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436 Main Menu Appendix B
The Analyze Menu
Screening Helps select a model to fit to a two-level screening design by showing which effects are
large. For details, see the JMP Statistics and Graphics Guide.
Nonlinear Fits nonlinear models, which are models that are nonlinear in their parameters. You
orchestrate the fitting process as a coordination of three important parts of JMP: the data table,
the formula editor, and the Nonlinear platform.
You define the nonlinear prediction formula with the formula editor. Then select Nonlinear with
the response variable as y and the model column with its fitting formula in the x role. You
interact with the platform through the Nonlinear Fitting control panel using:
• Buttons to start, stop, and step through the fitting process, and to reset parameter values
• Fitting options to specify loss functions and computational methods
• A processing messages area
• A list of current and limit convergence criteria and step counts, current parameter estimates,
and error sum of squares
• Options to specify the alpha level for confidence intervals and delta for numerical derivatives
The Nonlinear platform can show the model and the derivatives of the model with respect to
each of its parameters, and the fitting solution reports. There are features that give confidence
intervals on the parameters and plot the resulting function if it is of a single variable. You can also
save the SSE values in a data table with a grid for plotting them. The chapter “Nonlinear
Regression” of JMP Statistics and Graphics Guide describes the Nonlinear command in detail and
gives examples.
Neural Net Is a standard type of neural network. It is a particular case of a back propagation
feed-forward multilayer-perception neural net. The neural network is a set of nonlinear
equations that predict output variables (y) from input variables (x) in a flexible way using layers
of linear regressions and S-shaped functions. JMP fits the neural net using standard nonlinear
least squares regression methods. See the JMP Statistics and Graphics Guide for details.
Gaussian Process Models the relationship between a continuous response and one or more
continuous predictors. These models are common in areas like computer simulation
experiments, such as the output of finite element codes, and they often perfectly interpolate the
data. Gaussian processes can deal with these no-error-term models.
The Gaussian Process platform fits a spatial correlation model to the data, where the correlation
of the response between two observations decreases as the values of the independent variables
become more distant.
The main purpose for using this platform is to obtain a prediction formula that can be used for
further analysis and optimization. For details, see the JMP Statistics and Graphics Guide.
Partition Recursively partitions rows into groups according to x values that associate with y values.
This partitioning creates a tree of partitions.
The factor columns (x) can be either continuous or categorical (nominal or ordinal). If an x is
continuous, then the splits (partitions) are created by a cutting value, which divides the sample
into values below and values above this cutting value. If the x is categorical, then the sample is
divided into two groups of levels.
The response column (y) can be either continuous or categorical (nominal or ordinal). If y is
continuous, then the platform fits means, and creates splits which most significantly separate the