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elicitr

Description

elicitr is an R package used to standardise, visualise and aggregate data from expert elicitation.
The package is in active development and will implement functions based on two formal elicitation methods:

  • Elicitation of continuous variables
    Adapted from Hemming, V. et al. (2018). A practical guide to structured expert elicitation using the IDEA protocol. Methods in Ecology and Evolution, 9(1), 169–180. https://doi.org/10.1111/2041-210X.12857
  • Elicitation of categorical data
    Adapted from Vernet, M. et al. (2024). Assessing invasion risks using EICAT-based expert elicitation: application to a conservation translocation. Biological Invasions, 26(8), 2707–2721. https://doi.org/10.1007/s10530-024-03341-2

Installation

You can install the development version of elicitr from GitHub with:

# install.packages("pak")
pak::pak("CREWdecisions/elicitr")

How elicitr works

Just as one creates a form to collect estimates in an elicitation process, with elicitr one creates an object to store metadata information. This allows to check whether experts have given their answers in the expected way.
All the functions in the elicitr package start with two prefixes: cont and cat. This design choice is intended to enhance functions discovery. cont functions are used for the elicitation of continuous variables while cat functions for the elicitation of categorical variables.

Getting started

Create the metadata object and load the data for continuous variables:

my_elicitation <- cont_start(var_names = c("var1", "var2", "var3"),
                             var_types = "ZNp",
                             elic_types = "134",
                             experts = 6) |>
  cont_add_data(data_source = round_1, round = 1) |>
  cont_add_data(data_source = round_2, round = 2)
#> ✔ <elic_cont> object for "Elicitation" correctly initialised
#> ✔ Data added to "Round 1" from "data.frame"
#> ✔ Data added to "Round 2" from "data.frame"
my_elicitation
#> 
#> ── Elicitation ──
#> 
#> • Variables: "var1", "var2", and "var3"
#> • Variable types: "Z", "N", and "p"
#> • Elicitation types: "1p", "3p", and "4p"
#> • Number of experts: 6
#> • Number of rounds: 2

Plot raw data for variable 2 in round 1:

plot(my_elicitation, round = 1, var = "var2")

Data can be sampled from the elicitation object:

samp <- cont_sample_data(my_elicitation, round = 2)
#> ✔ Rescaled min and max for variable "var3".
#> ✔ Data for "var1", "var2", and "var3" sampled successfully using the "basic" method.
samp
#> # A tibble: 18,000 × 3
#>    id      var   value
#>    <chr>   <chr> <dbl>
#>  1 5ac97e0 var1     -2
#>  2 5ac97e0 var1     -2
#>  3 5ac97e0 var1      1
#>  4 5ac97e0 var1      1
#>  5 5ac97e0 var1     -2
#>  6 5ac97e0 var1      0
#>  7 5ac97e0 var1      0
#>  8 5ac97e0 var1     -2
#>  9 5ac97e0 var1      0
#> 10 5ac97e0 var1     -2
#> # ℹ 17,990 more rows

And the sample summarised:

summary(samp)
#> # A tibble: 3 × 7
#>   Var      Min     Q1 Median   Mean     Q3    Max
#>   <chr>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
#> 1 var1  -4     -2      0     -0.986  1      1    
#> 2 var2  11.0   14.5   16.2   16.3   18.4   22.5  
#> 3 var3   0.464  0.672  0.717  0.715  0.761  0.870

And plotted as violin plots:

plot(samp, var = "var2", type = "violin")

Violin plot of the sampled data for variable 2.

Or plotted as density plots:

plot(samp, var = "var3", type = "density")

Density plot of the sampled data for variable 3.

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