object StatVector

The StatVector companion object extends statistics vector operations to matrices and convenience functions on vectors.

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  4. def acorr(y: VectoD): Double

    Compute the '1'-lag auto-correlation of 'self' vector.

    Compute the '1'-lag auto-correlation of 'self' vector. Assumes a stationary vector, if not its an approximation.

    y

    the vector whose auto-correlation is sought

  5. def acorrz(z: VectoD): Double

    Compute the '1'-lag auto-correlation of 'self' vector.

    Compute the '1'-lag auto-correlation of 'self' vector. Assumes a zero-centered, stationary vector, if not its an approximation.

    z

    the zero-centered vector whose auto-correlation is sought

  6. def acov(y: VectoD): Double

    Compute the '1'-lag auto-covariance of 'self' vector.

    Compute the '1'-lag auto-covariance of 'self' vector.

    y

    the vector whose auto-covariance is sought

  7. def acovz(z: VectoD): Double

    Compute the '1'-lag auto-covariance of 'self' vector.

    Compute the '1'-lag auto-covariance of 'self' vector.

    z

    the zero-centered vector whose auto-covariance is sought

  8. final def asInstanceOf[T0]: T0
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  9. def center(x: MatriD, mu_x: VectoD): MatriD

    Center the input matrix 'x' to zero mean, column-wise, by subtracting the mean.

    Center the input matrix 'x' to zero mean, column-wise, by subtracting the mean.

    x

    the input matrix to center

    mu_x

    the vector of column means of matrix x

  10. def clone(): AnyRef
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  11. def corr(x: MatriD): MatriD

    Return the correlation matrix for the columns of matrix 'x'.

    Return the correlation matrix for the columns of matrix 'x'. If either variance is zero (column i, column j), will result in Not-a-Number (NaN), return one if the vectors are the same, or -0 (indicating undefined). Note: sample vs. population results in essentailly the same values.

    x

    the matrix whose column-column correlations are sought

    See also

    the related 'cos' function.

  12. def corrMat(y: VectoD): MatriD

    Return the first-order auto-regressive correlation matrix for vector (e.g., time series) 'y'.

    Return the first-order auto-regressive correlation matrix for vector (e.g., time series) 'y'.

    y

    the vector whose auto-regressive correlation matrix is sought

    See also

    halweb.uc3m.es/esp/Personal/personas/durban/esp/web/notes/gls.pdf

  13. def cos(x: MatriD): MatriD

    Return the cosine similarity matrix for the columns of matrix 'x'.

    Return the cosine similarity matrix for the columns of matrix 'x'. If the vectors are centered, will give the correlation.

    x

    the matrix whose column-column cosines are sought

    See also

    stats.stackexchange.com/questions/97051/building-the-connection-between-cosine-similarity-and-correlation-in-r

  14. def cov(x: MatriD): MatriD

    Return the sample covariance matrix for the columns of matrix 'x'.

    Return the sample covariance matrix for the columns of matrix 'x'.

    x

    the matrix whose column covariances are sought

  15. def covMat(y: VectoD): MatriD

    Return the first-order auto-regressive covariance matrix for vector (e.g., time series) 'y'.

    Return the first-order auto-regressive covariance matrix for vector (e.g., time series) 'y'.

    y

    the vector whose auto-regressive covariance matrix is sought

    See also

    halweb.uc3m.es/esp/Personal/personas/durban/esp/web/notes/gls.pdf

  16. final def eq(arg0: AnyRef): Boolean
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  24. def pcov(x: MatriD): MatriD

    Return the population covariance matrix for the columns of matrix 'x'.

    Return the population covariance matrix for the columns of matrix 'x'.

    x

    the matrix whose column columns covariances are sought

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