Function for getting chi-squared value for homogeneity of diagnosticity ratios
Details
To compute linedf, use the diag_param helper function.
To compute df, apply ln_diag_ratio, var_lnd & d_weights functions to linedf, then bind results into one dataframe (see Examples)
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
#Target present data:
A <- rep(1:6, length.out = 100)
B <- rep(1:5, length.out = 70)
C <- rep(1:4, length.out = 20)
lineup_pres_list <- list(A, B, C)
rm(A, B, C)
#Target absent data:
A <- rep(6:1, length.out = 100)
B <- rep(5:1, length.out = 70)
C <- rep(4:1, length.out = 20)
lineup_abs_list <- list(A, B, C)
rm(A, B, C)
# Suspect position for each TP/TA pair
pos_list <- c(3, 2, 1)
#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)
#Get ln(d), variance of ln(d) & d weights:
ratio <- ln_diag_ratio(linedf, correction = FALSE)
var <- var_lnd(linedf)
wi <- d_weights(linedf)
#Bind estimates into one df of 3 rows & x observations
#(see Details above)
df <- t(cbind(var, ratio, wi))
#Call:
chi_diag(df)
#> [1] 0