r4lineups

Data Format

The majority of functions in r4lineups require a numeric vector representing lineup data, whilst other functions require you to pass a list of lineup data. It is advisable to read the documentation to determine the format of data needed by each specific function. Most r4lineups functions will not work with a dataframe, even if the dataframe contains only one column, and thus appears to be in vector like format. If your lineup data is in dataframe format already, you can easily convert it to a vector or a list.

For instance, if we have 10 mock witnesses, and each views a lineup with 6 members, then we could record their choices as a numeric vector, where the elements represent the position of the lineup member chosen by each witness:

lineup_vec <- c(3, 2, 5, 6, 1, 3, 3, 5, 6, 4)

However, it is also possible to represent this set of choices as a one-dimensional table:

lineup_tbl <- table(lineup_vec)

If we had our lineup choices recorded in a data frame, which is what we might end up with if we import the data from a spreadsheet program, then we could easily extract the variable representing choices in the dataframe into a vector.

For instance, if the lineup choices shown above for 10 mock witnesses were part of a larger dataset, and we had recorded the ages and participant number of each participant, then the dataframe might appear as shown below (we create the dataframe below with simulated data here, though)

participant_num <- paste("Partic_",1:10, sep = "") age <- round(runif(10, 18, 80), 2) line_df <- data.frame(participant_num, age, lineup_vec)

Show the dataframe:

line_df

Extract the lineup choices, per participant, to a vector: a_lineup_vec <- line_df$linep_vec

Or make a table directly from the variable of choices: a_lineup_table <- table(line_df$lineup_vec)

One important issue is that there is no way of the functions in this package knowing if some of the members of a lineup received no choices. In other words, if there were in fact 7 lineup members in our running example, but member 7 was not chosen by any witnesses, then this would not be evident from the lineup vector, since it records the choice made by each witness, but not the number of lineup members. To deal with this problem, generally the functions in the package require nominal lineup size (the number of physical members of the lineup) to be passed as an argument.

Let’s look at a slightly larger data set:

library(r4lineups)
data(nortje2012)

• For functions that require data for only one lineup or one lineup pair, you can convert the data from a given lineup to a vector by calling for e.g. lineup_vec <- nortje2012$lineup_1 or alternatively lineup_vec <- nortje2012[[1]]

• For functions that require several lineups or several lineup pairs, you can convert the data to a list, e.g., lineup_list <- as.list(nortje2012)

• Nominal Size

  The notion of ‘Nominal size’ refers to the number of physical members of a lineup. Functions relying on nominal size for accurate calculations require you to manually specify nominal size. If the specified nominal size is not reflected in the dataframe or vector passed to the function, execution of the function is halted with the following error message: “User-declared nominal size does not match observed nominal size. Please check vector of target positions.”

r4lineups Sample Datasets

This package contains several sample datasets, listed below:

1. nortje2012: A dataframe of lineup choices for 3 independent lineups, from a study conducted by Nortje & Tredoux (2012)
2. line73 : Lineup data from Doob & Kirshenbaum (1973)
3. mockdata: Lineup data from a study conducted by Nortje & Tredoux (2012)
4. mickwick : Confidence and accuracy data for lineup choices. Sample data to be passed to ROC function make_roc()
5. Foil images (used to demonstrate face_sim) can be found at https://github.com/tmnaylor/malpass_foils

All sample datasets can be loaded by calling data(sample_data). Further details can be found in the documentation for each dataset.

Lineup Proportion

Proportion refers to the proportion of witnesses selecting a particular member of a lineup. This package provides calculations of proportion for individual lineup members (lineup_prop_vec()), and for all members of a lineup with a subsidiary function (allprop()).

