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• StatisticsGatherer
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PHP Класс MCordingley\Regression\StatisticsGatherer\Linear

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Открытые методы

Метод	Описание
__construct ( Observations $observations, array $coefficients, MCordingley\Regression\Predictor\Predictor $predictor )
getDegreesOfFreedomError ( ) : integer
getDegreesOfFreedomModel ( ) : integer
getDegreesOfFreedomTotal ( ) : integer
getFStatistic ( ) : float	Returns the F statistic, which is compared against the F distribution CDF to determine if the regression is "significant" or not.
getRSquared ( ) : float	Calculates the coefficient of determination. i.e. how well the line of best fit describes the data.
getStandardError ( ) : float	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.
getStandardErrorCoefficients ( ) : array	Calculates the standard error of each of the regression coefficients.
getTStatistics ( ) : array	Calculates the t test values of each of the regression coefficients.

Приватные методы

Метод	Описание
getFeatureCount ( ) : integer
getMeanSquaredError ( ) : float	Returns the mean-squared error of the regression, which is effectively the "average" of the corresponding sum of squares.
getMeanSquaredModel ( ) : float	Returns the mean-squared model of the regression, which is effectively the "average" of the corresponding sum of squares.
getObservationCount ( ) : integer
getPredictedOutcomes ( ) : array
getSumSquaredError ( ) : float	Calculates the sum of the squares of the residuals, which are the distances of the observations from their predicted values, a raw measure of the overall error in the regression model.
getSumSquaredModel ( ) : float	Calculates the sum-squared error of the regression. This is the sum of the squared distances of predicted values from their average.
getSumSquaredTotal ( ) : float	Calculates the sum-squared total of the regression. This is the sum of the squared distances of observations from their average, a useful measure to put the sum-squared error (SSE) and sum-squared model (SSM) into context.
sumSquaredDifference ( array $series, float $baseline ) : float

Описание методов