Packages

class ANOVA1 extends Predictor with Error

The ANOVA1 class supports one-way ANalysis Of VAriance (ANOVA), i.e, it allows only one binary/categorial treatment variable. It is framed using General Linear Model 'GLM' notation and supports the use of one binary/categorical treatment variable 't'. This is done by introducing dummy variables 'd_j' to distinguish the treatment level. The problem is again to fit the parameter vector 'b' in the following equation

y = b dot x + e = b_0 + b_1 * d_1 + b_1 * d_2 ... b_k * d_k + e

where 'e' represents the residuals (the part not explained by the model). Use Least-Squares (minimizing the residuals) to solve for the parameter vector 'b' using the Normal Equations:

x.t * x * b = x.t * y b = fac.solve (.)

See also

psych.colorado.edu/~carey/Courses/PSYC5741/handouts/GLM%20Theory.pdf

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Error, Predictor, AnyRef, Any
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Instance Constructors

  1. new ANOVA1(t: VectoI, y: VectoD, levels: Int, technique: RegTechnique = QR)

    t

    the binary/categorical treatment variable vector

    y

    the response vector

    levels

    the number of treatment levels (1, ... levels)

    technique

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

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. def assignDummyVar(lev: Int): VectorD

    Assign values for the dummy variables based on a treatment variable's level 'lev'.

    Assign values for the dummy variables based on a treatment variable's level 'lev'.

    lev

    treatment level of the variable

  6. def assignDummyVars(tt: VectoI = t): Unit

    Assign values for the dummy variables based on the treatment vector 't'.

  7. val b: VectoD
    Attributes
    protected
    Definition Classes
    Predictor
  8. 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

  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. def diagnose(yy: VectoD): Unit

    Compute diagostics for the predictor.

    Compute diagostics for the predictor. Override to add more diagostics. Note, for 'mse' and 'rmse', 'sse' is divided by the number of instances 'm' rather than the degrees of freedom.

    yy

    the response vector

    Attributes
    protected
    Definition Classes
    Predictor
    See also

    en.wikipedia.org/wiki/Mean_squared_error

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

    Compute the error and useful diagnostics.

    Compute the error and useful diagnostics.

    yy

    the response vector

    Definition Classes
    ANOVA1Predictor
  17. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  18. def fit: VectoD

    Return the quality of fit.

    Return the quality of fit.

    Definition Classes
    ANOVA1Predictor
  19. def fitLabels: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit.

    Definition Classes
    ANOVA1Predictor
  20. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  21. 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

  22. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  23. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  24. val index_rSq: Int
    Definition Classes
    Predictor
  25. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  26. val mae: Double
    Attributes
    protected
    Definition Classes
    Predictor
  27. def metrics: Map[String, Any]

    Build a map of selected quality of fit measures/metrics.

    Build a map of selected quality of fit measures/metrics.

    Definition Classes
    Predictor
  28. val mse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  29. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  30. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  31. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  32. 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
    ANOVA1Predictor
  33. def predict(z: Int): 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

  34. 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
  35. val rSq: Double
    Attributes
    protected
    Definition Classes
    Predictor
  36. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    ANOVA1Predictor
  37. val rg: Regression[MatrixD, VectoD]
  38. val rmse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  39. val sse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  40. val ssr: Double
    Attributes
    protected
    Definition Classes
    Predictor
  41. val sst: Double
    Attributes
    protected
    Definition Classes
    Predictor
  42. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  43. def toString(): String
    Definition Classes
    AnyRef → Any
  44. def train(yy: VectoD = y): Regression[MatrixD, 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, d_1, ... d_k] + e

    using the least squares method.

    yy

    the response vector

    Definition Classes
    ANOVA1Predictor
  45. 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.

  46. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  47. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  48. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  49. val x: MatrixD

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Inherited from Predictor

Inherited from AnyRef

Inherited from Any

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