remove_missing_values: Remove Missing Values

In calico/romic: R for High-Dimensional Omic Data

Remove Missing Values

Description

Account for missing values by dropping features, samples or using imputation.

Usage

remove_missing_values(
  tomic,
  value_var = NULL,
  missing_val_method = "drop_samples",
  missing_value_types = c("NA", "NaN", "Inf"),
  verbose = FALSE
)

Arguments

tomic	Either a tidy_omic or triple_omic object
value_var	An abundance value to use with hclust
missing_val_method	Approach to remove missing values: drop_features Drop features with missing values drop_samples Drop samples which are missing all features, then drop features impute Impute missing values
missing_value_types	Types of values to treat as missing. One or more of "NA", "NaN" and "Inf"
verbose	extra reporting messages

Value

A tomic object where missing values have been accounted for.

Examples

remove_missing_values(brauer_2008_triple)

calico/romic documentation built on Sept. 27, 2024, 7:16 p.m.