Packages

class TranRegression[MatT <: MatriD, VecT <: VectoD] extends Regression[MatT, VectoD]

The TranRegression class supports transformed multiple linear regression. In this case, 'x' is multi-dimensional [1, x_1, ... x_k]. Fit the parameter vector 'b' in the transformed regression equation

transform (y) = b dot x + e = b_0 + b_1 * x_1 + b_2 * x_2 ... b_k * x_k + e

where 'e' represents the residuals (the part not explained by the model) and 'transform' is the function (defaults to log) used to transform the response vector 'y'. Common transforms include 'log (y)', 'sqrt (y)' when 'y > 0', or even 'sq (y)', 'exp (y)'. More generally, a Box-Cox Transformation may be applied.

See also

www.ams.sunysb.edu/~zhu/ams57213/Team3.pptx

citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.469.7176&rep=rep1&type=pdf Use Least-Squares (minimizing the residuals) to fit the parameter vector 'b' Note: this class does not provide transformations on columns of matrix 'x'.

Linear Supertypes
Regression[MatT, VectoD], Error, Predictor, AnyRef, Any
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  1. TranRegression
  2. Regression
  3. Error
  4. Predictor
  5. AnyRef
  6. Any
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Instance Constructors

  1. new TranRegression(x: MatT, y: VecT, transform: FunctionS2S = log, transInv: FunctionS2S = exp, technique: RegTechnique = QR)

    x

    the design/data matrix

    y

    the response vector

    transform

    the transformation function (defaults to log)

    transInv

    the inverse transformation function to rescale predictions to original y scale (defaults to exp)

    technique

    the technique used to solve for b in x.t*x*b = x.t*y

Type Members

  1. type Fac_QR = Fac_QR_H[MatT]
    Definition Classes
    Regression

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. var aic: Double
    Attributes
    protected
    Definition Classes
    Regression
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. val b: VectoD
    Attributes
    protected
    Definition Classes
    Predictor
  7. def backwardElim(cols: Set[Int]): (Int, VectoD, VectoD)

    Perform backward elimination to remove the least predictive variable from the existing model, returning the variable to eliminate, the new parameter vector and the new quality of fit.

    Perform backward elimination to remove the least predictive variable from the existing model, returning the variable to eliminate, the new parameter vector and the new quality of fit. May be called repeatedly.

    cols

    the columns of matrix x included in the existing model

    Definition Classes
    Regression
  8. var bic: Double
    Attributes
    protected
    Definition Classes
    Regression
  9. def build(x: MatriD, y: VectoD): Predictor
    Definition Classes
    Predictor
  10. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  11. def coefficient: VectoD

    Return the vector of coefficient/parameter values.

    Return the vector of coefficient/parameter values.

    Definition Classes
    Predictor
  12. val df: Int
    Attributes
    protected
    Definition Classes
    Regression
  13. def diagnose(yy: VectoD): Unit

    Compute diagostics for the regression model.

    Compute diagostics for the regression model.

    yy

    the response vector

    Attributes
    protected
    Definition Classes
    RegressionPredictor
  14. val e: VectoD
    Attributes
    protected
    Definition Classes
    Predictor
  15. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  16. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  17. def eval(yy: VectoD = y): Unit

    Compute the error and useful diagnostics

    Compute the error and useful diagnostics

    yy

    the response vector

    Definition Classes
    TranRegressionRegressionPredictor
  18. var fStat: Double
    Attributes
    protected
    Definition Classes
    Regression
  19. val fac: Factorization
    Attributes
    protected
    Definition Classes
    Regression
  20. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  21. def fit: VectoD

    Return the quality of fit.

    Return the quality of fit.

    Definition Classes
    RegressionPredictor
  22. def fitLabels: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit.

    Definition Classes
    RegressionPredictor
  23. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  24. def forwardSel(cols: Set[Int]): (Int, VectoD, VectoD)

    Perform forward selection to add the most predictive variable to the existing model, returning the variable to add, the new parameter vector and the new quality of fit.

