impute_missing

This function imputes missing values in a data frame.
For categorical variables (factor or character), missing values are replaced with the mode.
For numeric variables, missing values can be imputed using the mean, median, or regression-based imputation.
If no method is specified for numeric columns, missing values are left as NA.

The 'dentomedical' package provides a comprehensive suite of tools for
medical and dental research. It includes automated descriptive statistics,
bivariate analysis with intelligent test selection, logistic regression,
and diagnostic accuracy assessment. All functions generate publication-ready tables using 'flextable', ensuring reproducibility and
clarity suitable for manuscripts, reports, and clinical research workflows.

Umar Hussain

dentomedical

Publication-Ready Descriptive, Bivariate, Regression,
Correlation and Diagnostic Accuracy Tools for Medical and
Dental Data

Nikolaos Pandis

impute_missing function

<dl><dt>data</dt>
<dd>A data frame containing numeric and/or categorical variables with missing values.</dd>
<dt>method</dt>
<dd>A character string specifying the imputation method for numeric columns.
Options are <code>"mean"</code>, <code>"median"</code>, <code>"regression"</code>, or <code>NULL</code> (default: <code>NULL</code>).</dd></dl>

Arguments

Impute Missing Values in a Data Frame — impute_missing

<dl>

<dt>data</dt>
<dd>A data frame containing numeric and/or categorical variables with missing values.</dd>


<dt>method</dt>
<dd>A character string specifying the imputation method for numeric columns.
Options are <code>"mean"</code>, <code>"median"</code>, <code>"regression"</code>, or <code>NULL</code> (default: <code>NULL</code>).</dd>

</dl>

impute_missing: Impute Missing Values in a Data Frame

Description

Usage

Value

Arguments

Examples