Modeling Function Overview: Difference between revisions

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:[[tld]] - Trilinear decomposition.
:[[tld]] - Trilinear decomposition.
:[[tucker]] - Analysis for n-way arrays.
:[[tucker]] - Analysis for n-way arrays.
 
:[[xgb]] - Gradient Boosted Tree Ensemble for regression using XGBoost.
:[[xgbda]] - Gradient Boosted Tree Ensemble for classification (Discriminant Analysis) using XGBoost.
'''Medium-Level Modeling Functions'''
'''Medium-Level Modeling Functions'''
The following provide functionality not generally available through another higher-level function and may be of use for certain analysis methods but also require more knowledge of Matlab and the methods involved.
The following provide functionality not generally available through another higher-level function and may be of use for certain analysis methods but also require more knowledge of Matlab and the methods involved.

Revision as of 10:40, 19 December 2018

High-Level Modeling Functions The following output a model structure (or similar) and are generally considered "high-level" modeling functions designed for relatively simple use by less-experienced users as well as expert users.

analysis - Graphical user interface for data analysis.
caltransfer - Create or apply calibration and instrument transfer models.
cluster - KNN and K-means cluster analysis with dendrograms.
cls - Classical Least Squares regression for multivariate Y.
corrspec - Resolves correlation spectroscopy maps.
frpcr - Full-ratio PCR calibration and prediction.
knn - K-nearest neighbor classifier.
lwr - Locally weighted regression for univariate Y.
mcr - Multivariate curve resolution with constraints.
mlr - Multiple Linear Regression for multivariate Y.
modelselector - Create or apply a model selector model.
mpca - Multi-way (unfold) principal components analysis.
npls - Multilinear-PLS (N-PLS) for true multi-way regression.
parafac - Parallel factor analysis for n-way arrays.
parafac2 - Parallel factor analysis for unevenly sized n-way arrays.
pca - Principal components analysis.
pcr - Principal components regression for multivariate Y.
pls - Partial least squares regression for multivariate Y.
plsda - Partial least squares discriminant analysis.
purity - Self-modeling mixture analysis method based on purity of variables or spectra.
simca - Soft Independent Method of Class Analogy.
svm - SVM Support Vector Machine for regression.
svmda - SVM Support Vector Machine for classification.
tld - Trilinear decomposition.
tucker - Analysis for n-way arrays.
xgb - Gradient Boosted Tree Ensemble for regression using XGBoost.
xgbda - Gradient Boosted Tree Ensemble for classification (Discriminant Analysis) using XGBoost.

Medium-Level Modeling Functions The following provide functionality not generally available through another higher-level function and may be of use for certain analysis methods but also require more knowledge of Matlab and the methods involved.

gram - Generalized rank annihilation method.
mlpca - Maximum likelihood principal components analysis.
coda_dw - Calculates values for the Durbin_Watson criterion of columns of data set.
comparelcms_sim_interactive - Interactive interface for COMPARELCMS.
cr - Continuum Regression for multivariate y.
evolvfa - Evolving factor analysis (forward and reverse).
ewfa - Evolving window factor analysis.
glsw - Generalized least-squares weighting/preprocessing.
gram - Generalized rank annihilation method.
lwrpred - Engine for locally weighted regression models.
mlpca - Maximum likelihood principal components analysis.
polypls - PLS regression with polynomial inner-relation.
ridge - Ridge regression by Hoerl-Kennard-Baldwin.
wtfa - Window target factor analysis.

(Sub topic of Categorical_Index)