Medical Imaging Interaction Toolkit  2022.04.99-01b95b17
Medical Imaging Interaction Toolkit
mitk::GeneralizedLinearModel Class Reference

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...
 

Detailed Description

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 30 of file mitkGeneralizedLinearModel.h.

Constructor & Destructor Documentation

◆ GeneralizedLinearModel()

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.

Parameters
xDataThe input data matrix.
yDataThe output data matrix. The values of y must meet the requirements of the link and distribution.
addConstantColumnDefault=True. If True an constant value is added to each row allowing a constant factor in the model.

Member Function Documentation

◆ B()

vnl_vector<double> mitk::GeneralizedLinearModel::B ( )

Returns the b-Vector for the estimation.

◆ ExpMu()

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.

◆ Predict() [1/2]

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!

Parameters
cColumn for which the data is guessed.

◆ Predict() [2/2]

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!

Parameters
xMatrix for which the data is guessed.

The documentation for this class was generated from the following file: