Apache Commons Math 3.6.1 · org.apache.commons.math3.stat.regression.SimpleRegression

Modifier and Type	Field and Description
private final boolean	hasIntercept include an intercept or not
private long	n number of observations
private static final long	serialVersionUID Serializable version identifier
private double	sumX sum of x values
private double	sumXX total variation in x (sum of squared deviations from xbar)
private double	sumXY sum of products
private double	sumY sum of y values
private double	sumYY total variation in y (sum of squared deviations from ybar)
private double	xbar mean of accumulated x values, used in updating formulas
private double	ybar mean of accumulated y values, used in updating formulas

Access	Constructor and Description
public	SimpleRegression() Create an empty SimpleRegression instance
public	SimpleRegression(boolean whether or not to include an intercept term in the regression model includeIntercept) Create a SimpleRegression instance, specifying whether or not to estimate an intercept.

Method Summary

Modifier and Type	Method and Description
public void	addData(final double independent variable value x, final double dependent variable value y) Adds the observation (x,y) to the regression data set.
public void	addData(final double[][] array of observations to be added data) Adds the observations represented by the elements in `data`.
public void	addObservation(final double[] the independent variables which form the design matrix x, final double the dependent or response variable y) Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.addObservation. Adds one observation to the regression model.
public void	addObservations(final double[][] a series of observations on the independent variables x, final double[] a series of observations on the dependent variable The length of x and y must be the same y) Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.addObservations. Adds a series of observations to the regression model.
public void	append(SimpleRegression model to append data from reg) Appends data from another regression calculation to this one.
public void	clear() Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.clear. Clears all data from the model.
public double	Returns: the intercept of the regression line if the model includes an intercept; 0 otherwise getIntercept() Returns the intercept of the estimated regression line, if `hasIntercept()` is true; otherwise 0.
private double	Returns: the intercept of the regression line getIntercept(final double current slope slope) Returns the intercept of the estimated regression line, given the slope.
public double	Returns: standard error associated with intercept estimate getInterceptStdErr() Returns the standard error of the intercept estimate, usually denoted s(b0).
public double	Returns: sum of squared deviations of y values getMeanSquareError() Returns the sum of squared errors divided by the degrees of freedom, usually abbreviated MSE.
public long	Returns: n number of observations that have been added. getN() Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.getN. Returns the number of observations that have been added to the model.
public double	Returns: Pearson's r getR() Returns Pearson's product moment correlation coefficient, usually denoted r. Preconditions: At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, `Double,NaN` is returned.
public double	Returns: sum of squared deviations of predicted y values getRegressionSumSquares() Returns the sum of squared deviations of the predicted y values about their mean (which equals the mean of y).
private double	Returns: sum of squared deviations of predicted y values getRegressionSumSquares(final double regression slope estimate slope) Computes SSR from b1.
public double	Returns: r-square getRSquare() Returns the coefficient of determination, usually denoted r-square.
public double	Returns: significance level for slope/correlation getSignificance() Returns the significance level of the slope (equiv) correlation.
public double	Returns: the slope of the regression line getSlope() Returns the slope of the estimated regression line.
public double	Returns: half-width of 95% confidence interval for the slope estimate getSlopeConfidenceInterval() Returns the half-width of a 95% confidence interval for the slope estimate.
public double	Returns: half-width of 95% confidence interval for the slope estimate getSlopeConfidenceInterval(final double the desired significance level alpha) Returns the half-width of a (100-100*alpha)% confidence interval for the slope estimate.
public double	Returns: standard error associated with slope estimate getSlopeStdErr() Returns the standard error of the slope estimate, usually denoted s(b1).
public double	Returns: sum of cross products getSumOfCrossProducts() Returns the sum of crossproducts, x_i*y_i.
public double	Returns: sum of squared errors associated with the regression model getSumSquaredErrors() Returns the sum of squared errors (SSE) associated with the regression model.
public double	Returns: sum of squared deviations of y values getTotalSumSquares() Returns the sum of squared deviations of the y values about their mean.
public double	Returns: sum of squared deviations of x values getXSumSquares() Returns the sum of squared deviations of the x values about their mean.
public boolean	Returns: true if the regression includes an intercept; false otherwise hasIntercept() Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.hasIntercept. Returns true if the model includes an intercept term.
public double	Returns: predicted `y` value predict(final double input `x` value x) Returns the "predicted" `y` value associated with the supplied `x` value, based on the data that has been added to the model when this method is activated.
public RegressionResults	Returns: RegressionResults acts as a container of regression output regress() Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.regress. Performs a regression on data present in buffers and outputs a RegressionResults object.
public RegressionResults	Returns: RegressionResults acts as a container of regression output regress(int[] an array of indices of regressors to include variablesToInclude) Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.regress. Performs a regression on data present in buffers including only regressors indexed in variablesToInclude and outputs a RegressionResults object
public void	removeData(final double independent variable value x, final double dependent variable value y) Removes the observation (x,y) from the regression data set.
public void	removeData(double[][] array of observations to be removed data) Removes observations represented by the elements in `data`.

