JMP 14.2 Online Documentation (English)
Discovering JMP
Using JMP
Basic Analysis
Essential Graphing
Profilers
Design of Experiments Guide
Fitting Linear Models
Predictive and Specialized Modeling
Multivariate Methods
Quality and Process Methods
Reliability and Survival Methods
Consumer Research
Scripting Guide
JSL Syntax Reference
JMP iPad Help
JMP Interactive HTML
Capabilities Index
JMP 13.2 Online Documentation
Predictive and Specialized Modeling
•
Model Comparison
• Model Comparison Platform Options
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Model Comparison Platform Options
Continuous and Categorical Responses
Model Averaging
Makes a new column of the arithmetic mean of the predicted values (for continuous responses) or the predicted.probabilities (for categorical responses).
Continuous Responses
Plot Actual by Predicted
Shows a scatterplot of the actual versus the predicted values. The plots for the different models are overlaid.
Plot Residual by Row
Shows a plot of the residuals by row number. The plots for the different models are overlaid.
Profiler
Shows a profiler for each response based on prediction formula columns in your data. The profilers have a row for each model being compared.
Categorical Responses
ROC Curve
Shows ROC curves for each level of the response variable. The curves for the different models are overlaid.
AUC Comparison
Provides a comparison of the area under the ROC curve (AUC) from each model. The area under the curve is the indicator of the goodness of fit, where 1 is a perfect fit.
The report includes the following information:
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standard errors and confidence intervals for each AUC
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standard errors, confidence intervals, and hypothesis tests for the difference between each pair of AUCs
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an overall hypothesis test for testing whether all AUCs are equal
Lift Curve
Shows lift curves for each level of the response variable. The curves for the different models are overlaid.
Cum Gains Curve
Shows cumulative gains curves for each level of the response variable. A cumulative gains curve is a plot of the proportion of a response level that is identified by the model against the proportion of all responses. A cumulative gains curve for a perfect model would reach 1.0 at the overall proportion of the response level. The curves for the different models are overlaid.
Confusion Matrix
Shows confusion matrices for each model. A confusion matrix is a two-way classification of actual and predicted responses. Count and rate confusion matrices are shown. Separate confusion matrices are produced for each level of the Group variable.
If the response has a Profit Matrix column property, then Actual by Decision Count and Actual by Decision Rate matrices are shown to the right of the confusion matrices. For details about these matrices, see
Additional Examples of Partitioning
in Partition Models
.
Profiler
Shows a profiler for each response based on prediction formula columns in your data. The profilers have a row for each model being compared.
Related Information
•
ROC Curve
in Partition Models
•
Lift Curve
in Partition Models
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Help created on 3/19/2020