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

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.

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]

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

x	Matrix for which the data is guessed.

Predict() [2/2]

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

c	Column for which the data is guessed.

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

• mitkGeneralizedLinearModel.h