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java.lang.Objectorg.apache.commons.math.stat.regression.AbstractMultipleLinearRegression
public abstract class AbstractMultipleLinearRegression
Abstract base class for implementations of MultipleLinearRegression.
Constructor Summary | |
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AbstractMultipleLinearRegression()
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Method Summary | |
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double |
estimateErrorVariance()
Estimates the variance of the error. |
double |
estimateRegressandVariance()
Returns the variance of the regressand, ie Var(y). |
double[] |
estimateRegressionParameters()
Estimates the regression parameters b. |
double[] |
estimateRegressionParametersStandardErrors()
Returns the standard errors of the regression parameters. |
double[][] |
estimateRegressionParametersVariance()
Estimates the variance of the regression parameters, ie Var(b). |
double |
estimateRegressionStandardError()
Estimates the standard error of the regression. |
double[] |
estimateResiduals()
Estimates the residuals, ie u = y - X*b. |
boolean |
isNoIntercept()
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void |
newSampleData(double[] data,
int nobs,
int nvars)
Loads model x and y sample data from a flat input array, overriding any previous sample. |
void |
setNoIntercept(boolean noIntercept)
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Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public AbstractMultipleLinearRegression()
Method Detail |
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public boolean isNoIntercept()
public void setNoIntercept(boolean noIntercept)
noIntercept
- true means the model is to be estimated without an intercept termpublic void newSampleData(double[] data, int nobs, int nvars)
Loads model x and y sample data from a flat input array, overriding any previous sample.
Assumes that rows are concatenated with y values first in each row. For example, an input
data
array containing the sequence of values (1, 2, 3, 4, 5, 6, 7, 8, 9) with
nobs = 3
and nvars = 2
creates a regression dataset with two
independent variables, as below:
y x[0] x[1] -------------- 1 2 3 4 5 6 7 8 9
Note that there is no need to add an initial unitary column (column of 1's) when
specifying a model including an intercept term. If isNoIntercept()
is true
,
the X matrix will be created without an initial column of "1"s; otherwise this column will
be added.
Throws IllegalArgumentException if any of the following preconditions fail:
data
cannot be nulldata.length = nobs * (nvars + 1)
nobs > nvars
data
- input data arraynobs
- number of observations (rows)nvars
- number of independent variables (columns, not counting y)
IllegalArgumentException
- if the preconditions are not metpublic double[] estimateRegressionParameters()
estimateRegressionParameters
in interface MultipleLinearRegression
public double[] estimateResiduals()
estimateResiduals
in interface MultipleLinearRegression
public double[][] estimateRegressionParametersVariance()
estimateRegressionParametersVariance
in interface MultipleLinearRegression
public double[] estimateRegressionParametersStandardErrors()
estimateRegressionParametersStandardErrors
in interface MultipleLinearRegression
public double estimateRegressandVariance()
estimateRegressandVariance
in interface MultipleLinearRegression
public double estimateErrorVariance()
public double estimateRegressionStandardError()
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