Packages

c

scalation.analytics

PoissonRegression

class PoissonRegression extends Predictor 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

Linear Supertypes
Error, Predictor, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. PoissonRegression
  2. Error
  3. Predictor
  4. AnyRef
  5. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new PoissonRegression(x: MatrixD, y: VectorI, fn: 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, ... }

    fn

    the names of the features/variable

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. val b: VectoD

    Coefficient/parameter vector [b_0, b_1, ...

    Coefficient/parameter vector [b_0, b_1, ... b_k]

    Attributes
    protected
    Definition Classes
    Predictor
  6. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  7. def coefficient: VectoD

    Return the vector of coefficient/parameter values.

    Return the vector of coefficient/parameter values.

    Definition Classes
    Predictor
  8. val e: VectoD

    Residual/error vector [e_0, e_1, ...

    Residual/error vector [e_0, e_1, ... e_m-1]

    Attributes
    protected
    Definition Classes
    Predictor
  9. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  10. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  11. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  12. def fit: VectorD

    Return the quality of fit including 'rSquared'.

    Return the quality of fit including 'rSquared'. Assumes both train_null and train have already been called.

    Definition Classes
    PoissonRegressionPredictor
  13. def fitLabels: Array[String]

    Return the labels for the fit.

    Return the labels for the fit. Override when necessary.

    Definition Classes
    Predictor
  14. final 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
  15. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  16. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  17. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  18. def ll(b: VectoD): 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

  19. 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

  20. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  21. final def notify(): Unit
    Definition Classes
    AnyRef
  22. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  23. def predict(z: VectoD): Double

    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 predict

    Definition Classes
    PoissonRegressionPredictor
  24. 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
  25. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    Predictor
  26. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  27. def toString(): String
    Definition Classes
    AnyRef → Any
  28. 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
    PoissonRegressionPredictor
  29. 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'.

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

Inherited from Error

Inherited from Predictor

Inherited from AnyRef

Inherited from Any

Ungrouped