imported>Jeremy |
imported>Mathias |
Line 1: |
Line 1: |
| '''High-Level Modeling Functions'''
| | ===Purpose=== |
| 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.
| | Outputs a sigmoid function. |
|
| |
| :[[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.
| |
| :[[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.
| |
| :[[tld]] - Trilinear decomposition.
| |
| :[[tucker]] - Analysis for n-way arrays.
| |
|
| |
|
| '''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.
| | ===Synopsis=== |
| | | |
| :[[gram]] - Generalized rank annihilation method. | | :[y,y1,y2] = peaksigmoid(x,ax) |
| :[[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.
| | ===Inputs=== |
| :[[cr]] - Continuum Regression for multivariate y.
| | |
| :[[evolvfa]] - Evolving factor analysis (forward and reverse).
| | * '''x''' = 3 element vector where |
| :[[ewfa]] - Evolving window factor analysis.
| | :* x(1) = coefficient |
| :[[glsw]] - Generalized least-squares weighting/preprocessing.
| | :* x(2) = offset |
| :[[gram]] - Generalized rank annihilation method.
| | :* x(3) = decay constant |
| :[[lwrpred]] - Predictions based on locally weighted regression models.
| | |
| :[[mlpca]] - Maximum likelihood principal components analysis.
| | ===Outputs=== |
| :[[polypls]] - PLS regression with polynomial inner-relation.
| | |
| :[[ridge]] - Ridge regression by Hoerl-Kennard-Baldwin.
| | |
| :[[wtfa]] - Window target factor analysis.
| | |
| | | |
| (Sub topic of [[Categorical_Index|Categorical_Index]]) | | <math>\sqrt{1-e^2}</math> |
| | |
| | <math>f\left( {{a}_{i}},\mathbf{x} \right)={{x}_{1}}\left[ {{x}_{4}}{{\operatorname{e}}^{\frac{-4\ln \left( 2 \right){{\left( {{a}_{i}}-{{x}_{2}} \right)}^{2}}}{x_{3}^{2}}}}+\left( 1-{{x}_{4}} \right)\left[ \frac{x_{3}^{2}}{{{\left( {{a}_{i}}-{{x}_{2}} \right)}^{2}}+x_{3}^{2}} \right] \right]</math> |
| | |
| | |
| | <math>f\left( {{a}_{i}},\mathbf{x} \right)={{x}_{1}}\left[ {{x}_{4}}{{\operatorname{e}}^{\frac{-4\ln \left( 2 \right){{\left( {{a}_{i}}-{{x}_{2}} \right)}^{2}}}{x_{3}^{2}}}}+\left( 1-{{x}_{4}} \right)\left[ \frac{1-x_{3}^{2}}{{{\left( {1}+{{x}_{2}} \right)}^{2}}+x_{3}^{2}} \right] \right]</math> |
| | |
| | |
| | |
| | <math>f\left( {{a}_{i}},\mathbf{x} \right)={{x}_{1}}\left[ {}+\left( 1-{{x}_{4}} \right)\left[ \frac{1-x_{3}^{2}}{{{\left( \right)}+x_{3}^{2}} \right] \right]</math> |
| | |
| | * '''y(1) ''' = |
| | |
| | * '''y(2) ''' = <math>{\operatorname{d}\!y\over\operatorname{d}\!{x}_{i}}</math> |
| | |
| | * ''' y(3) ''' = <math>{\operatorname{d^2}\!y\over\operatorname{d}\!{{x}_{i}}^{2}}</math> |
Purpose
Outputs a sigmoid function.
Synopsis
- [y,y1,y2] = peaksigmoid(x,ax)
Inputs
- x = 3 element vector where
- x(1) = coefficient
- x(2) = offset
- x(3) = decay constant
Outputs
Failed to parse (syntax error): {\displaystyle f\left( {{a}_{i}},\mathbf{x} \right)={{x}_{1}}\left[ {}+\left( 1-{{x}_{4}} \right)\left[ \frac{1-x_{3}^{2}}{{{\left( \right)}+x_{3}^{2}} \right] \right]}
- y(2) =
- y(3) =