Correlation

A class to calculate covariance and the first two central moments of a sequence of pairs of Doubles, from which the pearson correlation coeifficient can be calculated.

m{i}x denotes the ith central moment of the first projection of the pair. m{i}y denotes the ith central moment of the second projection of the pair. c2 the covariance equivalent of the second central moment, i.e. c2 = Sum_(x,y) (x - m1x)*(y - m1y).

Linear Supertypes

Serializable, Serializable, Product, Equals, AnyRef, Any

Instance Constructors

new Correlation(c2: Double, m2x: Double, m2y: Double, m1x: Double, m1y: Double, m0: Double)

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
val c2: Double
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def correlation: Double

returns
Pearson's correlation coefficient
def covariance: Double
def distanceMetric: Double
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def linearLeastSquares: (Double, Double)

Assume this instance of Correlation came from summing together Correlation.apply((x_i, y_i)) for i in 1...n.
Assume this instance of Correlation came from summing together Correlation.apply((x_i, y_i)) for i in 1...n.
returns
(m, b) where y = mx + b is the line with the least squares fit of the points (x_i, y_i). See, e.g. https://mathworld.wolfram.com/LeastSquaresFitting.html.
val m0: Double
val m1x: Double
val m1y: Double
val m2x: Double
val m2y: Double
def meanX: Double
def meanY: Double
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def scale(z: Double): Correlation
def stddevX: Double
def stddevY: Double
def swap: Correlation
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def totalWeight: Double
def varianceX: Double
def varianceY: Double
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Docs: object Correlation | package algebird

case class Correlation(c2: Double, m2x: Double, m2y: Double, m1x: Double, m1y: Double, m0: Double) extends Product with Serializable

Instance Constructors

new Correlation(c2: Double, m2x: Double, m2y: Double, m1x: Double, m1y: Double, m0: Double)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

val c2: Double

def clone(): AnyRef

def correlation: Double

def covariance: Double

def distanceMetric: Double

final def eq(arg0: AnyRef): Boolean

def finalize(): Unit

final def getClass(): Class[_]

final def isInstanceOf[T0]: Boolean

def linearLeastSquares: (Double, Double)

val m0: Double

val m1x: Double

val m1y: Double

val m2x: Double

val m2y: Double

def meanX: Double

def meanY: Double

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def scale(z: Double): Correlation

def stddevX: Double

def stddevY: Double

def swap: Correlation

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

def totalWeight: Double

def varianceX: Double

def varianceY: Double

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from AnyRef

Inherited from Any

Ungrouped