Inherited from java.lang.Object:: clone equals finalize getClass hashCode notify notifyAll toString wait wait wait

hasIntercept	back to summary
private final boolean hasIntercept include an intercept or not

n	back to summary
private long n number of observations

serialVersionUID	back to summary
private static final long serialVersionUID Serializable version identifier

sumX	back to summary
private double sumX sum of x values

sumXX	back to summary
private double sumXX total variation in x (sum of squared deviations from xbar)

sumXY	back to summary
private double sumXY sum of products

sumY	back to summary
private double sumY sum of y values

sumYY	back to summary
private double sumYY total variation in y (sum of squared deviations from ybar)

xbar	back to summary
private double xbar mean of accumulated x values, used in updating formulas

ybar	back to summary
private double ybar mean of accumulated y values, used in updating formulas

SimpleRegression	back to summary
public SimpleRegression() Create an empty SimpleRegression instance

SimpleRegression	back to summary
public SimpleRegression(boolean includeIntercept) Create a SimpleRegression instance, specifying whether or not to estimate an intercept. Use `false` to estimate a model with no intercept. When the `hasIntercept` property is false, the model is estimated without a constant term and `getIntercept()` returns `0`. Parameters includeIntercept:boolean whether or not to include an intercept term in the regression model

Method Detail

addData	back to summary
public void addData(final double x, final double y) Adds the observation (x,y) to the regression data set. Uses updating formulas for means and sums of squares defined in "Algorithms for Computing the Sample Variance: Analysis and Recommendations", Chan, T.F., Golub, G.H., and LeVeque, R.J. 1983, American Statistician, vol. 37, pp. 242-247, referenced in Weisberg, S. "Applied Linear Regression". 2nd Ed. 1985. Parameters x:double independent variable value y:double dependent variable value

addData

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public void addData(final double x, final double y)

Adds the observation (x,y) to the regression data set.

Uses updating formulas for means and sums of squares defined in "Algorithms for Computing the Sample Variance: Analysis and Recommendations", Chan, T.F., Golub, G.H., and LeVeque, R.J. 1983, American Statistician, vol. 37, pp. 242-247, referenced in Weisberg, S. "Applied Linear Regression". 2nd Ed. 1985.

Parameters

x:double: independent variable value
y:double: dependent variable value

addData back to summary

addData	back to summary
public void addData(final double[][] data) throws ModelSpecificationException Adds the observations represented by the elements in `data`. `(data[0][0],data[0][1])` will be the first observation, then `(data[1][0],data[1][1])`, etc. This method does not replace data that has already been added. The observations represented by `data` are added to the existing dataset. To replace all data, use `clear()` before adding the new data. Parameters data:double[][] array of observations to be added Exceptions ModelSpecificationException: if the length of `data[i]` is not greater than or equal to 2

public void addData(final double[][] data) throws ModelSpecificationException

Adds the observations represented by the elements in data.

(data[0][0],data[0][1]) will be the first observation, then (data[1][0],data[1][1]), etc.

