Packages

trait Imputation extends AnyRef

The Imputation trait specifies an imputation operation called 'impute' to be defined by the objects implementing it, i.e., ImputeRegression - impute missing values using SimpleRegression ImputeForward - impute missing values using previous values and slopes ImputeBackward - impute missing values using subsequent values and slopes ImputeMean - impute missing values usind the filtered mean ImputeNormal - impute missing values using the median of Normal random variates ImputeMovingAvg - impute missing values using the moving average ImputeNormalWin - impute missing values using the median of Normal random variates for a window

Linear Supertypes
AnyRef, Any
Known Subclasses
ImputeBackward, ImputeForward, ImputeMean, ImputeMovingAvg, ImputeNormal, ImputeNormalWin, ImputeRegression
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Abstract Value Members

  1. abstract def imputeAt(x: VectoD, i: Int): Double

    Impute a value for vector 'x' at index 'i'.

    Impute a value for vector 'x' at index 'i'. Does not modify the vector.

    x

    the vector with missing values

    i

    the index position for which to impute a value

Concrete Value Members

  1. def impute(x: MatriD): MatriD

    Replace all missing values in matrix 'x' with imputed values.

    Replace all missing values in matrix 'x' with imputed values. Will change the values in matrix 'x'. Make a copy to preserve values 'x.copy'.

    x

    the matrix with missing values

  2. def impute(x: VectoD, i: Int = 0): (Int, Double)

    Impute a value for the first missing value in vector 'x' from index 'i'.

    Impute a value for the first missing value in vector 'x' from index 'i'. The type (Int, Double) returns (vector index for imputation, imputed value). Does not modify the vector.

    x

    the vector with missing values

    i

    the starting index to look for missing values

  3. def imputeAll(x: VectoD): VectoD

    Replace all missing values in vector 'x' with imputed values.

    Replace all missing values in vector 'x' with imputed values. Will change the values in vector 'x'. Make a copy to preserve values 'x.copy'.

    x

    the vector with missing values

  4. def imputeCol(c: Vec, i: Int = 0): (Int, Any)

    Impute a value for the first missing value in column 'c' from index 'i'.

    Impute a value for the first missing value in column 'c' from index 'i'. The type (Int, Double) returns (vector index for imputation, imputed value). Does not modify the column.

    c

    the column with missing values

    i

    the starting index to look for missing values

  5. def setDist(dist_: Int): Unit

    Set the distance 'dist' to the new value 'dist_'.

    Set the distance 'dist' to the new value 'dist_'.

    dist_

    the new value for the distance

  6. def setMissVal(missVal_: Double): Unit

    Set the missing value 'missVal' to the new missing value indicator 'missVal_'.

    Set the missing value 'missVal' to the new missing value indicator 'missVal_'.

    missVal_

    the new missing value indicator