Overview

Statistics and Machine Learning Toolbox allows you to fit Nonlinear Regression Models. Once you fit a model, you can use it to predict or simulate responses, assess the model fit using hypothesis tests, or use plots to visualize diagnostics, residuals, and interaction effects. Parametric nonlinear models represent the relationship between a continuous response variable and one or more continuous predictor variables in the form y = f(X, b) + e, with f is a nonlinear function. fitnlm attempts to find values of the parameters b that minimize the mean squared differences between the observed responses y and the predictions of the model f(X, b). To do so, it needs a starting value beta0 before iteratively modifying the vector b to a vector with minimal mean squared error. This book develops nonlinear regression models taking into account the stages of identification, estimation, diagnosis and prediction. The most important content is the following: - Nonlinear Regression - Represent the Nonlinear Model - Choose Initial Vector beta0 - Fit Nonlinear Model to Data - Examine Quality and Adjust the Fitted Nonlinear Model - Predict or Simulate Responses Using a Nonlinear Model - Mixed-Effects Models - Introduction to Mixed-Effects Models - Mixed-Effects Model Hierarchy - Specifying Mixed-Effects Models - Specifying Covariate Models - Choosing nlmefit or nlmefitsa - Using Output Functions with Mixed-Effects Models - Examining Residuals for Model Verification - Mixed-Effects Models Using nlmefit and nlmefitsa - Multinomial Models for Nominal Responses - Multinomial Models for Ordinal Responses - Hierarchical Multinomial Models - Generalized Linear Models - Lasso Regularization of Generalized Linear Models - Regularize Poisson Regression - Regularize Logistic Regression - Regularize Wide Data in Parallel - Generalized Linear Mixed-Effects Models - Fit a Generalized Linear Mixed-Effects Model - Multivariate Generalized Linear Models - Multivariate Fixed Effects Panel Model with AutocorrelationMultivariate Longitudinal Analysis - Multivariate Longitudinal Analysis

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9781979547482

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