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net.sf.ij_plugins.color.calibration.regression

Regression

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object Regression

Helper methods for computing linear regression.

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  1. case class Result(beta: Array[Double], rSquared: Double, adjustedRSquared: Double, regressandVariance: Double, regressionStandardError: Double) extends Product with Serializable

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    Result returned by regression methods.

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  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  6. def createLinear(standard: Array[Double], observation: Array[Array[Double]]): Result

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    Compute linear fit coefficients that map observations to a reference: s = A*[o, 1].

    Compute linear fit coefficients that map observations to a reference: s = A*[o, 1].

    standard

    reference values.

    observation

    observed values.

    returns

    linear fit coefficients.

  7. final def eq(arg0: AnyRef): Boolean

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  9. def finalize(): Unit

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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  16. def regression(standard: Array[Double], observation: Array[Array[Double]], noIntercept: Boolean): Result

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    Compute linear fit coefficient s = A*o if noIntercept is true or s = A*o + b if noIntercept is false.

    Compute linear fit coefficient s = A*o if noIntercept is true or s = A*o + b if noIntercept is false.

    standard

    array of expected output values.

    observation

    array of input values

    noIntercept

    true means the model is to be estimated without an intercept term

    returns

    linear fit coefficients

  17. def regression(standard: Array[Double], observation: Array[Array[Double]]): Result

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    Compute linear fit coefficient

    Compute linear fit coefficient

    s = A*o.
    standard

    array of expected output values.

    observation

    array of input values

    returns

    linear fit coefficients

    See also

    #regression(double[], double[][], boolean)

  18. final def synchronized[T0](arg0: ⇒ T0): T0

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