Note that yerror and xyerror are similar in both form and interpretation to the yerrorlines and xyerrorlines 2D plot styles. In this case the errors on x and y are treated by Orear's effective variance method. Xyerrors, for the case of 1 independent variable, indicates that there are two extra columns, with errors of both the independent and the dependent variable. Note that the number of independent variables is thus implicitly given by the total number of columns in the using qualifier, minus 1 (for the dependent variable), minus the number of variables in the errors qualifier.Īs an example, if one has 2 independent variables, and errors for the first independent variable and the dependent variable, one uses the errors x,z qualifier, and a using qualifier with 5 columns, which are interpreted as x:y:z:sx:sz (where x and y are the independent variables, z the dependent variable, and sx and sz the standard deviations of x and z).Ī few shorthands for the errors qualifier are available: yerrors (for fits with 1 column of independent variable), and zerrors (for the general case) are all equivalent to errors z, indicating that there is a single extra column with errors of the dependent variable. Again, flexible interpretation is possible by providing the using qualifier. For each variable in this list, an additional column will be read from the file, containing that variable's error estimate. The errors keyword is to be followed by a comma-separated list of one or more variable names for which errors are to be input the dependent variable z must always be among them, while independent variables are optional. In case of error estimates of the independent variables, these weights are further multiplied by fitting function derivatives according to the "effective variance method" (Jay Orear, Am. These error estimates are interpreted as the standard deviation s of the corresponding variable value and used to compute a weight for the datum as 1/s**2. This can be changed by using the errors keyword to read error estimates of one or more of the variables from the data file. The unitweights option, which is the default, causes all data points to be weighted equally. If a using specification is given, there can be up to 12 independent variables (and more if specially configured at compile time). If the file itself, or the using specification, contains only a single column of data, the line number is taken as the independent variable. In the absence of a using specification, the fit implicitly assumes there is only a single independent variable. For example to generate the independent variable x as the sum of columns 2 and 3, while taking z from column 6 and requesting equal weights: fit. The datafile contents can be interpreted flexibly by providing a using qualifier as with plot commands. All the plot datafile modifiers ( using, every.) except smooth are applicable to fit. Furthermore, the expression should depend on one or more variables whose value is to be determined by the fitting procedure. The names of the independent variables are set by the set dummy command, or in the part of the command (see below) by default, the first two are called x and y. can be any valid gnuplot expression, although the most common is a previously user-defined function of the form f(x) or f(x,y). The basic use of fit is best explained by a simple example: f(x) = a + b*x + c*x** 2 fit f(x) 'measured.dat' using 1: 2 via a,b,c Optionally, error estimates can be input for weighting the data points. There can be up to 12 independent variables, there is always 1 dependent variable, and any number of parameters can be fitted. FitThe fit command fits a user-supplied real-valued expression to a set of data points, using the nonlinear least-squares Marquardt-Levenberg algorithm.
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