scalation.analytics

PoissonRegression

Related Doc: package analytics

class PoissonRegression extends Classifier with Error

The PoissonRegression class supports Poisson regression. In this case, x' may be multi-dimensional '[1, x_1, ... x_k]'. Fit the parameter vector 'b' in the Poisson regression equation

log (mu(x)) = b dot x = b_0 + b_1 * x_1 + ... b_k * x_k

where 'e' represents the residuals (the part not explained by the model) and 'y' is now integer valued.

See also

see.stanford.edu/materials/lsoeldsee263/05-ls.pdf

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Instance Constructors

  1. new PoissonRegression(x: MatrixD, y: VectorI, cn: Array[String] = null)

    x

    the input/design matrix augmented with a first column of ones

    y

    the integer response vector, y_i in {0, 1, ... }

    cn

    the optional names for all categories/classes

Value Members

  1. final def !=(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

    Definition Classes
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  3. final def ==(arg0: Any): Boolean

    Definition Classes
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  4. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  5. def classify(z: VectorI): (Int, String)

    Classify the value of y = f(z) by evaluating the formula y = exp (b dot z), for an integer vector.

    Classify the value of y = f(z) by evaluating the formula y = exp (b dot z), for an integer vector.

    z

    the new integer vector to classify

    Definition Classes
    PoissonRegression → Classifier

  6. def classify(z: VectorD): (Int, String)

    Classify the value of y = f(z) by evaluating the formula y = exp (b dot z).

    Classify the value of y = f(z) by evaluating the formula y = exp (b dot z).

    z

    the new vector to classify

    Definition Classes
    PoissonRegression → Classifier

  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
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    @throws( ... )

  8. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  10. def finalize(): Unit

    Attributes
    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )

  11. def fit: (VectorD, Double, Double, Double, Double)

    Return the fit (parameter vector b, quality of fit).

    Return the fit (parameter vector b, quality of fit). Assumes both train_null and train have already been called.

  12. 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

  13. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any

  14. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  15. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  16. def ll(b: VectorD): Double

    For a given parameter vector b, compute - Log-Likelihood (-LL).

    For a given parameter vector b, compute - Log-Likelihood (-LL). -LL is the standard measure.

    b

    the parameters to fit

    See also

    dept.stat.lsa.umich.edu/~kshedden/Courses/Stat600/Notes/glm.pdf

  17. def ll_null(b: VectorD): Double

    For a given parameter vector b = [b(0)], compute -2 * Log-Likelihood (-2LL).

    For a given parameter vector b = [b(0)], compute -2 * Log-Likelihood (-2LL). -2LL is the standard measure that follows a Chi-Square distribution.

    b

    the parameters to fit

    See also

    dept.stat.lsa.umich.edu/~kshedden/Courses/Stat600/Notes/glm.pdf

  18. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  19. final def notify(): Unit

    Definition Classes
    AnyRef

  20. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  21. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef

  22. def toString(): String

    Definition Classes
    AnyRef → Any

  23. def train(): Unit

    For the full model, train the classifier by fitting the parameter vector (b-vector) in the logistic regression equation using maximum likelihood.

    For the full model, train the classifier by fitting the parameter vector (b-vector) in the logistic regression equation using maximum likelihood. Do this by minimizing -2LL.

    Definition Classes
    PoissonRegression → Classifier

  24. def train_null(): Unit

    For the null model, train the classifier by fitting the parameter vector (b-vector) in the logistic regression equation using maximum likelihood.

    For the null model, train the classifier by fitting the parameter vector (b-vector) in the logistic regression equation using maximum likelihood. Do this by minimizing -2LL.

  25. final def wait(): Unit

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    AnyRef
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    @throws( ... )

  26. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )

  27. final def wait(arg0: Long): Unit

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    @throws( ... )

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