Regression can project to latent structures {adaptive least-squares} {ALS algorithm}, to put compounds in classes.
Methods {classical least-squares, drug} (CLS) can be the same as ordinary least-squares analysis.
Partial least-squares {Comparative Molecular Field Analysis} (CoMFA) can analyze grid around site atom and find grid-point electrostatic and steric interactions, to make sampled-point descriptors.
Compounds have different classes with different weights {fuzzy adaptive least-squares} (FALS).
Methods {Generating Optimal Linear PLS Estimations} (GOLPE) can use PLS and D-optimal design to select variables, and cross-validates.
Fitting methods {inverse least-squares} (ILS) can find regression line.
Methods {least-squares regression, drug} can be the same as ordinary least-squares analysis.
Methods {linear least-squares regression, drug} can be the same as ordinary least-squares analysis.
Partial least-squares methods {matrix bidiagonalization method, drug} can simplify data variance-covariance matrix.
Regression can project to latent structures {multi-block PLS}, to put compounds in classes.
Methods {multiple least-squares regression, drug} can be the same as ordinary least-squares analysis.
Methods {multiple linear regression} (MLR) can measure linear component dependence on physico-chemical or structural properties and finds descriptor coefficients.
Methods {multivariate least-squares regression, drug} can be the same as ordinary least-squares analysis.
Methods {non-least-squares} (NLS) can detect non-linear relationships.
Fitting methods {ordinary least-squares} (OLS) can find descriptor coefficients.
Methods {partial least-squares} (PLS) can use least-squares to find independent variables and dependencies among variables. It projects regression to latent structures. It maximizes latent-variable and observable covariation. It diagonalizes the matrix.
Methods {SAMPLS algorithm} can apply PLS to trend vector analysis.
5-Chemistry-Biochemistry-Drug-Activity-Methods-Regression
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Date Modified: 2022.0225