effect_summary: Effect measure for association between one continuous and one...

In crosstable: Crosstables for Descriptive Analyses

Effect measure for association between one continuous and one categorical variable

Description

User can either use or extend these functions to configure effect calculation.

Usage

diff_mean_auto(x, by, conf_level = 0.95, R = 500)

diff_mean_boot(x, by, conf_level = 0.95, R = 500)

diff_median_boot(x, by, conf_level = 0.95, R = 500)

diff_mean_student(x, by, conf_level = 0.95)

Arguments

x	numeric vector
by	categorical vector (of exactly 2 unique levels)
conf_level	confidence interval level
R	number of bootstrap replication

Value

A list with five components: effect, ci, effect.name, effect.type, and conf_level

Functions

• diff_mean_auto(): (Default) calculate a specific "difference in means" effect based on normality (Shapiro or Anderson test) and variance homogeneity (Bartlett test)

• diff_mean_boot(): calculate a "difference in means" effect with a bootstrapped CI using standard deviation

• diff_median_boot(): calculate a "difference in medians" effect with a bootstrapped CI using quantiles#'

• diff_mean_student(): calculate a "difference in means" effect using t.test confidence intervals

Author(s)

Dan Chaltiel, David Hajage

See Also

crosstable_effect_args()

crosstable documentation built on Nov. 13, 2023, 1:08 a.m.