TestDimorph (version 0.4.0)

Hedges_g: Hedges' g

Description

quantifies the size of difference between sexes in measured traits.

Usage

Hedges_g(x, Trait = 1, CI = 0.95, B = NULL, rand = TRUE, digits = 4)

Arguments

x

A data frame containing summary statistics.

Trait

Number of the column containing names of measured parameters, Default: 1

CI

confidence interval coverage takes value from 0 to 1, Default: 0.95.

B

number of bootstrap samples for generating confidence intervals. Higher number means greater accuracy but slower execution. If NULL bootstrap confidence intervals are not produced, Default:NULL

rand

logical; if TRUE, uses random seed. If FALSE, then set.seed(42) for repeatability, Default: TRUE

digits

Number of significant digits, Default: 4

Value

a table of Hedge's g values with confidence interval for different traits.

Details

Calculates Hedges' (1981) g and its confidence intervals using the pooled standard deviation and correcting for bias. See Goulet-Pelletier and Cousineau (2018) for details of the calculations and D_index for description of the bootstrap.

References

Hedges, L. V. (1981). Distribution theory for Glass's estimator of effect size and related estimators. Journal of Educational Statistics, 6(2), 107-128.

Goulet-Pelletier, J.-C., & Cousineau, D. (2018). A review of effect sizes and their confidence intervals, part I: The Cohen's d family. The Quantitative Methods for Psychology, 14(4), 242-265.

Examples

# NOT RUN {
library(TestDimorph)
data("Cremains_measurements")
# Confidence intervals with non-central t distribution
Hedges_g(Cremains_measurements[1, ])
# }
# NOT RUN {
# confidence interval with bootstrapping
Hedges_g(Cremains_measurements[1, ], rand = FALSE, B = 1000)
# }