Java org.apache.commons.math3.stat.regression OLSMultipleLinearRegression fields, constructors, methods, implement or subclass

Example usage for Java org.apache.commons.math3.stat.regression OLSMultipleLinearRegression fields, constructors, methods, implement or subclass

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Introduction

In this page you can find the methods, fields and constructors for org.apache.commons.math3.stat.regression OLSMultipleLinearRegression.

The text is from its open source code.

Subclass

org.apache.commons.math3.stat.regression.OLSMultipleLinearRegression has subclasses.
Click this link to see all its subclasses.

Constructor

OLSMultipleLinearRegression

Method

doublecalculateAdjustedRSquared()

Returns the adjusted R-squared statistic, defined by the formula 

 R2adj = 1 - [SSR (n - 1)] / [SSTO (n - p)]
where SSR is the #calculateResidualSumOfSquares() sum of squared residuals , SSTO is the #calculateTotalSumOfSquares() total sum of squares , n is the number of observations and p is the number of parameters estimated (including the intercept).

If the regression is estimated without an intercept term, what is returned is 

  1 - (1 -  #calculateRSquared() ) * (n / (n - p))

doublecalculateResidualSumOfSquares()
Returns the sum of squared residuals.
doublecalculateRSquared()
Returns the R-Squared statistic, defined by the formula 
 R2 = 1 - SSR / SSTO
where SSR is the #calculateResidualSumOfSquares() sum of squared residuals and SSTO is the #calculateTotalSumOfSquares() total sum of squares
doublecalculateTotalSumOfSquares()

Returns the sum of squared deviations of Y from its mean.

If the model has no intercept term, 0 is used for the mean of Y - i.e., what is returned is the sum of the squared Y values.

The value returned by this method is the SSTO value used in the #calculateRSquared() R-squared computation.

doubleestimateRegressandVariance()
double[]estimateRegressionParameters()
double[]estimateRegressionParametersStandardErrors()
double[][]estimateRegressionParametersVariance()
doubleestimateRegressionStandardError()
Estimates the standard error of the regression.
double[]estimateResiduals()
voidnewSampleData(double[] y, double[][] x)
Loads model x and y sample data, overriding any previous sample.
voidsetNoIntercept(boolean noIntercept)