The NLIN procedure produces least squares or weighted least squares estimates of the parameters of a nonlinear model.
Nonlinear models are more difficult to specify and estimate than linear models.
Instead of simply listing regressor variables, you must write the regression expression, declare parameter names, and supply initial parameter values. Some models are difficult to fit, and there is no guarantee that the procedure can fit the model successfully. To begin this process the NLIN procedure first examines the starting value specifications of the parameters. If a grid of values is specified, PROC NLIN evaluates the residual sum of squares at each combination of parameter values to determine the set of parameter values producing the lowest residual sum of squares. These parameter values are used for the initial step of the iteration.