Lm - scalaglm.Lm

Instance Constructors

new Lm(y: DenseVector[Double], Xmat: DenseMatrix[Double], colNames: Seq[String], addIntercept: Boolean = true)

y
Vector of responses
Xmat
Covariate matrix
colNames
List of covariate names
addIntercept
Add an intercept term to the covariate matrix?

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
val QR: DenseQR

Breeze QR object for the design matrix
val X: DenseMatrix[Double]

Design matrix (including intercept, if required)
Design matrix (including intercept, if required)

Definition Classes
Lm → Model
val Xmat: DenseMatrix[Double]

Covariate matrix
val addIntercept: Boolean

Add an intercept term to the covariate matrix?
lazy val adjRs: Double

The adjusted R^2 value for the regression
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val coefficients: DenseVector[Double]

Fitted regression coefficients
Fitted regression coefficients

Definition Classes
Lm → Model
val colNames: Seq[String]

List of covariate names
lazy val df: Int

Degrees of freedom
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
lazy val f: Double

The f-statistic for the regression analysis
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
lazy val fitted: DenseVector[Double]

Fitted values
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
lazy val h: DenseVector[Double]

Vector containing the leverages (diagonal of the hat matrix)
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
lazy val k: Int

Degrees of freedom for the F-statistic
lazy val n: Int

Number of observations
val names: List[String]

Column names (including intercept)
Column names (including intercept)

Definition Classes
Lm → Model
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
lazy val p: DenseVector[Double]

p-values for the regression coefficients
lazy val pf: Double

The p-value associated with the f-statistic
def plots: Figure
lazy val pp: Int

Number of variables (including any intercept)
def predict(newX: DenseMatrix[Double] = Xmat): PredictLm

Predictions for a new matrix of covariates
Predictions for a new matrix of covariates
newX
New matrix of covariates
returns
Prediction object
val q: DenseMatrix[Double]

n x p Q-matrix
val qty: DenseVector[Double]

Q'y
val r: DenseMatrix[Double]

p x p upper-triangular R-matrix
lazy val rSquared: Double

The R^2 value for the regression analysis
lazy val residuals: DenseVector[Double]

Residuals
lazy val ri: DenseMatrix[Double]

The inverse of the R-matrix
lazy val rse: Double

Residual squared error
lazy val rss: Double

Residual sum of squares
lazy val se: DenseVector[Double]

Standard errors for the regression coefficients
lazy val sh: DenseVector[Double]

Square root of the leverage vector
lazy val ssy: Double

The sum-of-squares of the centred observations
lazy val studentised: DenseVector[Double]
def summary: Unit

Prints a human-readable regression summary to the console
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
lazy val t: DenseVector[Double]

t-statistics for the regression coefficients
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( ... )
val y: DenseVector[Double]

Vector of responses
lazy val ybar: Double

The mean of the observations
lazy val ymyb: DenseVector[Double]

The centred observations

Lm

Related Docs: object Lm | package scalaglm

case class Lm(y: DenseVector[Double], Xmat: DenseMatrix[Double], colNames: Seq[String], addIntercept: Boolean = true) extends Model with Product with Serializable

Instance Constructors

new Lm(y: DenseVector[Double], Xmat: DenseMatrix[Double], colNames: Seq[String], addIntercept: Boolean = true)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

val QR: DenseQR

val X: DenseMatrix[Double]

val Xmat: DenseMatrix[Double]

val addIntercept: Boolean

lazy val adjRs: Double

final def asInstanceOf[T0]: T0

def clone(): AnyRef

val coefficients: DenseVector[Double]

val colNames: Seq[String]

lazy val df: Int

final def eq(arg0: AnyRef): Boolean

lazy val f: Double

def finalize(): Unit

lazy val fitted: DenseVector[Double]

final def getClass(): Class[_]

lazy val h: DenseVector[Double]

final def isInstanceOf[T0]: Boolean

lazy val k: Int

lazy val n: Int

val names: List[String]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

lazy val p: DenseVector[Double]

lazy val pf: Double

def plots: Figure

lazy val pp: Int

def predict(newX: DenseMatrix[Double] = Xmat): PredictLm

val q: DenseMatrix[Double]

val qty: DenseVector[Double]

val r: DenseMatrix[Double]

lazy val rSquared: Double

lazy val residuals: DenseVector[Double]

lazy val ri: DenseMatrix[Double]

lazy val rse: Double

lazy val rss: Double

lazy val se: DenseVector[Double]

lazy val sh: DenseVector[Double]

lazy val ssy: Double

lazy val studentised: DenseVector[Double]

def summary: Unit

final def synchronized[T0](arg0: ⇒ T0): T0

lazy val t: DenseVector[Double]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

val y: DenseVector[Double]

lazy val ybar: Double

lazy val ymyb: DenseVector[Double]

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from Model

Inherited from AnyRef

Inherited from Any

Ungrouped