crosstable()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.
crosstable_effect_args()A list with testing parameters:
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). See diff_mean_auto(), diff_mean_student(), diff_mean_boot(), or diff_median() for some examples of such functions. Users can provide their 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). See effect_odds_ratio(), effect_relative_risk(), or effect_risk_difference() for some examples of such functions. Users can provide their 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. See effect_survival_coxph(). Users can provide their own function.
effect_display - a function to format the effect. See display_effect().
conf_level - the desired confidence interval level
digits - the decimal places