Modeling and Multivariate Methods - SAS

Chapter 9 Performing Nonlinear Regression 263
Fit a Custom Model
SSE Grid Create a grid of values around the solution estimates and compute the error sum of squares for
each value. The solution estimates should have the minimum SSE. When the option is selected, the
Specify Grid for Output report is shown with these features:
Parameter lists the parameters in the model.
Min displays the minimum parameter values used in the grid calculations. By default, Min is the solution
estimate minus 2.5 times the ApproxStdErr.
Max displays the maximum parameter value used in the grid calculations. By default, Max is the
solution estimate plus 2.5 times the ApproxStdErr.
Number of Points gives the number of points to create for each parameter. To calculate the total
number of points in the new grid table, multiply all the Number of Points values. Initially Number of
Points is 11 for the first two parameters and 3 for the rest. If you specify new values, use odd values to
ensure that the grid table includes the solution estimates. Setting Number of Points to 0 for any
parameter records only the solution estimate in the grid table.
When you click Go, JMP creates the grid of points in a new table. A highlighted row marks the solution
estimate row if the solution is in the table.
Revert to Original Parameters Resets the platform to the original parameter values (the values given in
the formula column parameters).
Remember Solution Creates a report called Remembered Models, which contains the current parameter
estimates and summary statistics. Results of multiple models can be remembered and compared. This is
useful if you want to compare models based on different parameter restrictions, or models fit using
different options. Click on the radio button for a particular model to display that model in the Plot and
the parameter estimates in the Control Panel.
Custom Estimate Gives an estimate of a function of the parameters. You provide an expression involving
only parameters. JMP calculates the expression using the current parameter estimates, and also calculates
a standard error of the expression using a first-order Taylor series approximation.
Custom Inverse Prediction Estimates the X value for a given Y value. It also calculates a standard error
for the estimated X. JMP must be able to invert the model. The standard error is based on the first-order
Taylor series approximation using the inverted expression. The confidence interval uses a t-quantile with
the standard error, and is a Wald interval.
Save Pred Confid Limits Saves asymptotic confidence limits for the model prediction. This is the
confidence interval for the average Y at a given X value.
Save Indiv Confid Limits Saves asymptotic confidence limits for an individual prediction. This is the
confidence interval for an individual Y value at a given X value.
Save Formulas
Gives options for saving model results to data table columns:
Save Prediction Formula saves the prediction formula with the current parameter estimates.
Save Std Error of Predicted saves the standard error for a model prediction. This is the standard error
for predicting the average Y for a given X. The formula is of the form
Sqrt(VecQuadratic(matrix1,vector1)). matrix1 is the covariance matrix associated with the
parameter estimates, and vector1 is a composition of the partial derivatives of the model with respect
to each parameter.