The calculation of the proportion of mock witnesses choosing the suspect is essentially a measure of lineup bias (see Malpass, 1981; Tredoux, 1998), especially when one tests whether it exceeds the proportion expected by chance guessing alone (1/nominal_size).

library(r4lineups)

lineup_prop_vec(lineup_vec, target_pos = 3, k = 8)
[1] 0.1428571

allprop(lineup_vec, k = 8)
   prop
1 0.143
2 0.135
3 0.143
4 0.045
5 0.286
6 0.045
7 0.173
8 0.030

Functional Size

We defined nominal size above as the number of physical members of a lineup, but it is clear from experience that not all lineup members are good foils, and some might not end up being chosen at all. They are not good tests of witness memory, in short. Wells et al. (1979) proposed the notion of ‘functional size’ as a measure of the number of plausible lineup members, and defined it as the inverse of the proportion of mock witnesses choosing the suspect.

Functional size = $\frac{D}{N}$

You can compute functional size by inverting the result of the lineup_prop_vec() function, or by calling func_size, but we suggest you use the function func_size_report() which provides more detail, including bootstrapped confidence intervals.

• To compute functional size with confidence intervals, call func_size_report(), and pass a vector of lineup data, as well as one scalar indicating target position, and another indicating nominal size:

  func_size_report(lineup_vec, target_pos = 3, k = 8)
  Functional size of lineup is  7
  Confidence intervals [95%]
  Normal Theory 3.366 9.942
  Bootstrap: percentile (R = 1000) 4.926 11.083
  Bootstrap: bias-corrected (R = 1000) 4.586 10.231

Effective Size

The measure of functional size proposed by Wells et al. (1979) does not take the choosing rates of foils other than the suspect into account, and Malpass (1981) thus proposed a measure of the number of plausible lineup foils that takes all the data from a lineup table into account. He called this measure ‘effective size’.

Malpass’ Effective size A = $k_{a}-\sum_{i=1}^{k_{a}}\frac{|o_{i}-e_{a}|}{2e_{a}}$

where o_i is the (observed) number of mock witnesses who choose lineup member i; e_a is the adjusted nominal chance expectation [N(l/k_a)], and k_a is the adjusted nominal number of alternatives in the lineup (original number-number of null foils)

Malpass’ measure of effective size poses problems for statistical inference based on distribution theory, though, as argued by Tredoux (1998). He instead proposed an alternate measure of effective size, known as E’, derived from earlier work by Agresti and Agresti (1978).

This package allows you to calculate effective size in several different ways.

1. Malpass’s original (1981) & adjusted effective size (see: Tredoux, 1998)

   Malpass’ original formula for effective size proposed a denominator computed by entering the number of foils that attracted non-zero choice frequencies. Tredoux (1998) argued against this practice, pointing out that zero frequencies could arise from random guessing with appreciable frequency. He suggested a minor amendment of Malpass’ original formula, as follows.

   Malpass’ Effective size B = $k-\sum_{i=1}^{k}\frac{|o_{i}-e|}{2e}$

   Both Malpass’ measures of effective size can be computed by calling the esize_m() function and passing a table of lineup data. If ‘both = FALSE’ is passed as an argument, only Malpass’s adjusted formula for effective size is used. If both = TRUE, both Malpass’s original and adjusted calculations of effective size are returned. You must also specify nominal size.

esize_m(table(lineup_vec), k = 8, both = TRUE)
Effective size (Malpass, 1981) =  5.962406 
Effective size (Malpass, 1981, 
             adj Tredoux, 1998) =  5.962406 
[1] 5.962406

• Compute bootstrapped estimates (Malpass’s adjusted):

  Tredoux (1998) argued that it is better to use the alternate measure of E’, but this was in a time when bootrapping methods for statistical inference were not widely known, and we therefore include a function that allows you to compute bootstrap estimates of the measures, with confidence intervals.

  To do this, one must first generate a bootstrap sample using the gen_boot_samples() function, the gen_esize_m function(), and the gen_esize_m_ci() function. Their usage is shown below.

