Fitting Linear Models
Publication date: 10/01/2019

Fitting Linear Models

Model Specification

Specify 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.

Contents

Overview of the Fit Model Platform

Example of a Regression Analysis Using Fit Model

Launch the Fit Model Platform

Fit Model Launch Window Elements
Construct Model Effects
Fitting Personalities

Model Specification Options

Informative Missing

Validity Checks

Examples of Model Specifications and Their Model Fits

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
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