This helper function provides default parameters for defining how the effect sizes should be computed. It belongs to the effect_args argument of the crosstable() function. See effect_summary, effect_tabular, and effect_survival for more insight.

## Usage

crosstable_effect_args(
effect_summarize = diff_mean_auto,
effect_tabular = effect_odds_ratio,
effect_survival = effect_survival_coxph,
effect_display = display_effect,
conf_level = 0.95,
digits = 2
)

## Arguments

effect_summarize

a function of three arguments (continuous variable, grouping variable and conf_level), used to compare continuous variable. Returns a list of five components: effect (the effect value(s)), ci (the matrix of confidence interval(s)), effect.name (the interpretation(s) of the effect value(s)), effect.type (the description of the measure used) and conf_level (the confidence interval level). Users can use diff_mean_auto(), diff_mean_student(), diff_mean_boot(), or diff_median(), or their custom own function.

effect_tabular

a function of three arguments (two categorical variables and conf_level) used to measure the associations between two factors. Returns a list of five components: effect (the effect value(s)), ci (the matrix of confidence interval(s)), effect.name (the interpretation(s) of the effect value(s)), effect.type (the description of the measure used) and conf_level (the confidence interval level).Users can use effect_odds_ratio(), effect_relative_risk(), or effect_risk_difference(), or their custom own function.

effect_survival

a function of two argument (a formula and conf_level), used to measure the association between a censored and a factor. Returns the same components as created by effect_summarize.Users can use effect_survival_coxph() or their custom own function.

effect_display

a function to format the effect. See display_effect().

conf_level

the desired confidence interval level

digits

the decimal places

## Value

A list with effect parameters

Dan Chaltiel