r4lineups (version 0.1.1)

d_bar: Mean diagnosticity ratio for k lineup pairs

Description

Function for computing pooled estimator from a set of k diagnosticity ratios

Usage

d_bar(df)

Arguments

df

A dataframe containing rows: ln(d), variance of ln(d), d weights

Value

Mean diagnosticity ratio for k independent lineups

Details

. The order in which the estimates are bound together (i.e., their position in the dataframe) is important, and should always be as follows:

• row 1: var

• row 2: lnd

• row 3: wi

References

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.

Examples

# NOT RUN {
#Target present data:
A <-  round(runif(100,1,6))
B <-  round(runif(70,1,5))
C <-  round(runif(20,1,4))
lineup_pres_list <- list(A, B, C)
rm(A, B, C)


#Target absent data:
A <-  round(runif(100,1,6))
B <-  round(runif(70,1,5))
C <-  round(runif(20,1,4))
lineup_abs_list <- list(A, B, C)
rm(A, B, C)

#Pos list
lineup1_pos <- c(1, 2, 3, 4, 5, 6)
lineup2_pos <- c(1, 2, 3, 4, 5)
lineup3_pos <- c(1, 2, 3, 4)
pos_list <- list(lineup1_pos, lineup2_pos, lineup3_pos)
rm(lineup1_pos, lineup2_pos, lineup3_pos)

#Nominal size:
k <- c(6, 5, 4)

#Use diag param helper function to get data (n11, n21, n12, n22):
linedf <- diag_param(lineup_pres_list, lineup_abs_list, pos_list, k)

#Compute ln(d):
ratio <- ln_diag_ratio(linedf)

#Compute variance of ln(d):
var <- var_lnd(linedf)

#Compute weights for pooled estimator:
wi <- d_weights(linedf)

#Bind ln(d), variance of ln(d) and weights into one df (of 3 rows & x observations)
#(see Details above):
df <- t(cbind(ratio, var, wi))

#Call:
d_bar(df)
# }