abstract class PredictorVec extends Predictor with Error
The PredictorVec class supports term expanded regression.
Fit the parameter vector 'b' in the regression equation.
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 (.)
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Instance Constructors
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new
PredictorVec(t: VectoD, y: VectoD, ord: Int)
- t
the input vector: t_i expands to x_i = vector
- y
the response vector
- ord
the order of the expansion
Abstract Value Members
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abstract
def
crossVal(k: Int = 10, ord: Int = 10): Unit
The 'crossVal' abstract method must be coded in implementing classes to call the above 'crossValidate' method.
The 'crossVal' abstract method must be coded in implementing classes to call the above 'crossValidate' method. The 'algor' parameter may be specified as a lambda function to create the prediction algorithm.
- k
the number of crosses and cross-validations (defaults to 10x).
- ord
the given order
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abstract
def
expand(t: Double): VectoD
Expand the scalar 't' into a vector of terms/columns.
Expand the scalar 't' into a vector of terms/columns.
- t
the scalar to expand into the vector
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abstract
def
predict(z: Double): Double
Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot expand (z)', e.g., '(b_0, b_1, b_2) dot (1, z, z^2)'.
Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot expand (z)', e.g., '(b_0, b_1, b_2) dot (1, z, z^2)'.
- z
the new scalar to predict
Concrete Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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val
b: VectoD
- Attributes
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- Definition Classes
- Predictor
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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
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def
clone(): AnyRef
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def
coefficient: VectoD
Return the vector of coefficients.
Return the vector of coefficients.
- Definition Classes
- PredictorVec → Predictor
- def crossValidate(algor: (VectoD, VectoD, Int) ⇒ PredictorVec, ord: Int, k: Int = 10): Array[Statistic]
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val
e: VectoD
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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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
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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
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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
-
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
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def
finalize(): Unit
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def
fit: VectoD
Return the quality of fit measures including 'rSq'.
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def
fitLabel: Seq[String]
Return the labels for the fit.
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def
fitMap: Map[String, String]
Build a map of quality of fit measures.
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final
def
flaw(method: String, message: String): Unit
- Definition Classes
- Error
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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
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final
def
getClass(): Class[_]
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def
hashCode(): Int
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final
def
isInstanceOf[T0]: Boolean
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def
ne(arg0: AnyRef): Boolean
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def
notify(): Unit
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final
def
notifyAll(): Unit
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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
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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
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def
residual: VectoD
Return the vector of residuals/errors.
Return the vector of residuals/errors.
- Definition Classes
- PredictorVec → Predictor
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var
rg: Regression
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- protected
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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def
train(yy: VectoD = y): Regression
Train the predictor by fitting the parameter vector 'b' in the multiple regression equation using the least squares method.
Train the predictor by fitting the parameter vector 'b' in the multiple regression equation using the least squares method.
- yy
the response vector
- Definition Classes
- PredictorVec → Predictor
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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.
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def
wait(): Unit
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def
wait(arg0: Long, arg1: Int): Unit
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wait(arg0: Long): Unit
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