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, x1). Fit the parameter vector 'b' in the regression equation

y = b dot x + e = (b0, b1) dot (1, x1) + e = b0 + b1 * x1 + e

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

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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: AnyRef): Boolean

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    AnyRef
  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  8. final def eq(arg0: AnyRef): Boolean

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  11. def fit: (VectorD, Double)

    Return the fit (parameter vector b, quality of fit rSquared)

  12. 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
  13. final def getClass(): Class[_]

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. final def ne(arg0: AnyRef): Boolean

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  17. final def notify(): Unit

    Definition Classes
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  18. final def notifyAll(): Unit

    Definition Classes
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  19. def predict(z: MatrixD): 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
    SimpleRegressionPredictor
  20. def predict(z: VectorD): Double

    Predict the value of y = f(z) by evaluating the formula y = b dot z, i.

    Predict the value of y = f(z) by evaluating the formula y = b dot z, i.e.0, (b0, b1) dot (1.0, z1).

    z

    the new vector to predict

    Definition Classes
    SimpleRegressionPredictor
  21. 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
  22. final def synchronized[T0](arg0: ⇒ T0): T0

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  23. def toString(): String

    Definition Classes
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  24. def train(): Unit

    Train the predictor by fitting the parameter vector (b-vector) in the simple regression equation y = b dot x + e = (b0, b1) dot (1.

    Train the predictor by fitting the parameter vector (b-vector) in the simple regression equation y = b dot x + e = (b0, b1) dot (1.0, x1) + e using the least squares method.

    Definition Classes
    SimpleRegressionPredictor
    See also

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

  25. final def wait(): Unit

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

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  27. final def wait(arg0: Long): Unit

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Inherited from Error

Inherited from Predictor

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