impute_mode

Impute an observed mode value for every missing value

Supply functions for the creation and handling of missing
data as well as tools to evaluate missing data methods. Nearly all
possibilities of generating missing data discussed by Santos et al.
(2019) <doi:10.1109/ACCESS.2019.2891360> and some additional are
implemented. Functions are supplied to compare parameter estimates
and imputed values to true values to evaluate missing data methods.
Evaluations of these types are done, for example, by Cetin-Berber et
al. (2019) <doi:10.1177/0013164418805532> and Kim et al. (2005)
<doi:10.1093/bioinformatics/bth499>.

Tobias Rockel

missMethods

Methods for Missing Data

impute_mode function

<dl><dt>ds</dt>
<dd>A data frame or matrix with missing values.</dd>
<dt>type</dt>
<dd>A string specifying the values used for imputation; one of:
"columnwise", "rowwise", "total", "Two-Way" or "Winer" (see details).</dd>
<dt>convert_tibble</dt>
<dd>If <code>ds</code> is a tibble, should it be converted
(see section A note for tibble users).</dd></dl>

Arguments

Mode imputation — impute_mode

<dl>

<dt>ds</dt>
<dd>A data frame or matrix with missing values.</dd>


<dt>type</dt>
<dd>A string specifying the values used for imputation; one of:
"columnwise", "rowwise", "total", "Two-Way" or "Winer" (see details).</dd>


<dt>convert_tibble</dt>
<dd>If <code>ds</code> is a tibble, should it be converted
(see section A note for tibble users).</dd>

</dl>

impute_mode: Mode imputation

Description

Usage

Value

Arguments

Details

References

See Also

Examples