Fitting Linear Models
Using the Fit Model platform, you can specify complex models efficiently. Your task is simplified by Macros, Attributes, and transformations. Fit Model is your gateway to fitting a broad variety of models and effect structures.
These include:
• simple and multiple linear regression
• analysis of variance and covariance
• random effect, nested effect, mixed effect, repeated measures, and split plot models
• nominal and ordinal logistic regression
• multivariate analysis of variance (MANOVA)
• canonical correlation and discriminant analysis
• loglinear variance (to model the mean and the variance)
• generalized linear models (GLM)
• parametric survival and proportional hazards
• response screening, for studying a large number of responses
In JMP Pro, you can also fit the following:
• mixed models with a range of covariance structures
• generalized regression models including the elastic net, lasso, and ridge regression
• partial least squares
The Fit Model platform lets you fit a large variety of types of models by selecting the desired personality. This chapter focuses on the elements of the Model Specification window that are common to most personalities.