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Effect measure for association between one continuous and one categorical variable

Source: R/effect.R
effect_summary.Rd

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

See also

crosstable_effect_args()

Author

Dan Chaltiel, David Hajage