To fit a multivariate linear regression model using
mvregress, you must set up your response matrix and design matrices in a particular way.
This example shows how to set up a multivariate general linear model for estimation using
This example shows how to perform panel data analysis using
This example shows how to perform longitudinal analysis using
This example shows how to apply Partial Least Squares Regression (PLSR) and Principal Components Regression (PCR), and discusses the effectiveness of the two methods.
Large, high-dimensional data sets are common in the modern era of computer-based instrumentation and electronic data storage.
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 (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.