# Parameters for diagnosticity ratio This function calculates the parameters needed to calculate the diagnosticity ratio for several lineup pairs. ```r diag_param(lineup_pres_list, lineup_abs_list, pos_list, k) ``` ## Arguments - `lineup_pres_list`: A list containing k vectors of lineup choices for k lineups, in which the target was present - `lineup_abs_list`: A list containing k vectors of lineup choices for k lineups, in which the target was absent - `pos_list`: A list containing k numeric vectors indexing lineup member positions for each lineup pair - `k`: A vector indexing number of members in each lineup pair. Must be specified by user (scalar). ## Returns Returns a dataframe containing: * **n11**: Number of mock witnesses who identified the suspect in the target present condition * **n21**: Number of mock witnesses who did not identify the suspect in the target present condition * **n12**: Number of mock witnesses who identified the suspect in the target absent condition * **n13**: Number of mock witnesses who did not identify the suspect in the target absent condition ## Details * Lineup pairs consist of one lineup in which the target was present (TP) and one lineup in which the target was absent (TA). * Each lineup pair must occupy corresponding positions in the TA and TP lists. Example: For a lineup pair A that consists of (1)TP lineup and (2)TA lineup: A(1) is the first vector in the TP list A(2) is the first vector in the TP list * The order in which nominal size for each lineup pair is listed must also correspond with the positions of each respective lineup in the lineup lists (i.e., if lineup 1 has k = 6, then the first element of vector 'k' = 6) * Data must be in a list format. This allows the function to compare lineups in which the number of choices and number of lineup members differs. * The following warning will appear if vectors comprising lineup lists are of different lengths: **longer object length is not a multiple of shorter object length**. This does not affect the accuracy of the function and can be ignored. ## Examples ```r #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) #Call: linedf <- diag_param(lineup_pres_list, lineup_abs_list, pos_list, k) ``` ## 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.

diag_param function