add_miss_cluster

data

character vector of the agglomeration method to use,
the default is "mcquitty". Options are taken from <code>stats::hclust</code>
helpfile, and options include: "ward.D", "ward.D2", "single", "complete",
"average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or
"centroid" (= UPGMC).

cluster_method

numeric the number of clusters you expect. Defaults to 2.

n_clusters

A way to extract the cluster of missingness that a group belongs to.
For example, if you use <code>vis_miss(airquality, cluster = TRUE)</code>, you can
see some clustering in the data, but you do not have a way to identify
the cluster. Future work will incorporate the <code>seriation</code> package to
allow for better control over the clustering from the user.

Missing values are ubiquitous in data and need to be explored and
handled in the initial stages of analysis. 'naniar' provides data structures
and functions that facilitate the plotting of missing values and examination
of imputations. This allows missing data dependencies to be explored with
minimal deviation from the common work patterns of 'ggplot2' and tidy data.
The work is fully discussed at Tierney & Cook (2018) <arXiv:1809.02264>.

Nicholas Tierney

naniar

Data Structures, Summaries, and Visualisations for Missing Data

Di Cook

Miles McBain

Colin Fay

Mitchell O'Hara-Wild

Jim Hester

Luke Smith

Andrew Heiss

add_miss_cluster function

<dl><dt>data</dt>
<dd>a dataframe</dd>
<dt>cluster_method</dt>
<dd>character vector of the agglomeration method to use,
the default is "mcquitty". Options are taken from <code><a href="/link/stats%3A%3Ahclust?package=naniar&version=0.6.1" data-mini-rdoc="naniar::stats::hclust">stats::hclust</a></code>
helpfile, and options include: "ward.D", "ward.D2", "single", "complete",
"average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or
"centroid" (= UPGMC).</dd>
<dt>n_clusters</dt>
<dd>numeric the number of clusters you expect. Defaults to 2.</dd></dl>

Arguments

Add a column that tells us which "missingness cluster" a row belongs to — add_miss_cluster

<dl>

<dt>data</dt>
<dd>a dataframe</dd>


<dt>cluster_method</dt>
<dd>character vector of the agglomeration method to use,
the default is "mcquitty". Options are taken from <code><a href='https://rdrr.io/r/stats/hclust.html'>stats::hclust</a></code>
helpfile, and options include: "ward.D", "ward.D2", "single", "complete",
"average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or
"centroid" (= UPGMC).</dd>


<dt>n_clusters</dt>
<dd>numeric the number of clusters you expect. Defaults to 2.</dd>

</dl>

add_miss_cluster: Add a column that tells us which "missingness cluster" a row belongs to

Description

Usage

Arguments

See Also

Examples