Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

   Any

2.  final def ##(): Int

   Definition Classes

   Any

3.  final def ==(arg0: Any): Boolean

   Definition Classes

   Any

4.  def acorr(k: Int = 1): Double

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

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

   k

   the lag parameter

5.  def acov(k: Int = 1): Double

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

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

   k

   the lag parameter

6.  final def asInstanceOf[T0]: T0

   Definition Classes

   Any

7.  def ci(mu_: Double, ihw: Double): (Double, Double)

   Return the confidence interval as (lower, upper) after calling either interval (unknown standard deviation) or interval2 (know standard deviation).

   Return the confidence interval as (lower, upper) after calling either interval (unknown standard deviation) or interval2 (know standard deviation).

   mu_

   the sample mean

   ihw

   the interval half width

8.  def corr(y: VectorI): Double

   Compute Pearson's correlation of 'self' vector with vector 'y'.

   Compute Pearson's correlation of 'self' vector with vector 'y'.

   y

   the other vector

9.  def cov(y: VectorI): Double

   Compute the sample covariance of 'self' vector with vector 'y'.

   Compute the sample covariance of 'self' vector with vector 'y'.

   y

   the other vector

10.  def getClass(): Class[_ <: AnyVal]

    Definition Classes

    AnyVal → Any

11.  def interval(p: Double = .95): Double

    Compute the confidence interval half-width for the given confidence level.

    Compute the confidence interval half-width for the given confidence level. The Confidence Interval (CI) is on the mean, i.e., CI = [mean +/- interval].

    p

    the confidence level

12.  def interval2(sig: Double, p: Double = .95): Double

    Compute the confidence interval half-width for the given confidence level.

    Compute the confidence interval half-width for the given confidence level. The Confidence Interval (CI) is on the mean, i.e., CI = [mean +/- interval]. This method assumes that the population standard deviation is known. uses the Standard Normal distribution.

    sig

    the population standard deviation

    p

    the confidence level

13.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

14.  def kurtosis(unbiased: Boolean = false): Double

    Compute the kurtosis of 'self' vector.

    Compute the kurtosis of 'self' vector. High kurtosis (> 3) indicates a distribution with heavier tails than a Normal distribution.

    E(X - μ)^{4 / σ}4

    unbiased

    whether to shift the result so Normal is at 0 rather than 3

    See also

    www.mathworks.com/help/stats/kurtosis.html

15.  def median(k: Int = (self.dim+1)/2): Int

    Compute the 'k'-median ('k'-th smallest value) of 'self' vector.

    Compute the 'k'-median ('k'-th smallest value) of 'self' vector. Setting 'k = (dim+1)/2' gives the regular median.

    k

    the type of median (k-th smallest value)

16.  def mode: Int

    Compute the mode of the 'self' vector, i.e., the value that occurs most frequently.

17.  def ms: Double

    Compute the mean square (ms) of 'self' vector.

18.  def pcorr(y: VectorI): Double

    Compute the population Pearson's correlation of 'self' vector with vector 'y'.

    Compute the population Pearson's correlation of 'self' vector with vector 'y'. Note: should only differ from 'corr' due to round-off errors and NaN issue.

    y

    the other vector

19.  def pcov(y: VectorI): Double

    Compute the population covariance of 'self' vector with vector 'y'.

    Compute the population covariance of 'self' vector with vector 'y'.

    y

    the other vector

20.  def precise(threshold: Double = .2, p: Double = .95): Boolean

    Determine if the Confidence Interval (CI) on the mean is tight enough.

    Determine if the Confidence Interval (CI) on the mean is tight enough.

    threshold

    the cut-off value for CI to be considered tight

    p

    the confidence level

21.  def precision(p: Double = .95): Double

    Compute the relative precision, i.e., the ratio of the confidence interval half-width and the mean.

    Compute the relative precision, i.e., the ratio of the confidence interval half-width and the mean.

    p

    the confidence level

22.  def pstddev: Double

    Compute the population standard deviation of 'self' vector.

    Compute the population standard deviation of 'self' vector.

    See also

    VectorI for pvariance

23.  def rms: Double

    Compute the root mean square (rms) of 'self' vector.

24.  def scorr(y: VectorI): Double

    Compute Spearman's rank correlation of 'self' vector with vector 'y'.

    Compute Spearman's rank correlation of 'self' vector with vector 'y'.

    y

    the other vector

    See also

    en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient

25.  val self: VectorI
26.  def skew(unbiased: Boolean = false): Double

    Compute the skewness of 'self' vector.

    Compute the skewness of 'self' vector. Negative skewness indicates the distribution is elongated on the left, zero skewness indicates it is symmetric, and positive skewness indicates it is elongated on the right.

    E(X - μ)^{3 / σ}3

    unbiased

    whether to correct for bias

    See also

    www.mathworks.com/help/stats/skewness.html

27.  def stddev: Double

    Compute the standard deviation of 'self' vector.

    Compute the standard deviation of 'self' vector.

    See also

    VectorI for variance

28.  def t_sigma(p: Double = .95): Double

    Compute the product of the critical value from the t-distribution and the standard deviation of the vector.

    Compute the product of the critical value from the t-distribution and the standard deviation of the vector.

    p

    the confidence level

29.  def toString(): String

    Definition Classes

    Any

30.  def z_sigma(p: Double = .95): Double

    Compute the product of the critical value from the z-distribution (Standard Normal) and the standard deviation of the vector.

    Compute the product of the critical value from the z-distribution (Standard Normal) and the standard deviation of the vector.

    p

    the confidence level