com.imsl.stat
Class LinearRegression.CaseStatistics

java.lang.Object
  com.imsl.stat.LinearRegression.CaseStatistics

Enclosing class:

LinearRegression

public class LinearRegression.CaseStatistics

extends Object

Inner Class CaseStatistics allows for the computation of predicted values, confidence intervals, and diagnostics for detecting outliers and cases that greatly influence the fitted regression.

Method Summary

double[]	getConfidenceInterval() Returns the Confidence Interval on the mean for an observation.
double	getCooksDistance() Returns Cook's Distance for an observation.
double	getDFFITS() Returns DFFITS for an observation.
double	getJackknifeResidual() Returns the Jackknife Residual for an observation.
double	getLeverage() Returns the Leverage for an observation.
double	getObservedResponse() Returns the observed response for an observation.
double	getPredictedResponse() Returns the predicted response for an observation.
double[]	getPredictionInterval() Returns the Prediction Interval for an observation.
double	getResidual() Returns the Residual for an observation.
double	getStandardizedResidual() Returns the Standardized Residual for an observation.
void	setConLevelMean(double conpcm) Sets the confidence level for two-sided interval estimates on the mean, in percent.
void	setConLevelPred(double conpcp) Sets the confidence level for two-sided prediction intervals, in percent.
void	setEffects(int effects) Sets the effect option.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

getConfidenceInterval

public double[] getConfidenceInterval()

Returns the Confidence Interval on the mean for an observation.

Returns:

a double[2] array containing the Confidence Interval for the observation

getCooksDistance

public double getCooksDistance()

Returns Cook's Distance for an observation.

Returns:

a double containing Cook's Distance for an observation

getDFFITS

public double getDFFITS()

Returns DFFITS for an observation.

Returns:

a double containing the DFFITS value for an observation

getJackknifeResidual

public double getJackknifeResidual()

Returns the Jackknife Residual for an observation.

Returns:

a double containing the Jackknife Residual for an observation

getLeverage

public double getLeverage()

Returns the Leverage for an observation.

Returns:

a double containing the Leverage for an observation

getObservedResponse

public double getObservedResponse()

Returns the observed response for an observation.

Returns:

a double containing the observed response for an observation

getPredictedResponse

public double getPredictedResponse()

Returns the predicted response for an observation.

Returns:

a double containing the predicted response for an observation

getPredictionInterval

public double[] getPredictionInterval()

Returns the Prediction Interval for an observation.

Returns:

a double[2] array containing the Prediction Interval for the observation

getResidual

public double getResidual()

Returns the Residual for an observation.

Returns:

a double containing the residual for an observation

getStandardizedResidual

public double getStandardizedResidual()

Returns the Standardized Residual for an observation.

Returns:

a double containing the Standardized Residual for an observation

setConLevelMean

public void setConLevelMean(double conpcm)

Sets the confidence level for two-sided interval estimates on the mean, in percent.

Parameters:

conpcm - a double used as the confidence level for two-sided interval estimates on the mean, in percent. If this member function is not called, conpcm is set to .95.

setConLevelPred

public void setConLevelPred(double conpcp)

Sets the confidence level for two-sided prediction intervals, in percent.

Parameters:

conpcp - a double used as the confidence level for two-sided prediction intervals, in percent. If this member function is not called, conpcp is set to .95.

setEffects

public void setEffects(int effects)

Sets the effect option.

Parameters:

effects - an int, the absolute value of which is used to specify the number of effects (sources of variation) due to the model. The sign of effect specifies the following:

effects	Meaning
	Each effect corresponds to a single regressor (coefficient) in the model.
	Currently not used. This will result in an IllegalArgumentException being thrown.
0	There are no effects in the model. hasIntercept must be set to true.

If this member function is not called, effects is set to -1.