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Regresión lineal múltiple

Regresión lineal con varias variables predictoras

Para aumentar la precisión en conjuntos de datos de dimensiones bajas y medianas, ajuste un modelo de regresión lineal mediante fitlm.

Para reducir el tiempo de cálculo en conjuntos de datos de altas dimensiones, ajuste un modelo de regresión lineal mediante fitrlinear.

Apps

Regression Learner

Train regression models to predict data using supervised machine learning

Objetos

`LinearModel`	Linear regression model
`CompactLinearModel`	Compact linear regression model
`RegressionLinear`	Linear regression model for high-dimensional data
`RegressionPartitionedLinear`	Cross-validated linear regression model for high-dimensional data

Funciones

expandir todo

Funciones de `LinearModel` y `CompactLinearModel`

Crear un objeto `LinearModel`

`fitlm`	Ajustar un modelo de regresión lineal
`stepwiselm`	Perform stepwise regression

Crear un objeto `CompactLinearModel`

compact Compact linear regression model

Añadir o eliminar términos de un modelo lineal

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

Predecir respuestas

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

Evaluar un modelo lineal

`anova`	Analysis of variance for linear regression model
`coefCI`	Confidence intervals of coefficient estimates of linear regression model
`coefTest`	Linear hypothesis test on linear regression model coefficients
`dwtest`	Durbin-Watson test with linear regression model object
`partialDependence`	Compute partial dependence

Visualizar un modelo lineal y estadísticas descriptivas

`plot`	Scatter plot or added variable plot of linear regression model
`plotAdded`	Added variable plot of linear regression model
`plotAdjustedResponse`	Adjusted response plot of linear regression model
`plotDiagnostics`	Plot observation diagnostics of linear regression model
`plotEffects`	Plot main effects of predictors in linear regression model
`plotInteraction`	Plot interaction effects of two predictors in linear regression model
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`plotResiduals`	Plot residuals of linear regression model
`plotSlice`	Plot of slices through fitted linear regression surface

Recopilar propiedades de un modelo lineal

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

Funciones de `RegressionLinear` y `RegressionPartitionedLinear`

Crear un objeto

fitrlinear Fit linear regression model to high-dimensional data

Trabajar con un objeto `RegressionLinear`

`predict`	Predict response of linear regression model
`lime`	Local interpretable model-agnostic explanations (LIME)
`loss`	Regression loss for linear regression models
`partialDependence`	Compute partial dependence
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`shapley`	Shapley values
`selectModels`	Select fitted regularized linear regression models

Trabajar con un objeto `RegressionPartitionedLinear`

`kfoldLoss`	Regression loss for observations not used in training
`kfoldPredict`	Predict responses for observations not used for training

Regresión lineal sin utilizar ningún objeto

Ajustar y evaluar una regresión lineal

`dwtest`	Durbin-Watson test with residual inputs
`invpred`	Inverse prediction
`linhyptest`	Linear hypothesis test
`plsregress`	Partial least-squares (PLS) regression
`regress`	Regresión lineal múltiple
`regstats`	Regression diagnostics
`relieff`	Rank importance of predictors using ReliefF or RReliefF algorithm
`robustfit`	Fit robust linear regression
`stepwisefit`	Fit linear regression model using stepwise regression

Preparar los datos

`x2fx`	Convert predictor matrix to design matrix
`dummyvar`	Create dummy variables

Herramientas interactivas

`robustdemo`	Interactive robust regression
`rsmdemo`	Interactive response surface demonstration
`rstool`	Interactive response surface modeling
`stepwise`	Interactive stepwise regression

Temas

Introducción a la regresión lineal

What Is a Linear Regression Model?
Regression models describe the relationship between a dependent variable and one or more independent variables.
Linear Regression
Fit a linear regression model and examine the result.
Stepwise Regression
In stepwise regression, predictors are automatically added to or trimmed from a model.
Reduce Outlier Effects Using Robust Regression
Fit a robust model that is less sensitive than ordinary least squares to large changes in small parts of the data.
Choose a Regression Function
Choose a regression function depending on the type of regression problem, and update legacy code using new fitting functions.
Summary of Output and Diagnostic Statistics
Evaluate a fitted model by using model properties and object functions.
Wilkinson Notation
Wilkinson notation provides a way to describe regression and repeated measures models without specifying coefficient values.

Flujos de trabajo de las regresiones lineales

Linear Regression Workflow
Import and prepare data, fit a linear regression model, test and improve its quality, and share the model.
Interpret Linear Regression Results
Display and interpret linear regression output statistics.
Linear Regression with Interaction Effects
Construct and analyze a linear regression model with interaction effects and interpret the results.
Linear Regression Using Tables
This example shows how to perform linear and stepwise regression analyses using tables.
Linear Regression with Categorical Covariates
Perform a regression with categorical covariates using categorical arrays and fitlm.
Analyze Time Series Data
This example shows how to visualize and analyze time series data using a timeseries object and the regress function.
Train Linear Regression Model
Train a linear regression model using fitlm to analyze in-memory data and out-of-memory data.

Regresión de mínimos cuadrados parciales

Partial Least Squares
Partial least squares (PLS) constructs new predictor variables as linear combinations of the original predictor variables, while considering the observed response values, leading to a parsimonious model with reliable predictive power.
Partial Least Squares Regression and Principal Components Regression
Apply partial least squares regression (PLSR) and principal components regression (PCR), and explore the effectiveness of the two methods.