class ExpRegression extends Predictor with Error
The ExpRegression class supports exponential regression. In this case,
'x' is multi-dimensional [1, x_1, ... x_k]. Fit the parameter vector 'b' in the
exponential regression equation
log (mu (x)) = b dot x = b_0 + b_1 * x_1 + ... b_k * x_k
- See also
www.stat.uni-muenchen.de/~leiten/Lehre/Material/GLM_0708/chapterGLM.pdf
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new
ExpRegression(x: MatrixD, nonneg: Boolean, y: VectorD)
- x
the data/design matrix
- nonneg
whether to check that responses are nonnegative
- y
the response vector
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
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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
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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.
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- Predictor
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def
diagnose(yy: VectoD): Unit
Compute diagostics for the regression model.
Compute diagostics for the regression model.
- yy
the response vector
- Definition Classes
- ExpRegression → Predictor
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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
finalize(): Unit
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def
fit: VectorD
Return the quality of fit.
Return the quality of fit.
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- ExpRegression → Predictor
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def
fitLabels: Seq[String]
Return the labels for the fit.
Return the labels for the fit.
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- ExpRegression → Predictor
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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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def
ll(b: VectorD): Double
For a given parameter vector b, compute '-2 * Log-Likelihood' (-2LL).
For a given parameter vector b, compute '-2 * Log-Likelihood' (-2LL). '-2LL' is the standard measure that follows a Chi-Square distribution.
- b
the parameters to fit
- See also
www.statisticalhorizons.com/wp-content/uploads/Allison.StatComp.pdf
www.stat.cmu.edu/~cshalizi/350/lectures/26/lecture-26.pdf
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def
ll_null(b: VectorD): Double
For a given parameter vector b, compute '-2 * Log-Likelihood' (-2LL) for the null model (the one that does not consider the effects of x(i)).
For a given parameter vector b, compute '-2 * Log-Likelihood' (-2LL) for the null model (the one that does not consider the effects of x(i)).
- b
the parameters to fit
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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).
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
- ExpRegression → 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
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val
rSq: Double
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def
residual: VectoD
Return the vector of residuals/errors.
Return the vector of residuals/errors.
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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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final
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 exponential regression equation.
Train the predictor by fitting the parameter vector (b-vector) in the exponential regression equation.
- Definition Classes
- ExpRegression → Predictor
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def
train(yy: VectoD): Unit
Train the predictor by fitting the parameter vector (b-vector) in the exponential regression equation.
Train the predictor by fitting the parameter vector (b-vector) in the exponential regression equation.
- yy
the response vector
- Definition Classes
- ExpRegression → Predictor
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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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