mean_impute

Replace missing values of data set by the mean of other observed values.

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

mean_impute function

<dl><dt>X</dt>
<dd>An \(n\) x \(d\) matrix or data frame where \(n\) is the number of
observations and \(d\) is the number of columns or variables. Alternately,
<code>X</code> can be a vector of \(n\) observations.</dd></dl>

Arguments

Mean Imputation — mean_impute

<dl>

<dt>X</dt>
<dd>An \(n\) x \(d\) matrix or data frame where \(n\) is the number of
observations and \(d\) is the number of columns or variables. Alternately,
<code>X</code> can be a vector of \(n\) observations.</dd>

</dl>

mean_impute: Mean Imputation

Description

Usage

Value

Arguments

References

Examples