xvar_function: Create a summary table for an individual covariate

Description

Create a summary table for an individual covariate

Usage

xvar_function(
  xvar,
  data,
  grp,
  covTitle = "",
  digits = 1,
  digits.cat = 0,
  iqr = TRUE,
  all.stats = FALSE,
  pvalue = TRUE,
  effSize = FALSE,
  show.tests = FALSE,
  percentage = "col"
)

Value

A data frame is returned

Arguments

xvar: character with the name of covariate to include in table
data: dataframe containing data
grp: character with the name of the grouping variable
covTitle: character with the name of the covariate (predictor) column. The default is to leave this empty for output or, for table only output to use the column name 'Covariate'
digits: numeric specifying the number of digits for summarizing mean data. Otherwise, can specify for individual covariates using a vector of digits where each element is named using the covariate name. If a covariate is not in the vector the default will be used for it (default is 1). See examples
digits.cat: numeric specifying the number of digits for the proportions when summarizing categorical data (default is 0)
iqr: logical indicating if you want to display the interquartile range (Q1, Q3) as opposed to (min, max) in the summary for continuous variables
all.stats: logical indicating if all summary statistics (Q1, Q3 + min, max on a separate line) should be displayed. Overrides iqr
pvalue: logical indicating if you want p-values included in the table
effSize: logical indicating if you want effect sizes and their 95% confidence intervals included in the table. Effect sizes calculated include Cramer's V for categorical variables, and Cohen's d, Wilcoxon r, Epsilon-squared, or Omega-squared for numeric/continuous variables
show.tests: logical indicating if the type of statistical test and effect size (if effSize = TRUE) used should be shown in a column beside the p-values. Ignored if pvalue = FALSE
percentage: choice of how percentages are presented, either column (default) or row