Linear |
The LinearRegression type exposes the following members.
Name | Description | |
---|---|---|
LinearRegression | Default constructor. Constructs a LinearRegression instance with all sizes equal to zero. | |
LinearRegression(DataFrame, IDFColumn) | Constructs a LinearRegression instance with the specifed regresssion data and observation column. By default, the model parameter values are computed using a QR factorization. | |
LinearRegression(DataFrame, Int32) | Constructs a LinearRegression instance with the specifed regresssion data and an index to the observation column. By default, the model parameter values are computed using a QR factorization. | |
LinearRegression(DoubleMatrix, DoubleVector) | Constructs a LinearRegression instance with the specifed regresssion matrix and observation vector. By default, the model parameter values are computed using a QR factorization. | |
LinearRegression(DataFrame, IDFColumn, IRegressionCalculation) | Constructs a LinearRegression instance with the specifed regresssion data and observation column. Model parameter values are computed using the specified regression calculator. | |
LinearRegression(DataFrame, IDFColumn, Boolean) | Constructs a LinearRegression instance with the specifed regresssion data and observation column, optionally adding an intercept parameter. By default, the model parameter values are computed using a QR factorization. | |
LinearRegression(DataFrame, Int32, IRegressionCalculation) | Constructs a LinearRegression instance with the specifed regresssion data and an index to the observation column. Model parameter values are computed using the specified regression calculator. | |
LinearRegression(DataFrame, Int32, Boolean) | Constructs a LinearRegression instance with the specifed regresssion data and an index to the observation column, optionally adding an intercept parameter. By default, the model parameter values are computed using a QR factorization. | |
LinearRegression(DoubleMatrix, DoubleVector, IRegressionCalculation) | Constructs a LinearRegression instance with the specifed regresssion matrix and observation vector. Model parameter values are computed using the specified regression calculator. | |
LinearRegression(DoubleMatrix, DoubleVector, Boolean) | Constructs a LinearRegression instance with the specifed regresssion matrix and observation vector, optionally adding an intercept parameter. By default, the model parameter values are computed using a QR factorization. | |
LinearRegression(DataFrame, IDFColumn, Boolean, IRegressionCalculation) | Constructs a LinearRegression instance with the specifed regresssion data and observation column, optionally adding an intercept parameter. The model parameter values are computed using the specified regression calculator. | |
LinearRegression(DataFrame, Int32, Boolean, IRegressionCalculation) | Constructs a LinearRegression instance with the specifed regresssion data and an index to the observation column, optionally adding an intercept parameter. The model parameter values are computed using the specified regression calculator. | |
LinearRegression(DoubleMatrix, DoubleVector, Boolean, IRegressionCalculation) | Constructs a LinearRegression instance with the specifed regresssion matrix and observation vector, optionally adding an intercept parameter. The model parameter values are computed using the specified regression calculator. |
Name | Description | |
---|---|---|
CheckData | Gets and sets a boolean indicating whether or not to check input data for non-numeric values. If input data is large checking all values for NaN's and infinities can be a performance consideration. | |
ColumnResizeIncrement |
Gets and sets the amount by which the regression matrix is resized
if columns are added.
(Inherited from RegressionBase) | |
CovarianceMatrix | Gets the covariance matrix. | |
HasInterceptParameter |
Returns true if the model has an intercept parameter; otherwise,
false.
(Inherited from RegressionBase) | |
Intercept |
Gets the intercept.
(Inherited from RegressionBase) | |
IsGood |
Returns true if the model parameters were successfuly computed;
otherwise, false.
(Inherited from RegressionBase) | |
NumberOfObservations |
Gets the number of observations.
(Inherited from RegressionBase) | |
NumberOfParameters |
Gets the number of parameters in the model.
(Inherited from RegressionBase) | |
NumberOfPredictors |
Gets the number of predictors.
(Inherited from RegressionBase) | |
Observations |
Gets the vector of observations.
(Inherited from RegressionBase) | |
ParameterCalculationErrorMessage |
Gets the error message associated with a failed parameter calculation.
(Inherited from RegressionBase) | |
ParameterEstimates | Gets an array of parameter objects which may be used to perform hypothesis tests on individual parameters in the model. | |
Parameters |
Gets the computed model parameters.
(Inherited from RegressionBase) | |
PredictorMatrix |
Gets the predictor matrix.
(Inherited from RegressionBase) | |
RegressionCalculator | Gets and sets the regression calculation object used for computing the model parameters. | |
RegressionMatrix |
Gets the regression matrix.
