VecStats

abstract def argmax: Int

Integer offset of the minimum element of the Vec, if one exists, or else -1

abstract def argmin: Int

Integer offset of the minimum element of the Vec, if one exists, or else -1

abstract def count: Int

Count of the non-NA elements of the Vec

abstract def countif(test: (A) ⇒ Boolean): Int

Counts the non-NA elements of the Vec subject to passing the predicate function

abstract def demeaned: Vec[Double]

Return a copy of a numeric Vec with its values demeaned according to the mean function

abstract def geomean: Double

Return the geometric median of the values in the Vec, ignoring NA

abstract def kurt: Double

Return the sample kurtosis of the values in the Vec, ignoring NA

abstract def logsum: Double

Return the sum of the natural log of each element, ignoring NA values

abstract def max: Option[A]

Maximum element of the Vec, if one exists, or else None

abstract def mean: Double

Return the mean (average) of the values in the Vec, ignoring NA

abstract def median: Double

Return the median of the values in the Vec, ignoring NA

abstract def min: Option[A]

Minimum element of the Vec, if one exists, or else None

abstract def percentile(tile: Double, method: PctMethod = PctMethod.NIST): Double

Return the percentile of the values at a particular threshold, ignoring NA

abstract def prod: A

Product of all the values in the Vec, ignoring NA values

abstract def rank(tie: RankTie = RankTie.Avg, ascending: Boolean = true): Vec[Double]

Return a Vec of ranks corresponding to a Vec of numeric values.

abstract def skew: Double

Return the sample skewness of the values in the Vec, ignoring NA

abstract def sum: A

Sum of the elements of the Vec, ignoring NA values

abstract def variance: Double

Return the sample variance of the values in the Vec, ignoring NA

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

def _demeaned(r: Vec[A], subOp: (A, Double) ⇒ Double): Vec[Double]

def _kurt(r: Vec[A], subOp: (A, Double) ⇒ Double): Double

def _median(r: Vec[A])(implicit n: NUM[A]): Double

def _percentile(v: Vec[Double], tile: Double, method: PctMethod)(implicit n: NUM[A]): Double

def _rank(v: Array[Double], tie: RankTie, ascending: Boolean): Vec[Double]

def _skew(r: Vec[A], subOp: (A, Double) ⇒ Double): Double

def _variance(r: Vec[A], subOp: (A, Double) ⇒ Double): Double

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): java.lang.Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def stdev: Double

Return the sample standard deviation of values in the Vec, ignoring NA

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

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit