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Fitting Linear Models
Publication date: 05/05/2023

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

Image shown hereIn 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

Contents

Overview of the Fit Model Platform

Example of a Regression Analysis Using Fit Model

Launch the Fit Model Platform

Elements in the Fit Model Launch Window
Construct Model Effects
Fitting Personalities

Model Specification Options

Informative Missing

Validity Checks

Model Specification Templates

Simple Linear Regression
Polynomial in X to Degree k
Polynomial in X and Z to Degree k
Multiple Linear Regression
One-Way Analysis of Variance
Two-Way Analysis of Variance
Two-Way Analysis of Variance with Interaction
Three-Way Full Factorial
Analysis of Covariance, Equal Slopes
Analysis of Covariance, Unequal Slopes
Two-Factor Nested Random Effects Model
Three-Factor Fully Nested Random Effects Model
Simple Split Plot or Repeated Measures Model
Two-Factor Response Surface Model
Knotted Spline Effect
Want more information? Have questions? Get answers in the JMP User Community (community.jmp.com).