Linear and Non Linear Regression: Difference between revisions
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:[[ann]] - Predictions based on Artificial Neural Network (ANN) regression models. | |||
:[[cls]] - Classical Least Squares regression for multivariate Y. | :[[cls]] - Classical Least Squares regression for multivariate Y. | ||
:[[cr]] - Continuum Regression for multivariate y. | :[[cr]] - Continuum Regression for multivariate y. | ||
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:[[frpcrengine]] - Engine for full-ratio PCR regression. | :[[frpcrengine]] - Engine for full-ratio PCR regression. | ||
:[[leverag]] - Calculate sample leverages. | :[[leverag]] - Calculate sample leverages. | ||
:[[lwrpred]] - | :[[lwr]] - Locally weighted regression for univariate Y. | ||
:[[lwrpred]] - Engine for locally weighted regression models. | |||
:[[mlr]] - Multiple Linear Regression for multivariate Y. | :[[mlr]] - Multiple Linear Regression for multivariate Y. | ||
:[[mlrengine]] - Multiple Linear Regression computational engine. | :[[mlrengine]] - Multiple Linear Regression computational engine. | ||
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:[[rmse]] - Calculate Root Mean Square Error. | :[[rmse]] - Calculate Root Mean Square Error. | ||
:[[simpls]] - Partial Least Squares computational engine using SIMPLS algorithm. | :[[simpls]] - Partial Least Squares computational engine using SIMPLS algorithm. | ||
:[[svm]] - SVM Support Vector Machine for regression. | |||
:[[svmda]] - SVM Support Vector Machine for classification. | |||
:[[varcapy]] - Calculate percent y-block variance captured by a PLS regression model. | :[[varcapy]] - Calculate percent y-block variance captured by a PLS regression model. | ||
:[[vip]] - Calculate Variable Importance in Projection from regression model. | :[[vip]] - Calculate Variable Importance in Projection from regression model. | ||
:[[xgb]] - Gradient Boosted Tree Ensemble for regression using XGBoost. | |||
(Sub topic of [[Categorical_Index|Categorical_Index]]) | (Sub topic of [[Categorical_Index|Categorical_Index]]) |
Latest revision as of 10:40, 19 December 2018
- ann - Predictions based on Artificial Neural Network (ANN) regression models.
- cls - Classical Least Squares regression for multivariate Y.
- cr - Continuum Regression for multivariate y.
- crcvrnd - Cross-validation for continuum regression.
- crossval - Cross-validation for decomposition and linear regression.
- fastnnls - Fast non-negative least squares.
- figmerit - Analytical figures of merit for multivariate calibration.
- frpcr - Full-ratio PCR calibration and prediction.
- frpcrengine - Engine for full-ratio PCR regression.
- leverag - Calculate sample leverages.
- lwr - Locally weighted regression for univariate Y.
- lwrpred - Engine for locally weighted regression models.
- mlr - Multiple Linear Regression for multivariate Y.
- mlrengine - Multiple Linear Regression computational engine.
- modlpred - Predictions using standard model structures.
- modlrder - Displays model info for standard model structures.
- nippls - NIPALS Partial Least Squares computational engine.
- pcr - Principal components regression for multivariate Y.
- pcrengine - Principal Component Regression computational engine.
- pls - Partial least squares regression for multivariate Y.
- plsnipal - NIPALS algorithm for one PLS latent variable.
- polypls - PLS regression with polynomial inner-relation.
- regcon - Converts regression model to y = ax + b form.
- ridge - Ridge regression by Hoerl-Kennard-Baldwin.
- ridgecv - Ridge regression by cross validation.
- rinverse - Calculate pseudo inverse for PLS, PCR and RR models.
- rmse - Calculate Root Mean Square Error.
- simpls - Partial Least Squares computational engine using SIMPLS algorithm.
- svm - SVM Support Vector Machine for regression.
- svmda - SVM Support Vector Machine for classification.
- varcapy - Calculate percent y-block variance captured by a PLS regression model.
- vip - Calculate Variable Importance in Projection from regression model.
- xgb - Gradient Boosted Tree Ensemble for regression using XGBoost.
(Sub topic of Categorical_Index)