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
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val
b: VectoD
Coefficient/parameter vector [b_0, b_1, ...
Coefficient/parameter vector [b_0, b_1, ... b_k]
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def
coefficient: VectoD
Return the vector of coefficient/parameter values.
Return the vector of coefficient/parameter values.
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val
e: VectoD
Residual/error vector [e_0, e_1, ...
Residual/error vector [e_0, e_1, ... e_m-1]
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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.
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- PoissonRegression → Predictor
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def
fitLabels: Array[String]
Return the labels for the fit.
Return the labels for the fit. Override when necessary.
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def
flaw(method: String, message: String): Unit
Show the flaw by printing the error message.
Show the flaw by printing the error message.
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the method where the error occurred
- message
the error message
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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
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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
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notifyAll(): Unit
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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
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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
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def
residual: VectoD
Return the vector of residuals/errors.
Return the vector of residuals/errors.
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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'.
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- PoissonRegression → Predictor
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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'.
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