Packages

class PoissonRegression extends PredictorMat

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
PredictorMat, Error, Predictor, Fit, AnyRef, Any
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Inherited
  1. PoissonRegression
  2. PredictorMat
  3. Error
  4. Predictor
  5. Fit
  6. AnyRef
  7. Any
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Instance Constructors

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

    Return the vector of coefficient/parameter values.

    Return the vector of coefficient/parameter values.

    Definition Classes
    Predictor
  8. def crossVal(k: Int = 10): Unit

    Perform 'k'-fold cross-validation.

    Perform 'k'-fold cross-validation.

    k

    the number of folds

    Definition Classes
    PoissonRegression → PredictorMat
  9. def crossValidate(algor: (MatriD, VectoD) ⇒ PredictorMat, k: Int = 10): Array[Statistic]
    Definition Classes
    PredictorMat
  10. val df: (Double, Double)
    Definition Classes
    Fit
  11. def diagnose(e: VectoD, w: VectoD = null, yp: VectoD = null): Unit

    Given the error/residual vector, compute the quality of fit measures.

    Given the error/residual vector, compute the quality of fit measures.

    e

    the corresponding m-dimensional error vector (y - yp)

    w

    the weights on the instances

    yp

    the predicted response vector (x * b)

    Definition Classes
    Fit
  12. val e: VectoD
    Attributes
    protected
    Definition Classes
    Predictor
  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. def eval(): Unit

    Compute the error and useful diagnostics.

    Compute the error and useful diagnostics. FIX - not x * b

    Definition Classes
    PoissonRegression → PredictorMat → Predictor
  16. def eval(xx: MatriD, yy: VectoD): Unit

    Compute the error and useful diagnostics for the test dataset.

    Compute the error and useful diagnostics for the test dataset.

    xx

    the test data matrix

    yy

    the test response vector

    Definition Classes
    PredictorMat → Predictor
  17. def f_(z: Double): String

    Format a double value.

    Format a double value.

    z

    the double value to format

    Definition Classes
    Fit
  18. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  19. 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
    PoissonRegression → Fit
  20. def fitLabel: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit.

    Definition Classes
    PoissonRegression → Fit
  21. def fitMap: Map[String, String]

    Build a map of quality of fit measures (use of LinedHashMap makes it ordered).

    Build a map of quality of fit measures (use of LinedHashMap makes it ordered). Override to add more quality of fit measures.

    Definition Classes
    Fit
  22. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  23. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  24. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  25. val index_rSq: Int
    Definition Classes
    Fit
  26. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  27. val k: Int
    Attributes
    protected
    Definition Classes
    PredictorMat
  28. 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

  29. 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

  30. val m: Int
    Attributes
    protected
    Definition Classes
    PredictorMat
  31. def mse_: Double

    Return the mean of squares for error (sse / df._2).

    Return the mean of squares for error (sse / df._2). Must call diagnose first.

    Definition Classes
    Fit
  32. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  33. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  34. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  35. 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
    PoissonRegression → PredictorMat → Predictor
  36. def predict(z: MatriD): VectoD

    Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot z', for each row of matrix 'z'.

    Predict the value of 'y = f(z)' by evaluating the formula 'y = b dot z', for each row of matrix 'z'.

    z

    the new matrix to predict

    Definition Classes
    PredictorMat
  37. 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
  38. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    Predictor
  39. def sumCoeff(b: VectoD, stdErr: VectoD = null): String

    Produce the summary report portion for the cofficients.

    Produce the summary report portion for the cofficients.

    b

    the parameters/coefficients for the model

    Definition Classes
    Fit
  40. def summary(): Unit

    Compute diagostics for the regression model.

    Compute diagostics for the regression model.

    Definition Classes
    PredictorMat
  41. def summary(b: VectoD, stdErr: VectoD = null): String

    Produce a summary report with diagnostics for each predictor 'x_j' and the overall quality of fit.

    Produce a summary report with diagnostics for each predictor 'x_j' and the overall quality of fit.

    b

    the parameters/coefficients for the model

    Definition Classes
    Fit
  42. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  43. def toString(): String
    Definition Classes
    AnyRef → Any
  44. 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
    PoissonRegression → PredictorMat → Predictor
  45. def train(): PredictorMat

    Given a set of data vectors 'x's and their corresponding responses 'y's, passed into the implementing class, train the prediction function 'y = f(x)' by fitting its parameters.

    Given a set of data vectors 'x's and their corresponding responses 'y's, passed into the implementing class, train the prediction function 'y = f(x)' by fitting its parameters.

    Definition Classes
    PredictorMat
  46. 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'.

  47. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  48. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  49. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  50. val x: MatriD
    Attributes
    protected
    Definition Classes
    PredictorMat
  51. val y: VectoD
    Attributes
    protected
    Definition Classes
    PredictorMat

Inherited from PredictorMat

Inherited from Error

Inherited from Predictor

Inherited from Fit

Inherited from AnyRef

Inherited from Any

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