    Perform forward selection to add the most predictive variable to the existing model, returning the variable to add, the new parameter vector and the new quality of fit. May be called repeatedly.

    cols

    the columns of matrix x included in the existing model

    Definition Classes
    Regression
  25. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  26. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  27. val index_rSq: Int
    Definition Classes
    Predictor
  28. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  29. val k: Int
    Attributes
    protected
    Definition Classes
    Regression
  30. val m: Double
    Attributes
    protected
    Definition Classes
    Regression
  31. val mae: Double
    Attributes
    protected
    Definition Classes
    Predictor
  32. def metrics: Map[String, Any]

    Build a map of diagnostics metrics for the overall quality of fit.

    Build a map of diagnostics metrics for the overall quality of fit.

    Definition Classes
    RegressionPredictor
  33. val mse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  34. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  35. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  36. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  37. var p: VectoD
    Attributes
    protected
    Definition Classes
    Regression
  38. def predict(z: MatT): VectoD

    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
    TranRegressionRegression
  39. def predict(z: VectoD): Double

    Predict the value of y = f(z) by evaluating the formula y = b dot z, e.g., (b_0, b_1, b_2) dot (1, z_1, z_2).

    Predict the value of y = f(z) by evaluating the formula y = b dot z, e.g., (b_0, b_1, b_2) dot (1, z_1, z_2).

    z

    the new vector to predict

    Definition Classes
    TranRegressionRegressionPredictor
  40. def predict(z: VectoI): 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
  41. var rBarSq: Double
    Attributes
    protected
    Definition Classes
    Regression
  42. val rSq: Double
    Attributes
    protected
    Definition Classes
    Predictor
  43. val r_df: Double
    Attributes
    protected
    Definition Classes
    Regression
  44. def report(): Unit

    Print results and diagnostics for each predictor 'x_j' and the overall quality of fit.

    Print results and diagnostics for each predictor 'x_j' and the overall quality of fit.

    Definition Classes
    Regression
  45. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    Predictor
  46. val rmse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  47. val sse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  48. val ssr: Double
    Attributes
    protected
    Definition Classes
    Predictor
  49. val sst: Double
    Attributes
    protected
    Definition Classes
    Predictor
  50. var stdErr: VectoD
    Attributes
    protected
    Definition Classes
    Regression
  51. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  52. var t: VectoD
    Attributes
    protected
    Definition Classes
    Regression
  53. def toString(): String
    Definition Classes
    AnyRef → Any
  54. def train(yy: VectoD = y): Regression[MatT, VectoD]

    Train the predictor by fitting the parameter vector (b-vector) in the multiple regression equation

    Train the predictor by fitting the parameter vector (b-vector) in the multiple regression equation

    yy = b dot x + e = [b_0, ... b_k] dot [1, x_1 , ... x_k] + e

    using the ordinary least squares 'OLS' method.

    yy

    the response vector to work with (defaults to y)

    Definition Classes
    RegressionPredictor
  55. def vif: VectoD

    Compute the Variance Inflation Factor 'VIF' for each variable to test for multi-collinearity by regressing 'xj' against the rest of the variables.

    Compute the Variance Inflation Factor 'VIF' for each variable to test for multi-collinearity by regressing 'xj' against the rest of the variables. A VIF over 10 indicates that over 90% of the variance of 'xj' can be predicted from the other variables, so 'xj' is a candidate for removal from the model.

    Definition Classes
    Regression
  56. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  57. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  58. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  59. val x: MatT
    Attributes
    protected
    Definition Classes
    Regression
  60. val y: VectoD
    Attributes
    protected
    Definition Classes
    Regression

Inherited from Regression[MatT, VectoD]

Inherited from Error

Inherited from Predictor

Inherited from AnyRef

Inherited from Any

Ungrouped