JMP 11 Online Documentation (English)
Discovering JMP
Using JMP
Basic Analysis
Essential Graphing
Profilers
Design of Experiments Guide
Fitting Linear Models
Specialized Models
Multivariate Methods
Quality and Process Methods
Reliability and Survival Methods
Consumer Research
Scripting Guide
JSL Syntax Reference
JMP iPad Help
Capabilities Index
Fitting Linear Models
• Logistic Regression with Nominal or Ordinal Responses
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Logistic Regression with Nominal or Ordinal Responses
Fit Models for Categorical Responses
For nominal response variables, the Fit Model platform fits a linear model to a multi-level logistic response function using maximum likelihood. Likelihood-ratio statistics and Lack of Fit tests are computed for the whole model. Likelihood-ratio tests and Wald tests can be computed for each effect in the model. When the response is binary, odds ratios (with confidence intervals) are available.
For ordinal response variables, the Fit Model platform fits the cumulative response probabilities to the logistic distribution function of a linear model using maximum likelihood. Likelihood-ratio test statistics are provided for the whole model and lack of fit.
For simple main effects, you can use the Fit Y by X platform to see a cumulative logistic probability plot for each effect. See the
Basic Analysis
book.
Contents
Introduction to Logistic Models
The Logistic Fit Report
Logistic Plot
Iteration History
Whole Model Test
Lack of Fit Test (Goodness of Fit)
Parameter Estimates
Likelihood Ratio Tests
Logistic Fit Platform Options
Plot Options
Likelihood Ratio Tests
Wald Tests for Effects
Confidence Intervals
Odds Ratios (Nominal Responses Only)
Inverse Prediction
Save Commands
ROC Curve
Lift Curve
Confusion Matrix
Profiler
Validation
Example of a Nominal Logistic Model
Example of an Ordinal Logistic Model
Example of a Quadratic Ordinal Logistic Model
Stacking Counts in Multiple Columns