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
- Alphabetic
- By Inheritance
- PoissonRegression
- PredictorMat
- Error
- Predictor
- Fit
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Instance Constructors
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
- Definition Classes
- Any
-
val
b: VectoD
- Attributes
- protected
- Definition Classes
- Predictor
-
def
clone(): AnyRef
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @native() @throws( ... )
-
def
crossVal(k: Int = 10, rando: Boolean = true): Unit
Perform 'k'-fold cross-validation.
Perform 'k'-fold cross-validation.
- k
the number of folds
- rando
whether to use randomized cross-validation
- Definition Classes
- PoissonRegression → PredictorMat
-
def
crossValidate(algor: (MatriD, VectoD) ⇒ PredictorMat, k: Int = 10, rando: Boolean = true): Array[Statistic]
- Definition Classes
- PredictorMat
-
def
diagnose(e: VectoD, w: VectoD = null, yp: VectoD = null, y_: VectoD = y): 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
-
val
e: VectoD
- Attributes
- protected
- Definition Classes
- Predictor
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
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
-
def
eval(): Unit
Compute the error and useful diagnostics for the entire dataset.
Compute the error and useful diagnostics for the entire dataset.
- Definition Classes
- PredictorMat → Predictor
-
def
finalize(): Unit
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
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
-
def
fitLabel: Seq[String]
Return the labels for the fit.
Return the labels for the fit.
- Definition Classes
- PoissonRegression → Fit
-
def
fitMap: Map[String, String]
Build a map of quality of fit measures (use of
LinedHashMapmakes it ordered).Build a map of quality of fit measures (use of
LinedHashMapmakes it ordered). Override to add more quality of fit measures.- Definition Classes
- Fit
-
final
def
flaw(method: String, message: String): Unit
- Definition Classes
- Error
-
var
fname: Strings
- Attributes
- protected
- Definition Classes
- PredictorMat
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
hparameter: HyperParameter
Return the hyper-parameters.
Return the hyper-parameters.
- Definition Classes
- PredictorMat
-
val
index_rSq: Int
- Definition Classes
- Fit
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
val
k: Int
- Attributes
- protected
- Definition Classes
- PredictorMat
-
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
-
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
-
val
m: Int
- Attributes
- protected
- Definition Classes
- PredictorMat
-
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
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
def
parameter: VectoD
Return the vector of parameter/coefficient values.
Return the vector of parameter/coefficient values.
- Definition Classes
- Predictor
-
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
-
def
predict(z: MatriD = x): 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
-
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
-
def
resetDF(df_update: (Double, Double)): Unit
Reset the degrees of freedom to the new updated values.
Reset the degrees of freedom to the new updated values. For some models, the degrees of freedom is not known until after the model is built.
- df_update
the updated degrees of freedom
- Definition Classes
- Fit
-
def
residual: VectoD
Return the vector of residuals/errors.
Return the vector of residuals/errors.
- Definition Classes
- Predictor
-
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
-
def
summary(): String
Compute and return summary diagostics for the regression model.
Compute and return summary diagostics for the regression model.
- Definition Classes
- PredictorMat
-
def
summary(b: VectoD, stdErr: VectoD = null, show: Boolean = false): 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
- show
flag indicating whether to print the summary
- Definition Classes
- Fit
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
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
-
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
-
def
train2(yy: VectoD = y): PredictorMat
- Definition Classes
- PredictorMat
-
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'.
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @throws( ... )
-
val
x: MatriD
- Attributes
- protected
- Definition Classes
- PredictorMat
-
val
y: VectoD
- Attributes
- protected
- Definition Classes
- PredictorMat