Specialized Models • Introduction to Specialized Modeling
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Introduction to Specialized Modeling
Overview of Modeling Techniques
Specialized Models provides details about more technical modeling techniques, such as Response Screening, Partitioning, and Neural Networks.
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The Partition platform recursively partitions data according to a relationship between the X and Y values, creating a tree of partitions. See Chapter 3, “Partition Models”.
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The Neural platform implements a fully connected multi-layer perceptron with one or two layers. Use neural networks to predict one or more response variables using a flexible function of the input variables. See Chapter 4, “Neural Networks”.
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The Model Comparison platform lets you compare the predictive ability of different models. Measures of fit are provided for each model along with overlaid diagnostic plots. See Chapter 5, “Model Comparison”.
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The Nonlinear platform’s Fit Curve personality provides predefined models, such as polynomial, logistic, Gompertz, exponential, peak, and pharmacokinetic models. Compare different groups or subjects using a variety of analytical and graphical techniques. See Chapter 6, “Nonlinear Regression with Built-In Models”.
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The Nonlinear platform lets you fit custom nonlinear models, which include a model formula and parameters to be estimated. See Chapter 7, “Nonlinear Regression with Custom Models”.
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The Gaussian Process platform models the relationship between a continuous response and one or more continuous predictors. These models are common in areas like computer simulation experiments, such as the output of finite element codes, and they often perfectly interpolate the data. See Chapter 8, “Gaussian Process”.
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The Time Series platform lets you explore, analyze, and forecast univariate time series. See Chapter 9, “Time Series Analysis”.
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The Screening platform helps you analyze experimental data that results from a screening design. It enables you to select a model by identifying effects that have a large impact on the response. For details about the Screening platform, see the Design of Experiments Guide.
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The Response Screening platform automates the process of conducting tests across a large number of responses. Your test results and summary statistics are presented in data tables, rather than reports, to enable data exploration. See Chapter 10, “Response Screening”.