Imputation
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
Attributes
- Graph
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- Supertypes
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class Objecttrait Matchableclass Any
- Known subtypes
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object ImputeBackwardobject ImputeForwardobject ImputeMeanobject ImputeMovingAvgobject ImputeNormalobject ImputeNormalWinobject ImputeRegressionShow all
Members list
Value members
Abstract methods
Concrete methods
Return the index of last missing value in vector x from index i and the new imputed value.
Return the index of last missing value in vector x from index i and the new imputed value.
Value parameters
- i_
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the starting index to look for missing value
- x
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the vector with missing values
Attributes
Return the index of first missing value in vector x from index i and the new imputed value.
Return the index of first missing value in vector x from index i and the new imputed value.
Value parameters
- i
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the starting index to look for missing value
- x
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the vector with missing values
Attributes
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.
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.
Value parameters
- i
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the starting index to look for missing values
- x
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the vector with missing values
Attributes
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.
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.
Value parameters
- x
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the matrix with missing values
Attributes
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.
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.
Value parameters
- x
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the vector with missing values
Attributes
Return the next non-missing value in vector x from index i. If none, return missVal.
Return the next non-missing value in vector x from index i. If none, return missVal.
Value parameters
- i
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the starting index to look for non-missing value
- x
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the vector to be searched for a non-missing value
Attributes
Return the median of three normally distributed random numbers.
Return the median of three normally distributed random numbers.
Value parameters
- mu
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the mean
- sig2
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the variance
Attributes
Return the previous non-missing value in vector x from index i. If none, return missVal.
Return the previous non-missing value in vector x from index i. If none, return missVal.
Value parameters
- i
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the starting index to look for non-missing value
- x
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the vector to be searched (backwards) for a non-missing value
Attributes
Set the distance dist to the new value dist_.
Set the distance dist to the new value dist_.
Value parameters
- dist_
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the new value for the distance
Attributes
Set the missing value missVal to the new missing value indicator missVal_.
Set the missing value missVal to the new missing value indicator missVal_.
Value parameters
- missVal_
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the new missing value indicator