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# Nonlinear Models Options

Select the output to be generated in a Nonlinear regression analysis. ## Display

 Model Details of the model that is fitted Summary Summary analysis of variance/deviance F-probability Approximate F-probabilities for variance/deviance ratios Correlations Correlations between the parameter estimates Estimates Estimates of the parameters in the model t-probability Approximate t-probabilities for the parameter estimates Fitted values Table containing the values of the response variate, fitted values, standardized residuals and leverages Accumulated Analysis-of-variance/deviance table containing a line for each change in the fitted model Monitoring Monitoring information at each iteration

## Dispersion parameter

Controls whether the dispersion parameter for the variance of the response is estimated from the residual mean square of the fitted model, or fixed at a given value. The dispersion parameter (fixed or estimated) is used when calculating standard errors and standardized residuals. In models with the binomial, Poisson, negative binomial, geometric and exponential distributions, the dispersion should be fixed at 1 unless a heterogeneity parameter is to be estimated.

## Estimate constant term

If the estimation includes a linear parameter, you can specify whether to include a constant in the model.

## Algorithm for fitting model

A list of different algorithms available for fitting a nonlinear model.

## Maximum number of iterations

Specifies the maximum number of cycles in the iterative estimation process.

## Calculate standard errors for linear parameters

For a model including linear parameters, you can specify the standard errors for each of the linear parameters to be evaluated.

## Offset

A nonlinear model can be modified to take account of a fixed contribution to the linear effects for each unit, supplied in a variate referred to as the offset.

## Weights

A variate of weights can be supplied to give varying influence of each unit on the fit of the model. This would usually correspond to a known pattern of variance of the observations, when the weights would be the reciprocal of the variances.

## Factorial limit on model terms

For a model including linear parameters, you can control the factorial limit on model terms to be generated when you use model-formula operators like *. Th default is to include all interactions, up to those involving nine variates or factors. (You cannot ask for more than nine.)