This method does not replace data that has already been added. The observations represented by data are added to the existing dataset.

To replace all data, use clear() before adding the new data.

Parameters

data:double[][]: array of observations to be added

Exceptions

ModelSpecificationException:: if the length of data[i] is not greater than or equal to 2

addObservation back to summary

addObservation	back to summary
public void addObservation(final double[] x, final double y) throws ModelSpecificationException Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.addObservation. Adds one observation to the regression model. Parameters x:double[] the independent variables which form the design matrix y:double the dependent or response variable Exceptions ModelSpecificationException: if the length of `x` does not equal the number of independent variables in the model

public void addObservation(final double[] x, final double y) throws ModelSpecificationException

Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.addObservation.

Adds one observation to the regression model.

Parameters

x:double[]: the independent variables which form the design matrix
y:double: the dependent or response variable

Exceptions

ModelSpecificationException:: if the length of x does not equal the number of independent variables in the model

addObservations back to summary

addObservations	back to summary
public void addObservations(final double[][] x, final double[] y) throws ModelSpecificationException Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.addObservations. Adds a series of observations to the regression model. The lengths of x and y must be the same and x must be rectangular. Parameters x:double[][] a series of observations on the independent variables y:double[] a series of observations on the dependent variable The length of x and y must be the same Exceptions ModelSpecificationException: if `x` is not rectangular, does not match the length of `y` or does not contain sufficient data to estimate the model

public void addObservations(final double[][] x, final double[] y) throws ModelSpecificationException

Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.addObservations.

Adds a series of observations to the regression model. The lengths of x and y must be the same and x must be rectangular.

Parameters

x:double[][]: a series of observations on the independent variables
y:double[]: a series of observations on the dependent variable The length of x and y must be the same

Exceptions

ModelSpecificationException:: if x is not rectangular, does not match the length of y or does not contain sufficient data to estimate the model

append	back to summary
public void append(SimpleRegression reg) Appends data from another regression calculation to this one. The mean update formulae are based on a paper written by Philippe Pébay: Formulas for Robust, One-Pass Parallel Computation of Covariances and Arbitrary-Order Statistical Moments, 2008, Technical Report SAND2008-6212, Sandia National Laboratories. Parameters reg:SimpleRegression model to append data from Since 3.3

append

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public void append(SimpleRegression reg)

Appends data from another regression calculation to this one.

The mean update formulae are based on a paper written by Philippe Pébay: Formulas for Robust, One-Pass Parallel Computation of Covariances and Arbitrary-Order Statistical Moments, 2008, Technical Report SAND2008-6212, Sandia National Laboratories.

Parameters

reg:SimpleRegression: model to append data from

Since

3.3

clear	back to summary
public void clear() Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.clear. Clears all data from the model.

clear

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public void clear()

Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.clear.

Clears all data from the model.

getIntercept back to summary

getIntercept	back to summary
public double getIntercept() Returns the intercept of the estimated regression line, if `hasIntercept()` is true; otherwise 0. The least squares estimate of the intercept is computed using the normal equations. The intercept is sometimes denoted b0. Preconditions: At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, `Double,NaN` is returned. Returns:double the intercept of the regression line if the model includes an intercept; 0 otherwise See Also `SimpleRegression(boolean)`

public double getIntercept()

Returns the intercept of the estimated regression line, if hasIntercept() is true; otherwise 0.

The least squares estimate of the intercept is computed using the normal equations. The intercept is sometimes denoted b0.

Preconditions:

At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, Double,NaN is returned.

Returns:double: the intercept of the regression line if the model includes an intercept; 0 otherwise
See Also: SimpleRegression(boolean)

getIntercept back to summary

getIntercept	back to summary
private double getIntercept(final double slope) Returns the intercept of the estimated regression line, given the slope. Will return `NaN` if slope is `NaN`. Parameters slope:double current slope Returns:double the intercept of the regression line

private double getIntercept(final double slope)

Returns the intercept of the estimated regression line, given the slope.

Will return NaN if slope is NaN.

