Generalized Linear Models
by SAS Institute
JMP 13 Fitting Linear Models, Second Edition, 2nd Edition
- Contents
- Learn about JMP
- Model Specification
- Specify Linear Models
- Overview of the Fit Model Platform
- Example of a Regression Analysis Using Fit Model
- Launch the Fit Model Platform
- Model Specification Options
- 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
- Standard Least Squares Report and Options
- Analyze Common Classes of Models
- Example Using Standard Least Squares
- Launch the Standard Least Squares Personality
- Fit Least Squares Report
- Response Options
- Regression Reports
- Estimates
- Effect Screening
- Factor Profiling
- Row Diagnostics
- Save Columns
- Effect Summary Report
- Mixed and Random Effect Model Reports and Options
- Models with Linear Dependencies among Model Terms
- Statistical Details
- Standard Least Squares Examples
- Analyze Common Classes of Models
- One-Way Analysis of Variance Example
- Analysis of Covariance with Equal Slopes Example
- Analysis of Covariance with Unequal Slopes Example
- Response Surface Model Example
- Split Plot Design Example
- Estimation of Random Effect Parameters Example
- Knotted Spline Effect Example
- Bayes Plot for Active Factors Example
- Stepwise Regression Models
- Find a Model Using Variable Selection
- Overview of Stepwise Regression
- Example Using Stepwise Regression
- The Stepwise Report
- Models with Crossed, Interaction, or Polynomial Terms
- Models with Nominal and Ordinal Effects
- Performing Binary and Ordinal Logistic Stepwise Regression
- The All Possible Models Option
- The Model Averaging Option
- Using Validation
- Generalized Regression Models
- Build Models Using Variable Selection Techniques
- Generalized Regression Overview
- Example of Generalized Regression
- Launch the Generalized Regression Personality
- Generalized Regression Report Window
- Model Launch Control Panel
- Model Fit Reports
- Model Fit Options
- Statistical Details
- Generalized Regression Examples
- Mixed Models
- Jointly Model the Mean and Covariance
- Overview of the Mixed Model Personality
- Example Using Mixed Model
- Launch the Mixed Model Personality
- The Fit Mixed Report
- Multiple Comparisons
- Marginal Model Inference
- Conditional Model Inference
- Save Columns
- Additional Examples
- Statistical Details
- Multivariate Response Models
- Loglinear Variance Models
- Logistic Regression Models
- Generalized Linear Models
- Fit Models for Nonnormal Response Distributions
- Generalized Linear Models Overview
- Example of a Generalized Linear Model
- Launch the Generalized Linear Model Personality
- Generalized Linear Model Fit Report
- Generalized Linear Model Fit Report Options
- Additional Examples of the Generalized Linear Models Personality
- Statistical Details
- Statistical Details
- References
- Index
Chapter 12
Generalized Linear Models
Fit Models for Nonnormal Response Distributions
Generalized linear models provide a unified way to fit responses that do not fit the usual requirements of traditional linear models. For example, frequency counts are often characterized as having a Poisson distribution and fit using a generalized linear model.
The Generalized Linear Model personality of the Fit Model platform enables you to fit generalized linear models for responses with binomial, normal, Poisson, or exponential distributions. The platform provides reports similar to those that are provided for traditional linear models. The platform also accommodates separation in logistic regression models using the Firth correction.
Figure 12.1 Example of a Generalized Linear Model Fit
-
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.