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Regresión lineal generalizada

Modelos de regresión lineal generalizada con varias distribuciones y funciones de enlace, incluida la regresión logística

Para aumentar la precisión y las opciones de funciones de enlace en conjuntos de datos de dimensiones bajas y medianas, ajuste un modelo de regresión lineal generalizada mediante fitglm. En una regresión logística multinomial, ajuste un modelo mediante fitmnr.

Para reducir el tiempo de proceso en conjuntos de datos de altas dimensiones, entrene un modelo de clasificación lineal binaria, por ejemplo, un modelo de regresión logística, mediante fitclinear. También puede entrenar de forma eficiente un modelo multiclase de códigos de salida de corrección de errores (ECOC, por sus siglas en inglés) compuesto por modelos de regresión logística mediante fitcecoc.

Para las clasificaciones no lineales con big data, entrene un modelo de clasificación binaria de kernel gaussiano con regresión logística mediante fitckernel.

Bloques

ClassificationLinear Predict

Classify observations using linear classification model (desde R2023a)

Funciones

Regresión lineal generalizada utilizando un objeto `GeneralizedLinearModel`

Crear un objeto `GeneralizedLinearModel`

`fitglm`	Crear un modelo de regresión lineal generalizado
`stepwiseglm`	Create generalized linear regression model by stepwise regression

Crear un objeto `CompactGeneralizedLinearModel`

compact Compact generalized linear regression model

Añadir o eliminar términos de un modelo lineal generalizado

`addTerms`	Add terms to generalized linear regression model
`removeTerms`	Remove terms from generalized linear regression model
`step`	Improve generalized linear regression model by adding or removing terms

Predecir respuestas

`feval`	Predict responses of generalized linear regression model using one input for each predictor
`predict`	Predict responses of generalized linear regression model
`random`	Simular respuestas con ruido aleatorio para un modelo de regresión lineal generalizada

Evaluar un modelo lineal generalizado

`coefCI`	Confidence intervals of coefficient estimates of generalized linear regression model
`coefTest`	Linear hypothesis test on generalized linear regression model coefficients
`devianceTest`	Analysis of deviance for generalized linear regression model
`partialDependence`	Compute partial dependence (desde R2020b)

Visualizar un modelo lineal generalizado y estadísticas descriptivas

`plotDiagnostics`	Plot observation diagnostics of generalized linear regression model
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`plotResiduals`	Plot residuals of generalized linear regression model
`plotSlice`	Plot of slices through fitted generalized linear regression surface

Recopilar propiedades de un modelo lineal generalizado

gather Gather properties of Statistics and Machine Learning Toolbox object from GPU (desde R2020b)

Regresión logística multinomial utilizando un objeto `MultinomialRegression`

Crear un objeto `MultinomialRegression`

fitmnr Fit multinomial regression model (desde R2023a)

Trabajar con un objeto `MultinomialRegression`

`coefCI`	Confidence intervals for coefficient estimates of multinomial regression model (desde R2023a)
`coefTest`	Linear hypothesis test on multinomial regression model coefficients (desde R2023a)
`feval`	Predict responses of multinomial regression model using one input for each predictor (desde R2023a)
`partialDependence`	Compute partial dependence (desde R2020b)
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`plotResiduals`	Plot residuals of multinomial regression model (desde R2023a)
`plotSlice`	Plot of slices through fitted multinomial regression surface (desde R2023a)
`predict`	Predict responses of multinomial regression model (desde R2023a)
`random`	Generate random responses from fitted multinomial regression model (desde R2023a)
`testDeviance`	Deviance test for multinomial regression model (desde R2023a)

Clasificación con regresión logística

Crear un objeto

`fitclinear`	Fit binary linear classifier to high-dimensional data
`fitcecoc`	Fit multiclass models for support vector machines or other classifiers
`fitckernel`	Fit binary Gaussian kernel classifier using random feature expansion
`templateLinear`	Linear learner template

Predecir etiquetas

`predict`	Predict labels for linear classification models
`predict`	Classify observations using multiclass error-correcting output codes (ECOC) model
`predict`	Predict labels for Gaussian kernel classification model

Regresión lineal generalizada sin utilizar ningún objeto

`glmfit`	Fit generalized linear regression model
`glmval`	Generalized linear model values

Objetos

Regresión lineal generalizada

`GeneralizedLinearModel`	Generalized linear regression model class
`CompactGeneralizedLinearModel`	Compact generalized linear regression model class

Regresión multinomial

MultinomialRegression Multinomial regression model (desde R2023a)

Clasificación con regresión logística

`ClassificationLinear`	Linear model for binary classification of high-dimensional data
`ClassificationECOC`	Multiclass model for support vector machines (SVMs) and other classifiers
`ClassificationKernel`	Gaussian kernel classification model using random feature expansion
`ClassificationPartitionedLinear`	Cross-validated linear model for binary classification of high-dimensional data
`ClassificationPartitionedLinearECOC`	Cross-validated linear error-correcting output codes model for multiclass classification of high-dimensional data

Temas

Regresión lineal generalizada

Generalized Linear Models
Generalized linear models use linear methods to describe a potentially nonlinear relationship between predictor terms and a response variable.
Generalized Linear Model Workflow
Fit a generalized linear model and analyze the results.
Fitting Data with Generalized Linear Models
Fit and evaluate generalized linear models using glmfit and glmval.
Train Binary GLM Logistic Regression Classifier Using Classification Learner App
Create and compare binary logistic regression classifiers, and export trained models to make predictions for new data.
Predict Class Labels Using ClassificationLinear Predict Block
This example shows how to use the ClassificationLinear Predict block for label prediction in Simulink®. (desde R2023a)
Wilkinson Notation
Wilkinson notation provides a way to describe regression and repeated measures models without specifying coefficient values.

Regresión logística multinomial

Multinomial Models for Nominal Responses
A nominal response variable has a restricted set of possible values with no natural order between them. A nominal response model explains and predicts the probability that an observation is in each category of a categorical response variable.
Multinomial Models for Ordinal Responses
An ordinal response variable has a restricted set of possible values that fall into a natural order. An ordinal response model describes the relationship between the cumulative probabilities of the categories and predictor variables.
Multinomial Models for Hierarchical Responses
A hierarchical multinomial response variable (also known as a sequential or nested multinomial response) has a restricted set of possible values that fall into hierarchical categories. The hierarchical multinomial regression models are extensions of binary regression models based on conditional binary observations.