impute_simple: Simple imputation

In antonvsdata/notame: Workflow for non-targeted LC-MS metabolic profiling

Simple imputation

Description

Impute missing values using a simple imputation strategy. All missing values of a feature are imputed with the same value. It is possible to only impute features with a large number of missing values this way. This can be useful for using this function before random forest imputation to speed things up. The imputation strategies available are:

• a numeric value: impute all missing values in all features with the same value, e.g. 1

• "mean": impute missing values of a feature with the mean of observed values of that feature

• "median": impute missing values of a feature with the median of observed values of that feature

• "min": impute missing values of a feature with the minimum observed value of that feature

• "half_min": impute missing values of a feature with half the minimum observed value of that feature

• "small_random": impute missing values of a feature with random numbers between 0 and the minimum of that feature (uniform distribution, remember to set the seed number!).

Usage

impute_simple(object, value, na_limit = 0)

Arguments

object	a MetaboSet object
value	the value used for imputation, either a numeric or one of "min", "half_min", "small_random", see above
na_limit	only impute features with the proportion of NAs over this limit. For example, if na_limit = 0.5, only features with at least half of the values missing are imputed.

Examples

missing <- mark_nas(merged_sample, 0)
imputed <- impute_simple(missing, value = "min")

antonvsdata/notame documentation built on Sept. 14, 2024, 11:09 p.m.