scalation.analytics

SimpleRegression

Related Doc: package analytics

class SimpleRegression extends Predictor with Error

The SimpleRegression class supports simple linear regression. In this case, the vector 'x' consists of the constant one and a single variable 'x_1', i.e., (1, x_1). Fit the parameter vector 'b' in the regression equation

y = b dot x + e = (b_0, b_1) dot (1, x_1) + e = b_0 + b_1 * x_1 + e

where 'e' represents the residuals (the part not explained by the model).

Linear Supertypes

Error, Predictor, AnyRef, Any

Ordering

Alphabetic
By inheritance

Inherited

SimpleRegression
Error
Predictor
AnyRef
Any

Hide All
Show all

Learn more about member selection

Visibility

Public
All

Instance Constructors

new SimpleRegression(x: MatrixD, y: VectorD)

x
the input/design matrix augmented with a first column of ones
y
the response vector

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
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def fit: (VectorD, Double)

Return the fit (parameter vector b, quality of fit rSquared)
def flaw(method: String, message: String): Unit

Show the flaw by printing the error message.
Show the flaw by printing the error message.
method
the method where the error occurred
message
the error message

Definition Classes
Error
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

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

Definition Classes
Any
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def predict(z: Matrix): VectorD

Predict the value of y = f(z) by evaluating the formula y = b dot z for each row of matrix z.
Predict the value of y = f(z) by evaluating the formula y = b dot z for each row of matrix z.
z
the new matrix to predict

Definition Classes
SimpleRegression → Predictor
def predict(z: VectorD): Double

Predict the value of y = f(z) by evaluating the formula y = b dot z, i.e.0, (b_0, b_1) dot (1, z_1).
Predict the value of y = f(z) by evaluating the formula y = b dot z, i.e.0, (b_0, b_1) dot (1, z_1).
z
the new vector to predict

Definition Classes
SimpleRegression → Predictor
def predict(z: VectorI): Double

Given a new discrete data vector z, predict the y-value of f(z).
Given a new discrete data vector z, predict the y-value of f(z).
z
the vector to use for prediction

Definition Classes
Predictor
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def train(): Unit

Train the predictor by fitting the parameter vector (b-vector) in the simple regression equation y = b dot x + e = (b_0, b_1) dot (1, x_1) + e using the least squares method.
Train the predictor by fitting the parameter vector (b-vector) in the simple regression equation y = b dot x + e = (b_0, b_1) dot (1, x_1) + e using the least squares method.

Definition Classes
SimpleRegression → Predictor
See also
http://www.analyzemath.com/statistics/linear_regression.html
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( ... )

Inherited from Error

Inherited from Predictor

Inherited from AnyRef

Inherited from Any

Ungrouped