scalation.analytics

LogitRegression

class LogitRegression extends Predictor with Error

The LogitRegression class supports logit regression. In this case, 'x' may be multi-dimensional [1, x1, ... xk]. Fit the parameter vector 'b' in the logit regression equation

y = b dot x + e = b0 + b1 * x1 + ... bk * xk + e

where 'e' represents the residuals (the part not explained by the model) and y is now binary.

See also

see.stanford.edu/materials/lsoeldsee263/05-ls.pdf

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Instance Constructors

  1. new LogitRegression(x: MatrixD, y: VectorI)

    x

    the input/design matrix augmented with a first column of ones

    y

    the binary response vector, y_i in {0, 1}

Value Members

  1. final def !=(arg0: AnyRef): Boolean

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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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  8. final def eq(arg0: AnyRef): Boolean

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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  11. def fit: (VectorD, Double, Double, Double)

    Return the fit (parameter vector b, quality of fit rSquared)

  12. def flaw(method: String, message: String): Unit

    Show the flaw by printing the error message.

    Show the flaw by printing the error message.

    method

    the method where the error occurred

    message

    the error message

    Definition Classes
    Error
  13. final def getClass(): Class[_]

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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

  17. def logit(p: Double): Double

    Compute the log of the odds of an event ocurring (e.

    Compute the log of the odds of an event ocurring (e.g., success, 1).

    p

    the probability, a number between 0 and 1.

  18. def logitInv(a: Double): Double

    Compute the inverse of the logit function.

    Compute the inverse of the logit function.

    a

    the logit value

  19. final def ne(arg0: AnyRef): Boolean

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  20. final def notify(): Unit

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  21. final def notifyAll(): Unit

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  22. def predict(z: MatrixD): VectorD

    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
    LogitRegressionPredictor
  23. def predict(z: VectorD): Double

    Predict the value of y = f(z) by evaluating the formula y = b dot z, i.

    Predict the value of y = f(z) by evaluating the formula y = b dot z, i.e., (b0, b1) dot (1., z1).

    z

    the new vector to predict

    Definition Classes
    LogitRegressionPredictor
  24. 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

    Definition Classes
    Predictor
  25. final def synchronized[T0](arg0: ⇒ T0): T0

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  26. def toString(): String

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  27. def train(): Unit

    Train the predictor by fitting the parameter vector (b-vector) in the logit regression equation using maximum likelihood.

    Train the predictor by fitting the parameter vector (b-vector) in the logit regression equation using maximum likelihood. Do this by minimizing -2LL.

    Definition Classes
    LogitRegressionPredictor
  28. final def wait(): Unit

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  29. final def wait(arg0: Long, arg1: Int): Unit

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  30. final def wait(arg0: Long): Unit

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