INTERNAL USE ONLY: This document is internal use only at this time.
Key features exclusive to JMP® Pro. JMP Pro includes all of the features in JMP, plus the additional capabilities for analytics listed below.
Predictive Modeling
Neural network modeling
- Automated handling of missing data.
- Automatic selection of the number of hidden units using gradient boosting.
- Fit both one- and two-layer neural networks.
- Automated transformation of input variables.
- Three activation functions (Hyperbolic Tangent, Linear, Gaussian).
- Save randomly generated cross-validation columns.
- Save transformed covariates.
- Support for validation column.
Recursive partition modeling
- Choice of methods: Decision tree, Bootstrap forest (a random-forest technique), Boosted tree, K nearest neighbors, Naive Bayes
- Set random seed, suppress multithreading, use tuning design table, stochastic gradient descent available in Boosted Trees and Bootstrap Forest.
- Support for validation column.
- Dedicated model launch options for: Bootstrap forest, Boosted tree, K nearest neighbors and Naive Bayes.
Model Comparison
- Compare models built in JMP Pro.
- Profiler.
- Fit statistics (R2, Misclassification Rate, ROC curves, AUC, Lift Curves).
- Model averaging.
Make Validation Column
- Automatic partitioning of data into training, validation and test portions; creation of validation columns.
- Formula random, fixed random, stratified random, grouped random, cutpoint methods to create the holdback sets.
- Validation column creation from platform launch by clicking validation column role (Formula random only).
Formula Depot
- Stores and manages Formula Column scripts.
- Publish commands available for Discriminant, Fit Least Squares (7 commands), Fit Logistic (Nominal and Ordinal), Decision tree, Bootstrap forest, Boosted trees, Uplift, K nearest neighbors, Naive Bayes, Neural, Latent class analysis, Principal components (wide and sparse), Generalize regression, PLS, Gaussian process.
- Generate Score Code: SAS (DS2), C, Python, Javascript, SQL (with choice of syntax options for different destinations).
- Direct comparison of models collected in the Formula Depot by using Model Comparison.
- Profiler.
- Show script, copy script, copy formula, copy formula as column transform, run script to generate formula column in the data table.
- Add formulas from data table columns.
Fit Model
Generalized regression
- Regularization techniques: Ridge, Lasso, adaptive Lasso, Double Lasso, Elastic Net, adaptive Elastic Net.
- Forward selection and Two-Stage Forword Selection.
- Quantile regression.
- Handles censored data allowing you to do variable selection with survival/reliability data.
- Cox Proportional Hazards.
- Save simulation formula for use in the general simulation platform.
- Normal, LogNormal, Weibull, Cauchy, exponential, Gamma, Beta, binomial, Beta binomial, Poisson, negative binomial distribution.
- Zero inflated binomial, Beta binomial, Poisson, negative binomial, Gamma distribution.
- Choice of validation methods: Validation column, KFold, holdback, leave-one-out, BIC, AICc, ERIC.
Stepwise regression
- Support for validation column.
Logistic regression (nominal and ordinal)
- Support for validation column.
Standard Least Squares
- Support for validation column.
Partial least squares (PLS)
- PLS personality in Fit Model supports continuous or categorical response; continuous or categorical factors, interactions and polynomial terms.
- NIPALS-style missing value imputation.
- Save randomly generated cross-validation columns.
- A Standardize X option, which centers and scales individual variables that are included in a polynomial effect prior to applying the centering and scaling options.
- Choice of validation methods: Validation column, KFold, holdback, leave-one-out.
Mixed Models
- Specify fixed, random and repeated effects.
- Correlate groups of variables, set up subject and continuous effects.
- Choice of repeated covariance structure.
- Variograms serve as a visual diagnostic to determine which, if any, spatial correlation structure is most appropriate.
Text Explorer Analytics
- Latent Class Analysis.
- Latent Semantic Analysis (Sparse SVD).
- Topic Analysis (Rotated SVD).
- Cluster Terms and Documents.
- SVD and Topic Scatterplot Matrix.
- Save Columns: Document Singular and Topic Vectors, Stacked DTM for Association.
- Save Formula: Singular Vector, Topic Vector.
- Save Vectors: Term and Topic.
Multivariate Methods
Discriminant analysis
- Support for validation column.
Specialized Models
Gaussian Process
- Ability to fit models with thousands of rows through Fast GASP.
- Add categorical variables to your Gaussian Process models.
Reliability and Survival Models
Reliability Block Diagram (RBD)
- Build models of complex system reliability.
- Use basic, serial, parallel, knot, and K out of N nodes to build systems.
- Build nested designs using elements from design library.
Repairable System Simulation (RSS)
- Discrete Event Simulation based engine.
- Support traditional maintenance: corrective maintenance and preventive maintenance, as off-the-shelf building blocks.
- Introducing a diagrammatic representation of maintenance arrangement along side of a RBD in a single workspace.
- Diagrammatic linkages between event and action elements across components to convey the idea of grouped maintenance and maintenance dependencies.
Parametric Survival
- Supports variables selection through a bridge to the Generalized Regression personality of Fit Model.
Generalized Regression
- Handles censored data allowing you to do variable selection with survival/reliability data.
- Support for Cox Proportional Hazards.
- Supports Weibull, LogNormal, Exponential, Gamma, Normal and ZI family of Distributions.
Covering Arrays
- Design and analyze covering arrays.
- Optimize designs after they are created for further run reduction.
- Use disallowed combinations filter to specify infeasible testing regions.
- Import covering arrays created by other software; analyze coverage and optionally further optimize.
Consumer Research
Uplift Models
- Decision tree method to identify the consumer segments most likely to respond favorably to an offer or treatment.
- Incremental, true-lift, net modeling technique.
- Support for validation column.
Choice Models
- Support for Hierarchical Bayes in Choice
- Save subject estimates and Bayes Chain.
Association Analysis
- Support for Market Basket analysis.
- Analyze stacked document term matrix generated by the Text Explorer platform.
Advanced Computational Statistics
Oneway Analysis
- Nonparametric exact tests.
Contingency Analysis
- Exact measures of association.
General Bootstrapping
- Bootstrap statistics in most reports in a single click.
General Simulation Functionality
- Single click simulate statistics in most reports.
- Power calculations on almost anything.
- Support for parametric bootstrapping
- Randomization testing.
