bimodality_coefficient: Calculate bimodality coefficient.

In mousetrap: Process and Analyze Mouse-Tracking Data

Calculate bimodality coefficient.

Description

Calculate the bimodality coefficient for a numeric vector as specified in Pfister et al. (2013).

Usage

bimodality_coefficient(x, na.rm = FALSE)

Arguments

x	a numeric vector.
na.rm	logical specifying whether missing values should be removed.

Details

The calculation of the bimodality coefficient involves calculating the skewness and kurtosis of the distribution first. For this, the skew and kurtosi functions of the psych package are used. Note that type is set to "2" for these functions in accordance with Pfister et al. (2013).

Value

A numeric value.

Author(s)

Pascal J. Kieslich

Felix Henninger

References

Pfister, R., Schwarz, K. A., Janczyk, M., Dale, R., & Freeman, J. B. (2013). Good things peak in pairs: A note on the bimodality coefficient. Frontiers in Psychology, 4, 700. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3389/fpsyg.2013.00700")}

See Also

skew for calculating skewness and kurtosis.

mt_check_bimodality for assessing bimodality using several methods in a mousetrap data object.

Examples

pfister_data_a <- rep(1:11, times=c(3,5,5,10,17,20,17,10,5,5,3))
bimodality_coefficient(pfister_data_a) #.34
pfister_data_b <- rep(1:11, times=c(2,26,14,6,2,0,2,6,14,26,2))
bimodality_coefficient(pfister_data_b) #.79

mousetrap documentation built on Oct. 23, 2023, 5:08 p.m.