r4lineups (version 0.1.1)

esize_m_boot: Bootstrapped Effective Size

Description

Base function for computing bootstrapped effective size

Usage

esize_m_boot(lineup_vec, d, k)

Arguments

lineup_vec

A vector of lineup choices

d

Indices for bootstrap resampling

k

A vector indexing number of members in each lineup pair. Must be specified by user (scalar).

Value

If printarg=FALSE, provides only Malpass's priginal calculation of effective size

Details

Function to call when bootstrap resampling using boot function (in package 'boot')

References

Davison, A.C. & Hinkley, D.V. (1997). Bootstrap methods and their application. Cambridge University Press.

Malpass, R. S. (1981). Effective size and defendant bias in eyewitness identification lineups. Law and Human Behavior, 5(4), 299-309.

Malpass, R. S., Tredoux, C., & McQuiston-Surrett, D. (2007). Lineup construction and lineup fairness. In R. Lindsay, D. F. Ross, J. D. Read, & M. P. Toglia (Eds.), Handbook of Eyewitness Psychology, Vol. 2: Memory for people (pp. 155-178). Mahwah, NJ: Lawrence Erlbaum Associates.

Tredoux, C. G. (1998). Statistical inference on measures of lineup fairness. Law and Human Behavior, 22(2), 217-237.

Tredoux, C. (1999). Statistical considerations when determining measures of lineup size and lineup bias. Applied Cognitive Psychology, 13, S9-S26.

Wells, G. L.,Leippe, M. R., & Ostrom, T. M. (1979). Guidelines for empirically assessing the fairness of a lineup. Law and Human Behavior, 3(4), 285-293.

See Also

boot: https://cran.r-project.org/web/packages/boot/boot.pdf

Examples

# NOT RUN {
#Data:
lineup_vec <- round(runif(100, 1, 6))

#Get boot object:
bootobject <- boot::boot(lineup_vec, esize_m_boot, k = 6, R=1000)
bootobject

#To get confidence intervals:
cis <- boot::boot.ci(bootobject, conf = 0.95, type = "all")

# }