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public interface UpdatingMultipleLinearRegression
An interface for regression models allowing for dynamic updating of the data. That is, the entire data set need not be loaded into memory. As observations become available, they can be added to the regression model and an updated estimate regression statistics can be calculated.
Method Summary | |
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void |
addObservation(double[] x,
double y)
Adds one observation to the regression model. |
void |
addObservations(double[][] x,
double[] y)
Adds a series of observations to the regression model. |
void |
clear()
Clears internal buffers and resets the regression model. |
long |
getN()
Returns the number of observations added to the regression model. |
boolean |
hasIntercept()
Returns true if a constant has been included false otherwise. |
RegressionResults |
regress()
Performs a regression on data present in buffers and outputs a RegressionResults object |
RegressionResults |
regress(int[] variablesToInclude)
Performs a regression on data present in buffers including only regressors indexed in variablesToInclude and outputs a RegressionResults object |
Method Detail |
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boolean hasIntercept()
long getN()
void addObservation(double[] x, double y) throws ModelSpecificationException
x
- the independent variables which form the design matrixy
- the dependent or response variable
ModelSpecificationException
- if the length of x
does not equal
the number of independent variables in the modelvoid addObservations(double[][] x, double[] y) throws ModelSpecificationException
x
- a series of observations on the independent variablesy
- a series of observations on the dependent variable
The length of x and y must be the same
ModelSpecificationException
- if x
is not rectangular, does not match
the length of y
or does not contain sufficient data to estimate the modelvoid clear()
RegressionResults regress() throws ModelSpecificationException, NoDataException
ModelSpecificationException
- if the model is not correctly specified
NoDataException
- if there is not sufficient data in the model to
estimate the regression parametersRegressionResults regress(int[] variablesToInclude) throws ModelSpecificationException, MathIllegalArgumentException
variablesToInclude
- an array of indices of regressors to include
ModelSpecificationException
- if the model is not correctly specified
MathIllegalArgumentException
- if the variablesToInclude array is null or zero length
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