polyfit [ parameter=value ... ] [ inputfile outputfile ] polyfit [ parameter=value ... ] [ inputfile ... directory ]
Parameters are: x_var, y_vars, order, points, printout
The function polyfit computes the best-fit polynomial between pairs of x-y variables, e.g. y = a0 + a1 * x ... an * x^n, where n is given by the parameter order. x_var is a single variable representing x in the above relationship, and y_vars can be specified to be one or more sets of y values. Both the x and y variables specified must be in the same TeraScan dataset and must have the same type and dimensions.
The output data set contains selected values of the x_var variable and the associated, estimated values of the y_var variables. points is the number of x_var points to use in computing the estimated functions. The points are evenly disributed between the maximum and minimum x_var values. This output dataset is useful for the purposes of plotting the estimated relationship over the raw input data. The output dataset also contains, as attributes, the coefficients of the polynomial and the correlation between the input values and the values estimated by the polynomial.
NOTE: the algorithm assumes that errors exist only in the y variable and therefore minimizes the vertical distance from each point to the regression line (see REFERENCES and linfit).
For each x-y pair, polyfit outputs the following information:
Mean Mean values of y and the estimated y Stand Dev Standard deviations of y and the estimated y Correlation Correlation between y and the estimated y Bias Mean difference between y and the esimated y RMS RMS difference between y and the estimated y Count Number of data points in the y variables Good Number of good points in common between x and y Polynomial Coefficients in the form: a0 + a1*x ... an*x^n
Specifies the x variable.
There is no default.
Specifies the y variables to use in determining the best-fit polynomials against x. A polynomial relation and its corresponding output is produced for each y variable specified. If the list is preceded by a minus sign, then all variables, except those listed, are included. Wildcards * and ? are allowed. If the y_var list happens to include the x variable, this variable is filtered out by the processing and no corresponding output is printed for this x,y (i.e. x,x) pair.
The default is to include all variables.
Specifies the order of the polynomial to fit to each x-y pair.
The default is 1.
Number of points to compute the estimated relationship with based on evenly distributed values between the minimum and maximum x_var values.
The default is 100.
Whether or not to send the printout to the standard printer. Valid responses are [yes, no].
The default is no.
This example uses a dataset with four variables representing temperature and specific humidity at the sea surface and low-level atmosphere. These variables are denoted: Ts, Qs, Ta, Qa. A second-order polynomial is computed for Qs, Qa, Ta against Ts.
% polyfit bndlayer.tdf bndout.tdf x_var : char( 31) ? Ts y_vars : char(255) ? [] order : int ? [1] 2 points : int ? [100] printout : char( 3) ? [no] Polynomial Fit Page 1 bndlayer.tdf Qa Estimate Mean 18.123 18.1233 Stand. Dev. 2.07929 0.978818 Bias/RMS 0.0003 1.954 Good/Total 241 360 Correlation 0.689827 0.0527713 + 0.710641 * x^1 - 0.00196545 * x^2 bndlayer.tdf Qs Estimate Mean 23.0371 23.037 Stand. Dev. 4.80658 4.33331 Bias/RMS 0.0001 0.154 Good/Total 241 360 Correlation 0.948868 0.0279024 + 0.376072 * x^1 + 0.0166357 * x^2 bndlayer.tdf Ta Estimate Mean 27.1467 27.1459 Stand. Dev. 2.34562 1.51162 Bias/RMS 0.0008 1.052 Good/Total 245 360 Correlation 0.793531 0.090875 + 1.22431 * x^1 - 0.00876313 * x^2
spectral, dimcorr, binavg, xcorrel, linfit, correlate, regress, stats.
dpolft - SLATEC math library.
Last Update: $Date: 1998/05/29 20:16:46 $