sum_norm

This function performs the Shapiro-Wilk normality test on all numeric variables
in a dataset and returns the results in a publication-ready <code>flextable</code>.
Extremely small p-values are displayed as "p &lt; 0.001". The function
automatically detects numeric variables and ignores non-numeric columns.

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

sum_norm function

<dl><dt>data</dt>
<dd>A data frame containing numeric and non-numeric variables.
Only numeric variables are assessed for normality.</dd>
<dt>sample_size</dt>
<dd>Integer. Maximum number of observations to use for the
Shapiro-Wilk test per variable (default = 5000).</dd></dl>

Arguments

Normality Test Summary Table for Numeric Variables — sum_norm

<dl>

<dt>data</dt>
<dd>A data frame containing numeric and non-numeric variables.
Only numeric variables are assessed for normality.</dd>


<dt>sample_size</dt>
<dd>Integer. Maximum number of observations to use for the
Shapiro-Wilk test per variable (default = 5000).</dd>

</dl>

sum_norm: Normality Test Summary Table for Numeric Variables

Description

Value

Arguments

Examples