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
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  1. PoissonRegression
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Instance Constructors

  1. new PoissonRegression(x: MatriD, y: VectoI, 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
    Attributes
    protected
    Definition Classes
    Predictor
  6. def build(x: MatriD, y: VectoD): Predictor
    Definition Classes
    Predictor
  7. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  8. def coefficient: VectoD

    Return the vector of coefficient/parameter values.

    Return the vector of coefficient/parameter values.

    Definition Classes
    Predictor
  9. def diagnose(yy: VectoD): Unit

    Compute diagostics for the predictor.

    Compute diagostics for the predictor. Override to add more diagostics. Note, for 'mse' and 'rmse', 'sse' is divided by the number of instances 'm' rather than the degrees of freedom.

    yy

    the response vector

    Attributes
    protected
    Definition Classes
    Predictor
    See also

    en.wikipedia.org/wiki/Mean_squared_error

  10. val e: VectoD
    Attributes
    protected
    Definition Classes
    Predictor
  11. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  13. def eval(yy: VectoD = y.toDouble): Unit

    Compute the error and useful diagnostics.

    Compute the error and useful diagnostics.

    yy

    the response vector

    Definition Classes
    PoissonRegressionPredictor
  14. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  15. def fit: VectoD

    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
  16. def fitLabels: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit.

    Definition Classes
    PoissonRegressionPredictor
  17. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  18. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  19. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  20. val index_rSq: Int
    Definition Classes
    Predictor
  21. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  22. 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

  23. def ll_null(b: VectoD): 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

  24. val mae: Double
    Attributes
    protected
    Definition Classes
    Predictor
  25. def metrics: Map[String, Any]

    Build a map of selected quality of fit measures/metrics.

    Build a map of selected quality of fit measures/metrics.

    Definition Classes
    Predictor
  26. val mse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  27. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  28. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  29. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  30. 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
  31. 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
    Predictor
  32. val rSq: Double
    Attributes
    protected
    Definition Classes
    Predictor
  33. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    Predictor
  34. val rmse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  35. val sse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  36. val ssr: Double
    Attributes
    protected
    Definition Classes
    Predictor
  37. val sst: Double
    Attributes
    protected
    Definition Classes
    Predictor
  38. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  39. def toString(): String
    Definition Classes
    AnyRef → Any
  40. def train(yy: VectoD = y.toDouble): PoissonRegression

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

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

    yy

    the response vector

    Definition Classes
    PoissonRegressionPredictor
  41. def train_null(): Unit

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

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

  42. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  43. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  44. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Inherited from Error

Inherited from Predictor

Inherited from AnyRef

Inherited from Any

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