Parameters

slope:double: current slope

Returns:double

the intercept of the regression line

getInterceptStdErr back to summary

getInterceptStdErr	back to summary
public double getInterceptStdErr() Returns the standard error of the intercept estimate, usually denoted s(b0). If there are fewer that three observations in the model, or if there is no variation in x, this returns `Double.NaN`. Additionally, a `Double.NaN` is returned when the intercept is constrained to be zero Returns:double standard error associated with intercept estimate

public double getInterceptStdErr()

Returns the standard error of the intercept estimate, usually denoted s(b0).

If there are fewer that three observations in the model, or if there is no variation in x, this returns Double.NaN.

Additionally, a Double.NaN is returned when the intercept is constrained to be zero

Returns:double: standard error associated with intercept estimate

getMeanSquareError back to summary

getMeanSquareError	back to summary
public double getMeanSquareError() Returns the sum of squared errors divided by the degrees of freedom, usually abbreviated MSE. If there are fewer than three data pairs in the model, or if there is no variation in `x`, this returns `Double.NaN`. Returns:double sum of squared deviations of y values

public double getMeanSquareError()

Returns the sum of squared errors divided by the degrees of freedom, usually abbreviated MSE.

If there are fewer than three data pairs in the model, or if there is no variation in x, this returns Double.NaN.

Returns:double: sum of squared deviations of y values

getN	back to summary
public long getN() Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.getN. Returns the number of observations that have been added to the model. Returns:long n number of observations that have been added.

getN

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public long getN()

Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.getN.

Returns the number of observations that have been added to the model.

Returns:long: n number of observations that have been added.

getR back to summary

getR	back to summary
public double getR() Returns Pearson's product moment correlation coefficient, usually denoted r. Preconditions: At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, `Double,NaN` is returned. Returns:double Pearson's r

public double getR()

Returns Pearson's product moment correlation coefficient, usually denoted r.

Preconditions:

At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, Double,NaN is returned.

Returns:double: Pearson's r

getRegressionSumSquares back to summary

getRegressionSumSquares	back to summary
public double getRegressionSumSquares() Returns the sum of squared deviations of the predicted y values about their mean (which equals the mean of y). This is usually abbreviated SSR or SSM. It is defined as SSM here Preconditions: At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, `Double.NaN` is returned. Returns:double sum of squared deviations of predicted y values

public double getRegressionSumSquares()

Returns the sum of squared deviations of the predicted y values about their mean (which equals the mean of y).

This is usually abbreviated SSR or SSM. It is defined as SSM here

Preconditions:

At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, Double.NaN is returned.

Returns:double: sum of squared deviations of predicted y values

getRegressionSumSquares	back to summary
private double getRegressionSumSquares(final double slope) Computes SSR from b1. Parameters slope:double regression slope estimate Returns:double sum of squared deviations of predicted y values

getRegressionSumSquares

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private double getRegressionSumSquares(final double slope)

Computes SSR from b1.

Parameters

slope:double: regression slope estimate

Returns:double

sum of squared deviations of predicted y values

getRSquare back to summary

getRSquare	back to summary
public double getRSquare() Returns the coefficient of determination, usually denoted r-square. Preconditions: At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, `Double,NaN` is returned. Returns:double r-square

public double getRSquare()

Returns the coefficient of determination, usually denoted r-square.

Preconditions:

At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, Double,NaN is returned.

Returns:double: r-square

getSignificance back to summary

getSignificance	back to summary
public double getSignificance() Returns the significance level of the slope (equiv) correlation. Specifically, the returned value is the smallest `alpha` such that the slope confidence interval with significance level equal to `alpha` does not include `0`. On regression output, this is often denoted `Prob(\|t\| > 0)` Usage Note: The validity of this statistic depends on the assumption that the observations included in the model are drawn from a Bivariate Normal Distribution. If there are fewer that three observations in the model, or if there is no variation in x, this returns `Double.NaN`. Returns:double significance level for slope/correlation Exceptions MaxCountExceededException: if the significance level can not be computed.

public double getSignificance()

Returns the significance level of the slope (equiv) correlation.