  #Create a dataframe of bootstrapped lineup data
  bootdata <- gen_boot_samples(lineup_vec, 1000)
  
  #Calculate effective size for each lineup in bootdata
  #Pass bootstrap df to function
  #Nominal size is again declared by user
  lineupsizes <- gen_esize_m(bootdata, k = 8)
  
  #Pass vector of boostrapped effective sizes to gen_esize_m_ci
  #to calculate lower and upper CIs with desired level of alpha
  gen_esize_m_ci(lineupsizes, perc = .025)
      2.5% 
  5.285714 
  gen_esize_m_ci(lineupsizes, perc = .975)
     97.5% 
  6.323308 

• Compute bootstrap descriptive statistics (Malpass’s adjusted effective size):

  This function allows you to calculate descriptive statistics for a boostrapped vector/df of effective sizes (see object lineupsizes, above). We pass this to gen_boot_propmean_se(). Descriptive statistics are reported in some detail.

  gen_boot_propmean_se(lineupsizes)
  Boot prop. (mean)   =  5.818 
  Boot prop. (median) =  5.805 
  SD of boot prop     =  0.255 
  SE of boot prop     =  0.008063808 
  2.5% boot CI lvl    =  5.285714 
  97.5% boot CI lvl   =  6.323308 

2. Tredoux’s measure of Effective Size, E’

Tredoux (1998) proposed a measure of effective size that is amenable to statistical inference using distribution theory.

E’=$\frac{1}{1-I}$, where I=$1-\sum_{1=1}^k(\frac{O_i}{N})^2$

and where k, N, and 0_i_ are all as defined earlier.

To compute Tredoux’s measure of effective size, E’, use the function esize_T. We can compute confidence intervals around the measure of E’ using normal distribution theory, and using bootstrap intervals, with the functions esize_T_boot() from r4lineups, as well asboot() & boot.ci() from the boot package. Usage is shown below.

#Compute effective size from a single vector of lineup choices
esize_T(lineup_table)
[1] 5.693273

#Compute bootstrapped effective size
esize_boot <- boot::boot(lineup_table, esize_T_boot, R = 1000)

#View boot object
esize_boot

ORDINARY NONPARAMETRIC BOOTSTRAP


Call:
boot::boot(data = lineup_table, statistic = esize_T_boot, R = 1000)


Bootstrap Statistics :
    original    bias    std. error
t1* 5.693273 0.2437852   0.7316762

#Get confidence intervals
esize_boot.ci <- boot::boot.ci(esize_boot)
Warning in boot::boot.ci(esize_boot): bootstrap variances needed for
studentized intervals
esize_boot.ci
BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
Based on 1000 bootstrap replicates

CALL : 
boot::boot.ci(boot.out = esize_boot)

Intervals : 
Level      Normal              Basic         
95%   ( 4.015,  6.884 )   ( 3.971,  6.890 )  

Level     Percentile            BCa          
95%   ( 4.496,  7.416 )   ( 3.998,  6.922 )  
Calculations and Intervals on Original Scale
Some BCa intervals may be unstable

3. Effective Size Per Foils

   Malpass (1981) proposed an additional method for computing effective size, based on the number of foils who are chosen at a rate nearly equal to chance expectation. We propose that ‘nearly equal’ could mean that chance expectation falls within a confidence interval constructed around the proportion of mock witnesses choosing the lineup member.

   To calculate effective size by counting the number of foils who fall within the CI for chance guessing, we pass a vector of lineup data, a vector of target positions and nominal size. This function returns effective size from a set of bootstrapped data. Default alpha is 0.05, but this can be adjusted as desired.

eff_size_per_foils(lineup_vec, target_pos, k = 8, conf = 0.95)
[1] 0

Applying this function to our data, we see that effective size for this lineup is 4.

#Get data
linedf <- nortje2012[1:2]

#Compare effective size
effsize_compare(linedf)

The two Effective sizes are  5.693273   4.967425
If the interval includes 0, ns at p = .05
Confidence intervals of difference [95%]
Normal Theory -0.22 1.769
Bootstrap: percentile (R = 1000) -0.325 1.646
Bootstrap: bias-corrected (R = 1000) -0.258 1.729

Diagnosticity Ratio

Wells and Lindsay (1980) proposed the use of a ‘diagnosticity ratio’ (essentially a risk ratio) computed from witness choices in target present lineup and target absent lineup (i.e. the ratio of those). Such a ratio could tell us the trade off between hits and false positives, and be used to evaluate modifications to lineup procedures, for instance.

