scalation.analytics

ExpRegression

Related Doc: package analytics

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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Error, Predictor, AnyRef, Any

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Instance Constructors

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

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

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Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
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@throws( ... )
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
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@throws( classOf[java.lang.Throwable] )
def fit: (VectorD, Double, Double, Double)

Return the fit (parameter vector b, quality of fit including rSquared).
def flaw(method: String, message: String): Unit

Show the flaw by printing the error message.
Show the flaw by printing the error message.
method
the method where the error occurred
message
the error message

Definition Classes
Error
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
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
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
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def predict(z: Matrix): VectorD

Predict the value of y = f(z) by evaluating the formula y = b dot zi for each row zi of matrix z.
Predict the value of y = f(z) by evaluating the formula y = b dot zi for each row zi of matrix z.
z
the new matrix to predict

Definition Classes
ExpRegression → Predictor
def predict(z: VectorD): 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
def predict(z: VectorI): 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
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
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
final def wait(): Unit

Definition Classes
AnyRef
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@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
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@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
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@throws( ... )

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Inherited from Predictor

Inherited from AnyRef

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