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Simple imputation

Source: R/transformations.R
impute_simple.Rd

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, assay.type = NULL, name = NULL)

Arguments

object

a SummarizedExperiment or 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.

assay.type

character, assay to be used in case of multiple assays

name

character, name of the resultant assay in case of multiple assays

Value

A SummarizedExperiment or Metaboset object with imputed peak table.

Examples

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