MCordingley\Regression\StatisticsGatherer\Linear::getStandardError PHP Méthode

getStandardError() public méthode

Calculates the standard error of the regression. This is the average distance of observed values from the regression line. It's conceptually similar to the standard deviation.
public getStandardError ( ) : float
Résultat float
    public function getStandardError() : float
    {
        return sqrt($this->getMeanSquaredError());
    }

Usage Example

 public function testStatistics()
 {
     $observations = Observations::fromArray($this->getFeatures(), $this->getOutcomes());
     $coefficients = [1.0954970633022, 0.92451598868827];
     $predictor = new LinearPredictor($coefficients);
     $statisticsGatherer = new LinearStatisticsGatherer($observations, $coefficients, $predictor);
     static::assertEquals(4, $statisticsGatherer->getDegreesOfFreedomTotal());
     static::assertEquals(3, $statisticsGatherer->getDegreesOfFreedomError());
     static::assertEquals(1, $statisticsGatherer->getDegreesOfFreedomModel());
     static::assertEquals(1.94, round($statisticsGatherer->getFStatistic(), 2));
     static::assertEquals(0.39, round($statisticsGatherer->getRSquared(), 2));
     $stdErrorCoefficients = $statisticsGatherer->getStandardErrorCoefficients();
     static::assertEquals(1.51, round($stdErrorCoefficients[0], 2));
     static::assertEquals(0.66, round($stdErrorCoefficients[1], 2));
     static::assertEquals(1.42, round($statisticsGatherer->getStandardError(), 2));
     $tStatistics = $statisticsGatherer->getTStatistics();
     static::assertEquals(0.73, round($tStatistics[0], 2));
     static::assertEquals(1.39, round($tStatistics[1], 2));
 }