Specifically, the returned value is the smallest alpha such that the slope confidence interval with significance level equal to alpha does not include 0. On regression output, this is often denoted Prob(|t| > 0)

Usage Note:
The validity of this statistic depends on the assumption that the observations included in the model are drawn from a Bivariate Normal Distribution.

If there are fewer that three observations in the model, or if there is no variation in x, this returns Double.NaN.

Returns:double

significance level for slope/correlation

Exceptions

MaxCountExceededException:: if the significance level can not be computed.

getSlope back to summary

getSlope	back to summary
public double getSlope() Returns the slope of the estimated regression line. The least squares estimate of the slope is computed using the normal equations. The slope is sometimes denoted b1. Preconditions: At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, `Double.NaN` is returned. Returns:double the slope of the regression line

public double getSlope()

Returns the slope of the estimated regression line.

The least squares estimate of the slope is computed using the normal equations. The slope is sometimes denoted b1.

Preconditions:

At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, Double.NaN is returned.

Returns:double: the slope of the regression line

getSlopeConfidenceInterval back to summary

getSlopeConfidenceInterval	back to summary
public double getSlopeConfidenceInterval() throws OutOfRangeException Returns the half-width of a 95% confidence interval for the slope estimate. The 95% confidence interval is `(getSlope() - getSlopeConfidenceInterval(), getSlope() + getSlopeConfidenceInterval())` If there are fewer that three observations in the model, or if there is no variation in x, this returns `Double.NaN`. Usage Note: The validity of this statistic depends on the assumption that the observations included in the model are drawn from a Bivariate Normal Distribution. Returns:double half-width of 95% confidence interval for the slope estimate Exceptions OutOfRangeException: if the confidence interval can not be computed.

public double getSlopeConfidenceInterval() throws OutOfRangeException

Returns the half-width of a 95% confidence interval for the slope estimate.

The 95% confidence interval is

(getSlope() - getSlopeConfidenceInterval(), getSlope() + getSlopeConfidenceInterval())

If there are fewer that three observations in the model, or if there is no variation in x, this returns Double.NaN.

Usage Note:
The validity of this statistic depends on the assumption that the observations included in the model are drawn from a Bivariate Normal Distribution.

Returns:double

half-width of 95% confidence interval for the slope estimate

Exceptions

OutOfRangeException:: if the confidence interval can not be computed.

getSlopeConfidenceInterval back to summary

getSlopeConfidenceInterval	back to summary
public double getSlopeConfidenceInterval(final double alpha) throws OutOfRangeException Returns the half-width of a (100-100alpha)% confidence interval for the slope estimate. The (100-100alpha)% confidence interval is `(getSlope() - getSlopeConfidenceInterval(), getSlope() + getSlopeConfidenceInterval())` To request, for example, a 99% confidence interval, use `alpha = .01` Usage Note: The validity of this statistic depends on the assumption that the observations included in the model are drawn from a Bivariate Normal Distribution. Preconditions: If there are fewer that three observations in the model, or if there is no variation in x, this returns `Double.NaN`. `(0 < alpha < 1)`; otherwise an `OutOfRangeException` is thrown. Parameters alpha:double the desired significance level Returns:double half-width of 95% confidence interval for the slope estimate Exceptions OutOfRangeException: if the confidence interval can not be computed.

public double getSlopeConfidenceInterval(final double alpha) throws OutOfRangeException

Returns the half-width of a (100-100*alpha)% confidence interval for the slope estimate.

The (100-100*alpha)% confidence interval is

(getSlope() - getSlopeConfidenceInterval(), getSlope() + getSlopeConfidenceInterval())

To request, for example, a 99% confidence interval, use alpha = .01

Usage Note:
The validity of this statistic depends on the assumption that the observations included in the model are drawn from a Bivariate Normal Distribution.

Preconditions:

If there are fewer that three observations in the model, or if there is no variation in x, this returns Double.NaN.
(0 < alpha < 1); otherwise an OutOfRangeException is thrown.