(Inherited from RegressionBase) | |
Residuals | Get the vector of residuals. | |
RowResizeIncrement |
Gets and sets the amount by which the regression matrix is resized
if rows are added.
(Inherited from RegressionBase) | |
Variance | Gets an estimate of the variance. |
Name | Description | |
---|---|---|
AddInterceptParameter |
Adds an intercept parameter to the model and recalculates the model parameters.
(Inherited from RegressionBase) | |
AddObservation |
Adds the given observation to the model, and recalculates the model parameters.
(Inherited from RegressionBase) | |
AddObservations |
Adds the given observations to the model, and recalculates the model parameters.
(Inherited from RegressionBase) | |
AddPredictor |
Adds a predictor to the model, and recalculates the model parameters.
(Inherited from RegressionBase) | |
AddPredictors |
Adds predictors to the model, and recalculates the model parameters.
(Inherited from RegressionBase) | |
Clone | Creates a deep copy of this LinearRegression. | |
GetStandardizedResiduals | Returns the standardized residuals (also known as the internally studentized residuals). | |
GetStudentizedResiduals | Returns the (externally) studentized residuals. | |
PredictedObservation | Returns the value of the dependent variable predicted by the model for the given set of predictor values. | |
PredictedObservations | Returns the values of the dependent variable predicted by the model for the given sets of predictor values. | |
PredictionInterval | Returns a confidence interval for the value of the dependent variable predicted by the model for the given set of predictor values. | |
RecalculateParameters |
Recalculates the model parameters.
(Overrides RegressionBaseRecalculateParameters) | |
RemoveInterceptParameter |
Removes the intercept parameter from the model, and recalculates the model parameters.
(Inherited from RegressionBase) | |
RemoveObservation |
Removes the row at the indicated index from the predictor matrix and the
corresponding element from the observation vector, and recalculates the model
parameters.
(Inherited from RegressionBase) | |
RemoveObservations |
Removes the specified rows from the predictor matrix, and recalculates the model
parameters.
(Inherited from RegressionBase) | |
RemovePredictor |
Removes the specified predictor from the model, and recalculates the
model parameters.
(Inherited from RegressionBase) | |
RemovePredictors |
Removes the specified predictors from the model, and recalculates the model
parameters.
(Inherited from RegressionBase) | |
SetRegressionData(DataFrame, IDFColumn, Boolean) | Sets the regression matrix, observation vector, and intercept option to the specified values, and recalculates the model parameters. | |
SetRegressionData(DoubleMatrix, DoubleVector, Boolean) | Sets the regression matrix, observation vector, and intercept option to the specified values, and recalculates the model parameters. | |
VarianceInflationFactor | An index which measures how much the variance of a coefficient (square of the standard deviation) is increased because of collinearity. | |
VarianceInflationFactors | An index which measures how much the variance of a coefficient (square of the standard deviation) is increased because of collinearity. |
Name | Description | |
---|---|---|
colResizeIncrement_ |
Number of columns to add when adding variables (if needed).
(Inherited from RegressionBase) | |
DEFAULT_REGRESSION_CALCULATION | Default regression calculation is QRRegressionCalculation. | |
errorMessage_ |
Explains errors, if any.
(Inherited from RegressionBase) | |
hasIntercept_ |
Does the model have an intercept parameter?
(Inherited from RegressionBase) | |
isGood_ |
Is the regression good?
(Inherited from RegressionBase) | |
observationData_ |
Full set of observations.
(Inherited from RegressionBase) | |
observations_ |
Subvector of the observation data used in the current regression model.
observations_ = observationData_[regMatRowSlice_].
(Inherited from RegressionBase) | |
parameters_ |
Model parameters.
(Inherited from RegressionBase) | |
regMatColSlice_ |
regressionMatrx_ = regressionData_[regMatRowSlice_, regMatColSlice_]
(Inherited from RegressionBase) | |
regMatRowSlice_ |
regressionMatrx_ = regressionData_[regMatRowSlice_, regMatColSlice_]
(Inherited from RegressionBase) | |
regressionData_ |
The full set of regression data.
(Inherited from RegressionBase) | |
regressionMatrix_ |
A submatrix of the regression used in this regression
model.
regressionMatrx_ = regressionData_[regMatRowSlice_, regMatColSlice_]
(Inherited from RegressionBase) | |
rowResizeIncrement_ |
Number of rows to add when adding observations (if needed).
(Inherited from RegressionBase) |