Imputing Missing Data

This page describes routines that deal with missing data.


Method: Value

Impute Fill Value

Description

User provides a value that is filled into missing elements.

Returns

  • Matrix with the missing values filled.

Method: Column Mean

Impute Column Mean

Description

Fills in the columnwise means calculated from the non-zero elements.

Returns

  • Matrix with the missing values filled.

Method: LOCF

Impute LOCF

Description

Return a matrix where the last valid observation is carried forward.

Returns

  • Matrix with the missing values filled.

Method: Regress

Impute Regress

Description

Return a matrix where the NaN values have been imputed by iterative regression.

There are three methods available:

  • OLS
  • Ridge {$0 \leq \alpha \leq 1$}
  • Lasso {$0 \leq \alpha \leq 1$; $\lambda > 0$}

Lasso will standardize the data.

Returns

  • Matrix with the missing values imputed.

Method: Remove Rows with NaN values

Impute Fill Value

Description

Return a matrix where every row with an NaN value is found is removed.

Returns

  • Matrix with no missing values.