2.1.11 Fitting

Functions

Name Brief Example
fitlinear a simplified version of Linear fit Examples
fitpoly Fit a polynomial equation to XY vectors and return the coefficients and statistical results. Examples
nlfFit Get the value of the specified dependent variable for the specified value of the independent variable. Examples
nlfXfromY Get the value(s) of the independent variable for the specified value of the specified dependent variable. Examples
nlsf_func_to_fdf Find nlf FDF name by function name from given category's file list. Examples
nslf_get_func_list Load a functions list from certain Category. Examples
nlf_get_section_keys_and_values Get the keys and values from sepcial section name from NLSF.ini file. Examples
nlsf_install_fdf Install a fitting function into Origin using its FDF file, optionally specifying the category to install it into. Examples
nlsf_uninstall_fdf Uninstall a fitting function from Origin using its FDF file, optionally specifying the category to uninstall it from. Examples
ocmath_AIC Function to compute Akaike's Information Criterion (AIC) Examples
ocmath_calc_conf_and_pred_bands_for_polynomial Calculate ranges of confidence or prediction in polynomial fitting Examples
ocmath_diagnostic_residuals Function to compute diagnostic residual for fits(NLSF, LR, PR and MR) Examples
ocmath_init_linear_fit_options Function to set default options to LROptions structure for simple linear regression. Examples
ocmath_fit_linear_multi_regions Split the curve to segments and then perform linear fit on them Examples
ocmath_linear_fit Function to perform simple linear regression. using g02cac, The computational engine is the NAG function nag_simple_linear_regression (g02cac). Examples
ocmath_multiple_linear_regression Function to perform multiple linear regression. Examples
ocmath_partial_reg_plots Function to compute the data for partial regression plot Examples
ocmath_polynomial This function constructs a polynomial curve Examples
ocmath_polynomial_fit Function to Perform polynomail fit on the given X and Y data points. The function will normailize X data first, and then call ocmath_multiple_linear_regression(), which is the main computational engine, and finally correct the variance-covariance matrix. Examples
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