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class Fit extends AnyRef

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Instance Constructors

  1. new Fit(y: VectoD, n: Int, df_: (Double, Double) = (0.0, 0.0))

    y

    the values in the m-dimensional response vector

    n

    the number of parameters (b.dim)

    df_

    the degrees of freedom (df._1, df._2) for (regression, error)

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  6. def diagnose(e: VectoD, w: VectoD = null, yp: VectoD = null, y_: VectoD = y): Unit

    Given the error/residual vector, compute the quality of fit measures.

    Given the error/residual vector, compute the quality of fit measures.

    e

    the corresponding m-dimensional error vector (y - yp)

    w

    the weights on the instances

    yp

    the predicted response vector (x * b)

  7. final def eq(arg0: AnyRef): Boolean
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  8. def equals(arg0: Any): Boolean
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  9. def finalize(): Unit
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  10. def fit: VectoD

    Return the quality of fit including 'rSq', 'sst', 'sse', 'mse0', rmse', 'mae', 'df._2', 'rBarSq', 'fStat', 'aic', 'bic'.

    Return the quality of fit including 'rSq', 'sst', 'sse', 'mse0', rmse', 'mae', 'df._2', 'rBarSq', 'fStat', 'aic', 'bic'. Note, if 'sse > sst', the model introduces errors and the 'rSq' may be negative, otherwise, R^2 ('rSq') ranges from 0 (weak) to 1 (strong). Note that 'rSq' is the number 5 measure. Override to add more quality of fit measures.

  11. def fitLabel: Seq[String]

    Return the labels for the quality of fit measures.

    Return the labels for the quality of fit measures. Override to add more quality of fit measures.

  12. def fitMap: Map[String, String]

    Build a map of quality of fit measures (use of LinedHashMap makes it ordered).

    Build a map of quality of fit measures (use of LinedHashMap makes it ordered). Override to add more quality of fit measures.

  13. final def getClass(): Class[_]
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  14. def hashCode(): Int
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  15. val index_rSq: Int
  16. final def isInstanceOf[T0]: Boolean
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  17. def mse_: Double

    Return the mean of squares for error (sse / df._2).

    Return the mean of squares for error (sse / df._2). Must call diagnose first.

  18. final def ne(arg0: AnyRef): Boolean
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  19. final def notify(): Unit
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  20. final def notifyAll(): Unit
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  21. def resetDF(df_update: (Double, Double)): Unit

    Reset the degrees of freedom to the new updated values.

    Reset the degrees of freedom to the new updated values. For some models, the degrees of freedom is not known until after the model is built.

    df_update

    the updated degrees of freedom

  22. def sumCoeff(b: VectoD, stdErr: VectoD = null): String

    Produce the summary report portion for the cofficients.

    Produce the summary report portion for the cofficients.

    b

    the parameters/coefficients for the model

  23. def summary(b: VectoD, stdErr: VectoD = null, show: Boolean = false): String

    Produce a summary report with diagnostics for each predictor 'x_j' and the overall quality of fit.

    Produce a summary report with diagnostics for each predictor 'x_j' and the overall quality of fit.

    b

    the parameters/coefficients for the model

    show

    flag indicating whether to print the summary

  24. final def synchronized[T0](arg0: ⇒ T0): T0
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  25. def toString(): String
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  26. final def wait(): Unit
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  27. final def wait(arg0: Long, arg1: Int): Unit
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  28. final def wait(arg0: Long): Unit
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