Bootstrapped Effective Size

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

The Tredoux-adjusted Malpass effective-size estimate for the resample.

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

#Data:
lineup_vec <- round(runif(100, 1, 6))

#Get boot object:
bootobject <- boot::boot(lineup_vec, esize_m_boot, k = 6, R=1000)
bootobject
#> 
#> ORDINARY NONPARAMETRIC BOOTSTRAP
#> 
#> 
#> Call:
#> boot::boot(data = lineup_vec, statistic = esize_m_boot, R = 1000, 
#>     k = 6)
#> 
#> 
#> Bootstrap Statistics :
#>     original   bias    std. error
#> t1*      5.2 -0.15596   0.2371141

#To get confidence intervals:
cis <- boot::boot.ci(bootobject, conf = 0.95, type = "all", k = 6)
#> Warning: bootstrap variances needed for studentized intervals
#> Warning: extreme order statistics used as endpoints