impute_missing_values

Impute missing values using K-nearest neighbors imputation

Represents high-dimensional data as tables of features, samples and measurements, and a design list for tracking the meaning of individual variables. Using this format, filtering, normalization, and other transformations of a dataset can be carried out in a flexible manner. 'romic' takes advantage of these transformations to create interactive 'shiny' apps for exploratory data analysis such as an interactive heatmap.

Sean Hackett

romic

R for High-Dimensional Omic Data

Calico Life Sciences LLC 

impute_missing_values function

<dl><dt>tomic</dt>
<dd>Either a <code>tidy_omic</code> or <code>triple_omic</code> object</dd>
<dt>impute_var_name</dt>
<dd>variable to create for imputed measurements</dd>
<dt>value_var</dt>
<dd>An abundance value to use with <code>hclust</code></dd>
<dt>...</dt>
<dd>additional arguments to pass to impute.knn</dd></dl>

Arguments

Impute Missing Values — impute_missing_values

<dl>

<dt>tomic</dt>
<dd>Either a <code>tidy_omic</code> or <code>triple_omic</code> object</dd>


<dt>impute_var_name</dt>
<dd>variable to create for imputed measurements</dd>


<dt>value_var</dt>
<dd>An abundance value to use with <code>hclust</code></dd>


<dt>...</dt>
<dd>additional arguments to pass to impute.knn</dd>

</dl>

impute_missing_values: Impute Missing Values

Description

Usage

Value

Arguments

Examples