Multivariate Regression

Linear regression with a multivariate response variable

Functions

`mvregress`	Multivariate linear regression
`mvregresslike`	Negative log-likelihood for multivariate regression
`polytool`	Interactive polynomial fitting
`polyconf`	Polynomial confidence intervals
`plsregress`	Partial least-squares (PLS) regression

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.
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.

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.