generate_patterns

Generate all possible missing patterns in a multivariate data set. The function
can be used to complement the function <code>ampute()</code> from package <code>mice</code>
in which a matrix of patterns is needed to allow for general missing-data
patterns with missing-data mechanism missing at random (MAR). Using this
function, each observation can have more than one missing value.

Implementations of various robust and flexible model-based clustering methods for data sets with missing values at random.
Two main models are: Multivariate Contaminated Normal Mixture (MCNM, Tong and Tortora, 2022, <doi:10.1007/s11634-021-00476-1>) and
Multivariate Generalized Hyperbolic Mixture (MGHM, Wei et al., 2019, <doi:10.1016/j.csda.2018.08.016>). Mixtures via some special or limiting
cases of the multivariate generalized hyperbolic distribution are also included: Normal-Inverse Gaussian, Symmetric Normal-Inverse Gaussian,
Skew-Cauchy, Cauchy, Skew-t, Student's t, Normal, Symmetric Generalized Hyperbolic, Hyperbolic Univariate Marginals,
Hyperbolic, and Symmetric Hyperbolic.

Hung Tong

MixtureMissing

Robust and Flexible Model-Based Clustering for Data Sets with
Missing Values at Random

Cristina Tortora

generate_patterns function

<dl><dt>d</dt>
<dd>The number of variables or columns of the data set. <code>d</code> must be
an integer greater than 1.</dd></dl>

Arguments

Missing-Data Pattern Generation — generate_patterns

<dl>

<dt>d</dt>
<dd>The number of variables or columns of the data set. <code>d</code> must be
an integer greater than 1.</dd>

</dl>

generate_patterns: Missing-Data Pattern Generation

Description

Usage

Value

Arguments

Details

Examples