class ANOVA extends Predictor with Error
The ANOVA class supports one-way ANalysis Of VAriance (ANOVA). 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 fit the parameter vector
b = x_pinv * y
where 'x_pinv' is the pseudo-inverse.
- See also
psych.colorado.edu/~carey/Courses/PSYC5741/handouts/GLM%20Theory.pdf
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new
ANOVA(t: VectorI, y: VectorD, levels: Int, technique: RegTechnique = QR)
- t
the treatment/categorical 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
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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def
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final
def
asInstanceOf[T0]: T0
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def
assignDummyVars(): Unit
Assign values for the dummy variables based on the treatment vector 't'.
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val
b: VectoD
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def
backElim(): (Int, VectoD, VectorD)
Perform backward elimination to remove the least predictive variable from the model, returning the variable to eliminate, the new parameter vector, the new R-squared value and the new F statistic.
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def
clone(): AnyRef
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def
coefficient: VectoD
Return the vector of coefficient/parameter values.
Return the vector of coefficient/parameter values.
- Definition Classes
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def
diagnose(yy: VectoD): Unit
Compute diagostics for the predictor.
Compute diagostics for the predictor. Override to add more diagostics. Note, for 'rmse', 'sse' is divided by the number of instances 'm' rather than degrees of freedom.
- yy
the response vector
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- See also
en.wikipedia.org/wiki/Mean_squared_error
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val
e: VectoD
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final
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eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
finalize(): Unit
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def
fit: VectorD
Return the quality of fit.
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def
fitLabels: Seq[String]
Return the labels for the fit.
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final
def
flaw(method: String, message: String): Unit
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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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val
mae: Double
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final
def
ne(arg0: AnyRef): Boolean
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final
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).
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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
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val
rSq: Double
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def
residual: VectoD
Return the vector of residuals/errors.
- val rg: Regression[MatrixD, VectorD]
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val
rmse: Double
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val
sse: Double
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val
ssr: Double
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val
sst: Double
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def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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def
train(): Unit
Train the predictor by fitting the parameter vector (b-vector) in the regression equation using the least squares method on 'y'.
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def
train(yy: VectoD): Unit
Train the predictor by fitting the parameter vector (b-vector) in the multiple regression equation
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def
vif: VectorD
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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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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- val x: MatrixD