Packages

c

scalation.analytics

ExpRegression

class ExpRegression extends Predictor with Error

The ExpRegression class supports exponential regression. In this case, 'x' is multi-dimensional [1, x_1, ... x_k]. Fit the parameter vector 'b' in the exponential regression equation

log (mu (x)) = b dot x = b_0 + b_1 * x_1 + ... b_k * x_k

See also

www.stat.uni-muenchen.de/~leiten/Lehre/Material/GLM_0708/chapterGLM.pdf

Linear Supertypes
Error, Predictor, AnyRef, Any
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Instance Constructors

  1. new ExpRegression(x: MatrixD, nonneg: Boolean, y: VectorD)

    x

    the data/design matrix

    nonneg

    whether to check that responses are nonnegative

    y

    the response vector

Value Members

  1. def coefficient: VectoD

    Return the vector of coefficient/parameter values.

    Return the vector of coefficient/parameter values.

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

    Compute diagostics for the regression model.

    Compute diagostics for the regression model.

    yy

    the response vector

    Definition Classes
    ExpRegression → Predictor
  3. def fit: VectorD

    Return the quality of fit.

    Return the quality of fit.

    Definition Classes
    ExpRegression → Predictor
  4. def fitLabels: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit.

    Definition Classes
    ExpRegression → Predictor
  5. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  6. def ll(b: VectorD): Double

    For a given parameter vector b, compute '-2 * Log-Likelihood' (-2LL).

    For a given parameter vector b, compute '-2 * Log-Likelihood' (-2LL). '-2LL' is the standard measure that follows a Chi-Square distribution.

    b

    the parameters to fit

    See also

    www.statisticalhorizons.com/wp-content/uploads/Allison.StatComp.pdf

    www.stat.cmu.edu/~cshalizi/350/lectures/26/lecture-26.pdf

  7. def ll_null(b: VectorD): Double

    For a given parameter vector b, compute '-2 * Log-Likelihood' (-2LL) for the null model (the one that does not consider the effects of x(i)).

    For a given parameter vector b, compute '-2 * Log-Likelihood' (-2LL) for the null model (the one that does not consider the effects of x(i)).

    b

    the parameters to fit

  8. def predict(z: VectoD): Double

    Predict the value of y = f(z) by evaluating the formula y = b dot z, e.g., (b_0, b_1, b_2) dot (1, z_1, z_2).

    Predict the value of y = f(z) by evaluating the formula y = b dot z, e.g., (b_0, b_1, b_2) dot (1, z_1, z_2).

    z

    the new vector to predict

    Definition Classes
    ExpRegression → Predictor
  9. 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
  10. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    Predictor
  11. def train(): Unit

    Train the predictor by fitting the parameter vector (b-vector) in the exponential regression equation.

    Train the predictor by fitting the parameter vector (b-vector) in the exponential regression equation.

    Definition Classes
    ExpRegression → Predictor
  12. def train(yy: VectoD): Unit

    Train the predictor by fitting the parameter vector (b-vector) in the exponential regression equation.

    Train the predictor by fitting the parameter vector (b-vector) in the exponential regression equation.

    yy

    the response vector

    Definition Classes
    ExpRegression → Predictor