Packages

c

scalation.analytics

SimpleRegression

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).

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Error, Predictor, AnyRef, Any
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Instance Constructors

  1. new SimpleRegression(x: MatrixD, y: VectorD)

    x

    the input/design matrix augmented with a first column of ones

    y

    the response vector

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. val b: VectoD

    Coefficient/parameter vector [b_0, b_1, ...

    Coefficient/parameter vector [b_0, b_1, ... b_k]

    Attributes
    protected
    Definition Classes
    Predictor
  6. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  7. def coefficient: VectoD

    Return the vector of coefficient/parameter values.

    Return the vector of coefficient/parameter values.

    Definition Classes
    Predictor
  8. def diagnose(yy: VectorD, e: VectorD): Unit

    Compute diagostics for the regression model.

    Compute diagostics for the regression model.

    yy

    the response vector

    e

    the residual/error vector

  9. val e: VectoD

    Residual/error vector [e_0, e_1, ...

    Residual/error vector [e_0, e_1, ... e_m-1]

    Attributes
    protected
    Definition Classes
    Predictor
  10. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  11. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  12. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  13. def fit: VectorD

    Return the quality of fit including 'rSquared'.

    Return the quality of fit including 'rSquared'.

    Definition Classes
    SimpleRegressionPredictor
  14. def fitLabels: Array[String]

    Return the labels for the fit.

    Return the labels for the fit. Override when necessary.

    Definition Classes
    Predictor
  15. final 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
  16. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  17. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  18. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  19. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  20. final def notify(): Unit
    Definition Classes
    AnyRef
  21. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  22. def predict(z: VectoD): 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
    SimpleRegressionPredictor
  23. 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
  24. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    Predictor
  25. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  26. def toString(): String
    Definition Classes
    AnyRef → Any
  27. 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
    SimpleRegressionPredictor
    See also

    http://www.analyzemath.com/statistics/linear_regression.html

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

Inherited from Error

Inherited from Predictor

Inherited from AnyRef

Inherited from Any

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