effect_summary {crosstable} | R Documentation |
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 usingt.test
confidence intervals
Author(s)
Dan Chaltiel, David Hajage