Packages

class ANOVA1 extends PredictorVec

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

ANCOVA for models with multiple variables

Linear Supertypes
PredictorVec, Error, Predictor, AnyRef, Any
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  1. ANOVA1
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Instance Constructors

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

    t

    the binary/categorical treatment variable vector double with integer values

    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: VectoD = 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

    Definition Classes
    PredictorVec
  9. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  10. def crossVal(ord: Int, k: Int = 10, rando: Boolean = true): Unit

    Perform 'k'-fold cross-validation.

    Perform 'k'-fold cross-validation.

    ord

    the number of treatment levels

    k

    the number of folds

    rando

    whether to use randomized cross-validation

    Definition Classes
    ANOVA1 → PredictorVec
  11. def crossValidate(algor: (VectoD, VectoD, Int) ⇒ PredictorVec, k: Int = 10, rando: Boolean = true): Array[Statistic]
    Definition Classes
    PredictorVec
  12. val e: VectoD
    Attributes
    protected
    Definition Classes
    Predictor
  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. def eval(tt: VectoD, yy: VectoD): Unit

    Compute the error and useful diagnostics for the test dataset.

    Compute the error and useful diagnostics for the test dataset.

    yy

    the test response vector

    Definition Classes
    PredictorVec
  16. def eval(): Unit

    Compute the error and useful diagnostics for the entire dataset.

    Compute the error and useful diagnostics for the entire dataset.

    Definition Classes
    PredictorVec → Predictor
  17. def eval(xx: MatriD, yy: VectoD): Unit

    Compute the error and useful diagnostics for the test dataset.

    Compute the error and useful diagnostics for the test dataset.

    xx

    the test data matrix

    yy

    the test response vector FIX - implement in classes

    Definition Classes
    Predictor
  18. def expand(t: Double): VectoD

    Definition Classes
    ANOVA1 → PredictorVec
  19. def expand(t: VectoD): MatriD

    Expand the vector 't' into a matrix.

    Expand the vector 't' into a matrix.

    t

    the vector to expand into the matrix

    Definition Classes
    PredictorVec
  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 measures including 'rSq'.

    Return the quality of fit measures including 'rSq'.

    Definition Classes
    PredictorVec
  22. def fitLabel: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit.

    Definition Classes
    PredictorVec
  23. def fitMap: Map[String, String]

    Build a map of quality of fit measures.

    Build a map of quality of fit measures.

    Definition Classes
    PredictorVec
  24. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  25. 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
    PredictorVec
  26. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  27. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  28. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  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 parameter: VectoD

    Return the vector of parameters/coefficients.

    Return the vector of parameters/coefficients.

    Definition Classes
    PredictorVec → Predictor
  33. def predict(z: Double): 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
    ANOVA1 → PredictorVec
  34. 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 expanded/orhogonalized vector to predict

    Definition Classes
    PredictorVec → Predictor
  35. 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
  36. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    PredictorVec → Predictor
  37. var rg: Regression
    Attributes
    protected
    Definition Classes
    PredictorVec
  38. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  39. def toString(): String
    Definition Classes
    AnyRef → Any
  40. def train(yy: VectoD = y): Regression

    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
    ANOVA1 → PredictorVec → Predictor
  41. 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
    PredictorVec
  42. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  43. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  44. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  45. val x: MatrixD

Inherited from PredictorVec

Inherited from Error

Inherited from Predictor

Inherited from AnyRef

Inherited from Any

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