Fitting Linear Models
Model Specification
Specify Linear Models
The Fit Model platform enables you to specify a variety of complex models using different fitting techniques or personalities. This chapter focuses on elements that are common to most personalities.
Fit Model personalities enable you to fit the following types of models:
• 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 models:
• generalized regression models including the elastic net, lasso, and ridge regression
• mixed models with a range of covariance structures
• generalized linear mixed models (GLMM)
• partial least squares