devtools::install_local(here())
# or: devtools::install_github("neumanrq/fairreviewers")
library(fairreviewers)
library(here)
# The rating dataset:
#
# Each column contains the ratings
# of a reviewer.
#
# Each row describes the ratings that
# a single applicant got.
data.env <- randomDataset(20, 5)
head(data.env$ratings)
# Output looks like this:
#
# Roy Rose Robin Ricarda Ryan
# Arthur 10 10 NA 3 8
# Aisha NA 10 3 7 7
# Anna 10 10 3 NA 9
# Aisha 10 9 4 2 8
# Ashley 9 9 4 4 NA
# Ally NA 10 6 2 10
# …
# (Note that duplicated names can occur!)
# Let's start the analysis!
review <- fairreviewers::init(data.env$ratings)
strictness <- review$strictness # contains strictness factors for each reviewer
print(strictness)
# Output looks like this
#
# Arithmetic Strictness Expected Strictness
# Roy 0.7541836 0.7540541
# Rose 0.7697874 0.7696552
# Robin 1.7717328 1.7714286
# Ricarda 1.5945595 1.5942857
# Ryan 0.8455997 0.8454545
#
# Interpreation: Robin and Ricarda are quite strict
# reviewers, but Roy is a quite graceful one
result <- review$result
print(result)
# Roy Rose Robin Ricarda Ryan Mean Rating after AS correction Rating after ES correction
# Arthur 10 10 NA 3 8 7.75 6.70 6.70
# Aisha NA 10 3 7 7 6.75 7.52 7.52
# Anna 10 10 3 NA 9 8.00 7.04 7.04
# Aisha 10 9 4 2 8 6.60 6.30 6.30
# Ashley 9 9 4 4 NA 6.50 6.80 6.79
# Ally NA 10 6 2 10 7.00 7.49 7.49
# Ali 9 9 2 4 6 6.00 5.74 5.74
# Ali 9 10 1 6 8 6.80 6.52 6.52
# Albert 8 NA 3 5 4 5.00 5.68 5.67
# Albert 10 5 4 NA 9 7.00 6.52 6.52
# Ally 9 6 6 1 NA 5.50 5.91 5.91
# Ali 10 9 NA 5 7 7.75 7.09 7.09
# Arthur 6 NA 5 6 8 6.25 7.43 7.43
# Amanda 10 9 6 3 7 7.00 7.16 7.16
# Anna 8 7 2 NA NA 5.67 4.99 4.99
# Aisha NA 9 5 5 8 6.75 7.63 7.63
# Ally 7 NA 4 3 8 5.50 5.98 5.98
# Aljona 8 7 4 5 7 6.20 6.48 6.48
# Amanda 8 8 1 4 8 5.80 5.42 5.42
# Amanda 7 8 NA 5 10 7.50 6.97 6.97
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