|
Medical Imaging Interaction Toolkit
2016.11.0
Medical Imaging Interaction Toolkit
|
|
Medical Imaging Interaction Toolkit
2016.11.0
Medical Imaging Interaction Toolkit
|
Generalized Linear Model that allows linear models for non-gaussian data. More...
#include <mitkGeneralizedLinearModel.h>
Public Member Functions | |
| GeneralizedLinearModel (const vnl_matrix< double > &xData, const vnl_vector< double > &yData, bool addConstantColumn=true) | |
| Initialization of the GLM. The parameters needs to be passed at the beginning. More... | |
| double | Predict (const vnl_vector< double > &c) |
| Predicts the value corresponding to the given vector. More... | |
| vnl_vector< double > | Predict (const vnl_matrix< double > &x) |
| Predicts the value corresponding to the given matrix. More... | |
| vnl_vector< double > | ExpMu (const vnl_matrix< double > &x) |
| Estimation of the exponential factor for a given function. More... | |
| vnl_vector< double > | B () |
| Returns the b-Vector for the estimation. More... | |
Generalized Linear Model that allows linear models for non-gaussian data.
Generalized linear models are an extension of standard linear models that allow a different apperance of the data. This is for example usefull to calculate Logistic regressions.
Definition at line 34 of file mitkGeneralizedLinearModel.h.
| mitk::GeneralizedLinearModel::GeneralizedLinearModel | ( | const vnl_matrix< double > & | xData, |
| const vnl_vector< double > & | yData, | ||
| bool | addConstantColumn = true |
||
| ) |
Initialization of the GLM. The parameters needs to be passed at the beginning.
Constructor for a GLM. During the creation process the glm model parameter are guessed.
| xData | The input data matrix. |
| yData | The output data matrix. The values of y must meet the requirements of the link and distribution. |
| addConstantColumn | Default=True. If True an constant value is added to each row allowing a constant factor in the model. |
Definition at line 100 of file mitkGeneralizedLinearModel.cpp.
References _FinalizeBVector(), _InitMuEta(), _UpdatePermXMatrix(), mitk::LogItLinking::DLink(), mitk::LogItLinking::InverseLink(), max(), and mitk::DistSimpleBinominal::SqrtVariance().
| vnl_vector< double > mitk::GeneralizedLinearModel::B | ( | ) |
Returns the b-Vector for the estimation.
Definition at line 73 of file mitkGeneralizedLinearModel.cpp.
| vnl_vector< double > mitk::GeneralizedLinearModel::ExpMu | ( | const vnl_matrix< double > & | x | ) |
Estimation of the exponential factor for a given function.
Gives the exponential part of a link function. Only suitable for log-it models. This is especially usefull for calculating the weights for transfer learning since it is equal to the weights.
Definition at line 78 of file mitkGeneralizedLinearModel.cpp.
Referenced by mitk::LRDensityEstimation::Update(), and mitk::LRDensityEstimation::WeightsForAll().
| double mitk::GeneralizedLinearModel::Predict | ( | const vnl_vector< double > & | c | ) |
Predicts the value corresponding to the given vector.
From the learned data a guess is given depending on the provided input vector. The value depend on the b-values of the learned model as well as on the chosen link and distribution.
No input validation is done. The data and the learned model might not match!
c Column for which the data is guessed.
Definition at line 34 of file mitkGeneralizedLinearModel.cpp.
References mitk::LogItLinking::InverseLink().
Referenced by mitk::LRDensityEstimation::Predict().
| vnl_vector< double > mitk::GeneralizedLinearModel::Predict | ( | const vnl_matrix< double > & | x | ) |
Predicts the value corresponding to the given matrix.
From the learned data a guess is given depending on the provided input matrix. The value depend on the b-values of the learned model as well as on the chosen link and distribution.
No input validation is done. The data and the learned model might not match!
x Matrix for which the data is guessed.
Definition at line 51 of file mitkGeneralizedLinearModel.cpp.
References mitk::LogItLinking::InverseLink().
1.8.9.1 
