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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 mnrfit.

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.

Objetos

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GeneralizedLinearModelGeneralized linear regression model class
CompactGeneralizedLinearModelCompact generalized linear regression model class
ClassificationLinearLinear model for binary classification of high-dimensional data
ClassificationECOCMulticlass model for support vector machines (SVMs) and other classifiers
ClassificationKernelGaussian kernel classification model using random feature expansion
ClassificationPartitionedLinearCross-validated linear model for binary classification of high-dimensional data
ClassificationPartitionedLinearECOCCross-validated linear error-correcting output codes model for multiclass classification of high-dimensional data

Funciones

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Crear un objeto GeneralizedLinearModel

fitglmCreate generalized linear regression model
stepwiseglmCreate generalized linear regression model by stepwise regression

Crear un objeto CompactGeneralizedLinearModel

compactCompact generalized linear regression model

Añadir o eliminar términos de un modelo lineal generalizado

addTermsAdd terms to generalized linear regression model
removeTermsRemove terms from generalized linear regression model
stepImprove generalized linear regression model by adding or removing terms

Predecir respuestas

fevalPredict responses of generalized linear regression model using one input for each predictor
predictPredict responses of generalized linear regression model
randomSimulate responses with random noise for generalized linear regression model

Evaluar un modelo lineal generalizado

coefCIConfidence intervals of coefficient estimates of generalized linear regression model
coefTestLinear hypothesis test on generalized linear regression model coefficients
devianceTestAnalysis of deviance for generalized linear regression model
partialDependenceCompute partial dependence

Visualizar un modelo lineal generalizado y estadísticas descriptivas

plotDiagnosticsPlot observation diagnostics of generalized linear regression model
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
plotResidualsPlot residuals of generalized linear regression model
plotSlicePlot of slices through fitted generalized linear regression surface

Recopilar propiedades de un modelo lineal generalizado

gatherGather properties of Statistics and Machine Learning Toolbox object from GPU

Crear un objeto

fitclinearFit binary linear classifier to high-dimensional data
fitcecocFit multiclass models for support vector machines or other classifiers
fitckernelFit binary Gaussian kernel classifier using random feature expansion
templateLinearLinear classification learner template

Predecir etiquetas

predictPredict labels for linear classification models
predictClassify observations using multiclass error-correcting output codes (ECOC) model
predictPredict labels for Gaussian kernel classification model
mnrfitMultinomial logistic regression
mnrvalMultinomial logistic regression values
glmfitFit generalized linear regression model
glmvalGeneralized linear model values

Temas

Regresión lineal generalizada

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.
  • Hierarchical Multinomial Models
    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.