In the context of Partial Least Squares Regression (PLSR) using the ‘plsregress’ command in MATLAB, X-loadings are the predictor loadings, returned as numeric matrix. XL is a p-by-ncomp matrix, where p is the number of predictor variables and ‘ncomp’ is the number of PLS components. Each row of XL contains coefficients that define a linear combination of PLS components approximating the original predictor variables.
Also, BETA (betaPLS) is the coefficient estimates for PLS regression which is returned as a numeric matrix. If the model includes the constant term (intercept), BETA is a (p + 1)-by-m matrix, where p is the number of predictor variables, m is the number of response variables, and the first row of BETA contains the constant term. And in PLSR, standardization is a common preprocessing step which needs to be done externally before ‘plsregress’ command. Moreover, it ensures that variables with large scales do not unduly influence the model.
For more information, you can refer to the documentation link below:
