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
- Alphabetic
- By Inheritance
- PoissonRegression
- Error
- Predictor
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Instance Constructors
-
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
-
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
- @throws( ... )
-
def
coefficient: VectoD
Return the vector of coefficient/parameter values.
Return the vector of coefficient/parameter values.
- Definition Classes
- Predictor
-
def
diagnose(yy: VectoD): Unit
Compute diagostics for the predictor.
Compute diagostics for the predictor. Override to add more diagostics. Note, for 'rmse', 'sse' is divided by the number of instances 'm' rather than degrees of freedom.
- yy
the response vector
- Definition Classes
- Predictor
- See also
en.wikipedia.org/wiki/Mean_squared_error
-
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
finalize(): Unit
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
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
- PoissonRegression → Predictor
-
def
fitLabels: Seq[String]
Return the labels for the fit.
Return the labels for the fit.
- Definition Classes
- PoissonRegression → Predictor
-
final
def
flaw(method: String, message: String): Unit
- Definition Classes
- Error
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
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: 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
-
val
mae: Double
- Attributes
- protected
- Definition Classes
- Predictor
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
-
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 → Predictor
-
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
-
val
rSq: Double
- Attributes
- protected
- Definition Classes
- Predictor
-
def
residual: VectoD
Return the vector of residuals/errors.
Return the vector of residuals/errors.
- Definition Classes
- Predictor
-
val
rmse: Double
- Attributes
- protected
- Definition Classes
- Predictor
-
val
sse: Double
- Attributes
- protected
- Definition Classes
- Predictor
-
val
ssr: Double
- Attributes
- protected
- Definition Classes
- Predictor
-
val
sst: Double
- Attributes
- protected
- Definition Classes
- Predictor
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
def
train(): Unit
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. Do this by minimizing '-2LL'.
- Definition Classes
- PoissonRegression → Predictor
-
def
train(yy: VectoD): Unit
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 → Predictor
-
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
- @throws( ... )