
-0.78, -2.61, 0.31, 1.74]
; Define an n-element vector of Poisson measurement errors:
measure_errors = SQRT(ABS(Y))
; Compute the model parameters, A and B, and print the result:
result = LINFIT(X, Y, MEASURE_ERRORS=measure_errors)
PRINT, result
IDL prints:
-3.16574 0.829856
Introduced: 4.0
COMFIT, CURVEFIT, GAUSSFIT, LADFIT, LMFIT, POLY_FIT, REGRESS, SFIT, SVDFIT