We can calculate diagnosticity ratio for a set of identification parades using either diag_ratio_W() for Wells & Lindsay’s (1980) diagnosticity ratio formula, or diag_ratio_T() for Wells’ adjusted diagnosticity ratio (see: Tredoux, 1998).

• Both functions require the same set of arguments: a vector of lineup choices in which the target was present (TP), a vector of lineup choices in which the target was absent (TA), as well as target positions and nominal size for TP and TA lineups, respectively. These last arguments are specified explicitly.

  #Target present & target absent lineup data for 1 lineup pair 
  TP_lineup <- nortje2012$lineup_1
  TA_lineup <- nortje2012$lineup_3
  
  #Compute diagnosticity ratio
  diag_ratio_W(TP_lineup, TA_lineup, pos_pres = 3, pos_abs = 7, k1 =8, k2 = 8)
  [1] 6.333333
  diag_ratio_T(TP_lineup, TA_lineup, pos_pres = 3, pos_abs = 7, k1 =8, k2 = 8)
  [1] 5.571429

• We can also calculate the variance of the diagnosticity ratio, which is useful for applying some inferential methods:

  var_diag_ratio(TP_lineup, TA_lineup, 3, 7, 8, 8)
  [1] 0.3709273

  Note: var_diag_ratio() relies on Wells & Lindsay’s original formula for computing diagnosticity ratio. See Tredoux 1998 for further details.

#Target present data:
TP_lineup1 <-       round(runif(100,1,6))
TP_lineup2 <-       round(runif(70,1,5))
TP_lineup3 <-       round(runif(20,1,4))
lineup_pres_list <- list(TP_lineup1, TP_lineup2, TP_lineup3)

#Target absent data:
TA_lineup1 <-       round(runif(100,1,6))
TA_lineup2 <-       round(runif(70,1,5))
TA_lineup3 <-       round(runif(20,1,4))
lineup_abs_list <-  list(TA_lineup1, TA_lineup2, TA_lineup3)

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)

homog_diag(lineup_pres_list, lineup_abs_list, pos_list, k)
Mean diagnosticity ratio: 4.000711
Chi-square estimate (q): 0.003988083
Sig: 0.9999331

ROC Curve (Confidence ~ Accuracy)

The make_roc() function allows you to compute and plot an ROC curve for data from an eyewitness experiment, where accuracy is recorded for target present and target absent lineups. We follow the ideas suggested by Mickes, Wixted, and others (see references). This function is experimental, and we don’t offer much control over the output.

1. This function requires a dataframe with two columns, which must be named confidence and accuracy (where accuracy = binary accuracy):

#Call roc function
make_roc(mickwick)
Setting levels: control = 0, case = 1
Setting direction: controls < cases

ROC Curve for Confidence ~ Accuracy

Similarity of Faces in a Lineup

Following some ideas suggested by Tredoux (2002), this function computes the degree to which each face in a set of faces loads onto a common factor computed from numeric representations of the faces.

The images used in this example can be found at https://github.com/tmnaylor/malpass_foils, and can be loaded by calling image_read from the magick package:

foil1 <- magick::image_read('https://raw.githubusercontent.com/tmnaylor/malpass_foils/master/malp1.jpg')

print(foil1)
# A tibble: 1 × 7
  format width height colorspace matte filesize density
  <chr>  <int>  <int> <chr>      <lgl>    <int> <chr>  
1 JPEG     138    154 sRGB       FALSE    33506 72x72  

Without passing any arguments, call face_sim():

• A dialog box will appear, through which you may access your OS’s file explorer. Navigate to the folder in which the images are stored, select all, and open.

  Note: Each image should be of one face only. Each face should be standardised to have the same pupil locations in xy space (this will mean repositioning and resizing each face image).

• The function will now process the selected images, printing the lineup array to the viewer pane in RStudio, and reporting the loading of each face on the first common factor in a factor analysis:

Set of faces is printed to viewer pane

Returns factor loading for each face