Parameters

alpha:double: the desired significance level

Returns:double

half-width of 95% confidence interval for the slope estimate

Exceptions

OutOfRangeException:: if the confidence interval can not be computed.

getSlopeStdErr back to summary

getSlopeStdErr	back to summary
public double getSlopeStdErr() Returns the standard error of the slope estimate, usually denoted s(b1). If there are fewer that three data pairs in the model, or if there is no variation in x, this returns `Double.NaN`. Returns:double standard error associated with slope estimate

public double getSlopeStdErr()

Returns the standard error of the slope estimate, usually denoted s(b1).

If there are fewer that three data pairs in the model, or if there is no variation in x, this returns Double.NaN.

Returns:double: standard error associated with slope estimate

getSumOfCrossProducts	back to summary
public double getSumOfCrossProducts() Returns the sum of crossproducts, x_i*y_i. Returns:double sum of cross products

getSumOfCrossProducts

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public double getSumOfCrossProducts()

Returns the sum of crossproducts, x_i*y_i.

Returns:double: sum of cross products

getSumSquaredErrors back to summary

getSumSquaredErrors	back to summary
public double getSumSquaredErrors() Returns the sum of squared errors (SSE) associated with the regression model. The sum is computed using the computational formula `SSE = SYY - (SXY * SXY / SXX)` where `SYY` is the sum of the squared deviations of the y values about their mean, `SXX` is similarly defined and `SXY` is the sum of the products of x and y mean deviations. The sums are accumulated using the updating algorithm referenced in `addData`. The return value is constrained to be non-negative - i.e., if due to rounding errors the computational formula returns a negative result, 0 is returned. Preconditions: At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, `Double,NaN` is returned. Returns:double sum of squared errors associated with the regression model

public double getSumSquaredErrors()

Returns the sum of squared errors (SSE) associated with the regression model.

The sum is computed using the computational formula

SSE = SYY - (SXY * SXY / SXX)

where SYY is the sum of the squared deviations of the y values about their mean, SXX is similarly defined and SXY is the sum of the products of x and y mean deviations.

The sums are accumulated using the updating algorithm referenced in addData.

The return value is constrained to be non-negative - i.e., if due to rounding errors the computational formula returns a negative result, 0 is returned.

Preconditions:

At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, Double,NaN is returned.

Returns:double: sum of squared errors associated with the regression model

getTotalSumSquares back to summary

getTotalSumSquares	back to summary
public double getTotalSumSquares() Returns the sum of squared deviations of the y values about their mean. This is defined as SSTO here. If `n < 2`, this returns `Double.NaN`. Returns:double sum of squared deviations of y values

public double getTotalSumSquares()

Returns the sum of squared deviations of the y values about their mean.

This is defined as SSTO here.

If n < 2, this returns Double.NaN.

Returns:double: sum of squared deviations of y values

getXSumSquares back to summary

getXSumSquares	back to summary
public double getXSumSquares() Returns the sum of squared deviations of the x values about their mean. If `n < 2`, this returns `Double.NaN`. Returns:double sum of squared deviations of x values

public double getXSumSquares()

Returns the sum of squared deviations of the x values about their mean. If n < 2, this returns Double.NaN.

Returns:double: sum of squared deviations of x values

hasIntercept back to summary

hasIntercept	back to summary
public boolean hasIntercept() Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.hasIntercept. Returns true if the model includes an intercept term. Returns:boolean true if the regression includes an intercept; false otherwise See Also `SimpleRegression(boolean)`

public boolean hasIntercept()

Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.hasIntercept.

Returns true if the model includes an intercept term.

Returns:boolean: true if the regression includes an intercept; false otherwise
See Also: SimpleRegression(boolean)

predict back to summary

predict	back to summary
public double predict(final double x) Returns the "predicted" `y` value associated with the supplied `x` value, based on the data that has been added to the model when this method is activated. `predict(x) = intercept + slope * x` Preconditions: At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, `Double,NaN` is returned. Parameters x:double input `x` value Returns:double predicted `y` value

public double predict(final double x)

Returns the "predicted" y value associated with the supplied x value, based on the data that has been added to the model when this method is activated.

predict(x) = intercept + slope * x

Preconditions:

At least two observations (with at least two different x values) must have been added before invoking this method. If this method is invoked before a model can be estimated, Double,NaN is returned.

