多元线性回归
当您需要在一个回归模型中包含多个响应变量时,请使用多元线性回归模型。多元线性回归模型将 d
维连续响应向量表示为预测变量项与服从多元正态分布的误差项组成的向量的线性组合。您可以使用 mvregress
来创建多元线性回归模型。
偏最小二乘 (PLS) 回归是一种降维方法,它使用原始预测变量的线性组合来构造新的预测变量。要拟合一个具有多个响应变量的 PLS 回归模型,请使用 plsregress
。
注意
多元线性回归模型不同于多重线性回归模型,后者将一元连续响应表示为由外生项与一个独立同分布误差项的线性组合。要拟合一个多重线性回归模型,请使用 fitlm
或 fitrlinear
。
函数
mvregress | Multivariate linear regression |
mvregresslike | Negative log-likelihood for multivariate regression |
plsregress | Partial least-squares (PLS) regression |
主题
- Set Up Multivariate Regression Problems
To fit a multivariate linear regression model using
mvregress
, you must set up your response matrix and design matrices in a particular way. - Multivariate General Linear Model
This example shows how to set up a multivariate general linear model for estimation using
mvregress
. - Fixed Effects Panel Model with Concurrent Correlation
This example shows how to perform panel data analysis using
mvregress
. - Longitudinal Analysis
This example shows how to perform longitudinal analysis using
mvregress
. - 偏最小二乘回归和主成分回归
应用偏最小二乘回归 (PLSR) 和主成分回归 (PCR),并研究这两种方法的有效性。
- Multivariate Linear Regression
Large, high-dimensional data sets are common in the modern era of computer-based instrumentation and electronic data storage.
- Estimation of Multivariate Regression Models
When you fit multivariate linear regression models using
mvregress
, you can use the optional name-value pair'algorithm','cwls'
to choose least squares estimation. - 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.