Glm

scalaglm.Glm

See theGlm companion object

case class Glm(y: DenseVector[Double], Xmat: DenseMatrix[Double], colNames: Seq[String], fam: GlmFamily, addIntercept: Boolean, its: Int) extends Model

Generalised linear regression modelling

Attributes

Xmat: Covariate matrix
addIntercept: Add an intercept term to the covariate matrix?
colNames: List of covariate names
fam: Observation model. eg. LogisticGlm or PoissonGlm
its: Max iterations for the IRLS algorithm (default 50)
y: Vector of responses
Returns:: An object of type Glm with many useful methods providing information about the regression fit, including .coefficients, .p and .summary
Companion:: object
Graph
Supertypes: trait Serializable
trait Product
trait Equals
trait Model
class Object
trait Matchable
class Any

Members list

Concise view

Value members

Concrete methods

Predictions for a new matrix of covariates

Attributes

newX: New matrix of covariates
response: Fitted values on the scale of the response?
Returns:: Prediction object

Prints a human-readable regression summary to the console

Attributes

Inherited methods

Attributes

Inherited from:: Product

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Inherited from:: Product

Concrete fields

Design matrix (including the intercept column, if required)

Attributes

Fitted regression coefficients

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Degrees of freedom

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Tuple containing results of running the IRLS algorithm - not for general use

Attributes

Number of observations

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Sequence of variable names (including the intercept)

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p-values for the regression coefficients

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Number of variables (including the intercept)

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Final Q-matrix from the IRLS algorithm

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Final R-matrix from the IRLS algorithm

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Inverse of the final R-matrix

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Standard errors for the regression coefficients

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z-statistics for the regression coefficients

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