Regressione lineare generalizzata

Modelli di regressione lineare generalizzata con diverse distribuzioni e funzioni di collegamento, compresa la regressione logistica

Per ottenere una maggiore precisione e una scelta più ampia di funzioni di collegamento su insiemi di dati a bassa e media dimensionalità, adattare un modello di regressione lineare generalizzata utilizzando fitglm. Per una regressione logistica multinomiale, adattare un modello utilizzando fitmnr.

Per ridurre i tempi computazionali su insiemi di dati ad alta dimensionalità, addestrare un modello di classificazione lineare binario, come ad esempio un modello di regressione logistica, utilizzando fitclinear. È inoltre possibile addestrare in modo efficiente un modello ECOC (codici di output con correzione degli errori) multiclasse, composto da modelli di regressione logistica, utilizzando fitcecoc.

Per la classificazione non lineare con grandi quantità di dati, addestrare un modello di classificazione binario con kernel gaussiano e regressione logistica utilizzando fitckernel.

Blocchi

ClassificationLinear Predict

Classify observations using linear classification model (Da R2023a)

Funzioni

Regressione lineare generalizzata utilizzando l'oggetto `GeneralizedLinearModel`

Creazione di oggetti `GeneralizedLinearModel`

`fitglm`	Create generalized linear regression model
`stepwiseglm`	Create generalized linear regression model by stepwise regression

Creazione di oggetti `CompactGeneralizedLinearModel`

compact Compact generalized linear regression model

Aggiunta o rimozione di termini dal modello lineare generalizzato

`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

Previsione delle risposte

`feval`	Predict responses of generalized linear regression model using one input for each predictor
`predict`	Predict responses of generalized linear regression model
`random`	Simulate responses with random noise for generalized linear regression model

Valutazione del modello lineare generalizzato

`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

Visualizzazione del modello lineare generalizzato e delle statistiche di riepilogo

`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

Acquisizione delle proprietà del modello lineare generalizzato

gather Gather properties of Statistics and Machine Learning Toolbox object from GPU

Regressione logistica multinomiale utilizzando l'oggetto `MultinomialRegression`

Creazione di oggetti `MultinomialRegression`

fitmnr Fit multinomial regression model (Da R2023a)

Utilizzo dell'oggetto `MultinomialRegression`

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

Classificazione con regressione logistica

Creazione di oggetti

`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

Previsione delle etichette

`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

Regressione lineare generalizzata senza utilizzo dell'oggetto

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

Oggetti

Regressione lineare generalizzata

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

Regressione multinomiale

MultinomialRegression Multinomial regression model (Da R2023a)

Classificazione con regressione logistica

`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

Argomenti

Regressione lineare generalizzata

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.
Predict Class Labels Using ClassificationLinear Predict Block
This example shows how to use the ClassificationLinear Predict block for label prediction in Simulink®. (Da R2023a)
Logistic Regression with Tall Arrays
This example shows how to use logistic regression and other techniques to perform data analysis on tall arrays.
Wilkinson Notation
Wilkinson notation provides a way to describe regression and repeated measures models without specifying coefficient values.

Regressione logistica multinomiale

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.