Parameters

x:double: input x value

Returns:double

predicted y value

regress back to summary

regress	back to summary
public RegressionResults regress() throws ModelSpecificationException, NoDataException Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.regress. Performs a regression on data present in buffers and outputs a RegressionResults object. If there are fewer than 3 observations in the model and `hasIntercept` is true a `NoDataException` is thrown. If there is no intercept term, the model must contain at least 2 observations. Returns:RegressionResults RegressionResults acts as a container of regression output Exceptions ModelSpecificationException: if the model is not correctly specified NoDataException: if there is not sufficient data in the model to estimate the regression parameters

public RegressionResults regress() throws ModelSpecificationException, NoDataException

Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.regress.

Performs a regression on data present in buffers and outputs a RegressionResults object.

If there are fewer than 3 observations in the model and hasIntercept is true a NoDataException is thrown. If there is no intercept term, the model must contain at least 2 observations.

Returns:RegressionResults

RegressionResults acts as a container of regression output

Exceptions

ModelSpecificationException:: if the model is not correctly specified
NoDataException:: if there is not sufficient data in the model to estimate the regression parameters

regress	back to summary
public RegressionResults regress(int[] variablesToInclude) throws MathIllegalArgumentException Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.regress. Performs a regression on data present in buffers including only regressors indexed in variablesToInclude and outputs a RegressionResults object Parameters variablesToInclude:int[] an array of indices of regressors to include Returns:RegressionResults RegressionResults acts as a container of regression output Exceptions MathIllegalArgumentException: if the variablesToInclude array is null or zero length OutOfRangeException: if a requested variable is not present in model

regress

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public RegressionResults regress(int[] variablesToInclude) throws MathIllegalArgumentException

Implements org.apache.commons.math3.stat.regression.UpdatingMultipleLinearRegression.regress.

Performs a regression on data present in buffers including only regressors indexed in variablesToInclude and outputs a RegressionResults object

Parameters

variablesToInclude:int[]: an array of indices of regressors to include

Returns:RegressionResults

RegressionResults acts as a container of regression output

Exceptions

MathIllegalArgumentException:: if the variablesToInclude array is null or zero length
OutOfRangeException:: if a requested variable is not present in model

removeData	back to summary
public void removeData(final double x, final double y) Removes the observation (x,y) from the regression data set. Mirrors the addData method. This method permits the use of SimpleRegression instances in streaming mode where the regression is applied to a sliding "window" of observations, however the caller is responsible for maintaining the set of observations in the window. The method has no effect if there are no points of data (i.e. n=0) Parameters x:double independent variable value y:double dependent variable value

removeData

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public void removeData(final double x, final double y)

Removes the observation (x,y) from the regression data set.

Mirrors the addData method. This method permits the use of SimpleRegression instances in streaming mode where the regression is applied to a sliding "window" of observations, however the caller is responsible for maintaining the set of observations in the window.

The method has no effect if there are no points of data (i.e. n=0)

Parameters

x:double: independent variable value
y:double: dependent variable value

removeData back to summary

removeData	back to summary
public void removeData(double[][] data) Removes observations represented by the elements in `data`. If the array is larger than the current n, only the first n elements are processed. This method permits the use of SimpleRegression instances in streaming mode where the regression is applied to a sliding "window" of observations, however the caller is responsible for maintaining the set of observations in the window. To remove all data, use `clear()`. Parameters data:double[][] array of observations to be removed

public void removeData(double[][] data)

Removes observations represented by the elements in data.

If the array is larger than the current n, only the first n elements are processed. This method permits the use of SimpleRegression instances in streaming mode where the regression is applied to a sliding "window" of observations, however the caller is responsible for maintaining the set of observations in the window.

To remove all data, use clear().

Parameters

data:double[][]: array of observations to be removed

public Class SimpleRegression

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