An R implementation for Snell scoring
Paul F. Petrowski
February 18, 2023
With thanks to AbacusBio for funding the development of this package.
This package is an R implementation of the Snell scoring procedure originally derived by EJ Snell (1964) [1]. The scoring procedure is used to approximate the distance between ordinal values, assuming an approximately normal underlying distribution. The result are values on a continuous scale that are more amenable to conventional statistical analyses.
Tong et al [2] recognized the value in applying the Snell scoring procedure to calving ease data. Today, Interbull requires that calving ease traits are Snell transformed prior to genetic evaluation.
Practically, Snell scoring can be applied in any scenario where subjective ordinal scores have been used as measurements. Note that there must be three or more possible scores in order for the snell transformation to work. Binary scores will not work.
Chien Ho Wu (2008) [3] released an excel spreadsheet containing
practical formulas and calculations to execute the snell procedure. This
R package is based heavily on this excel spreadsheet. A significant
difference between the R package and the spreadsheet is that while the
spreadsheet uses a “rule fo thumb” to estimate boundary scores, the R
package uses an analytic formula. The link to the spreadsheet appears to
be broken today. A copy of the spreadsheet can be found in the
tests directory of this repository.
Create dataset. This is copied from the original Snell paper.
data <- data.frame(
"X-3" = c(0,6,0,0,0,2,3,0,1,2,0,5),
"X-2" = c(0,3,0,4,0,4,4,0,2,2,0,1),
"X-1" = c(0,1,3,1,0,3,3,1,0,2,0,1),
"X0" = c(3,0,2,2,0,1,0,1,0,0,0,0),
"X1" = c(3,1,2,4,2,0,1,1,1,2,4,1),
"X2" = c(2,1,4,0,5,2,1,5,4,4,1,3),
"X3" = c(4,0,1,1,5,0,0,4,4,0,7,1),
row.names = as.character(1:12)
)
summary(data)'data.frame': 12 obs. of 7 variables:
$ X.3: num 0 6 0 0 0 2 3 0 1 2 ...
$ X.2: num 0 3 0 4 0 4 4 0 2 2 ...
$ X.1: num 0 1 3 1 0 3 3 1 0 2 ...
$ X0 : num 3 0 2 2 0 1 0 1 0 0 ...
$ X1 : num 3 1 2 4 2 0 1 1 1 2 ...
$ X2 : num 2 1 4 0 5 2 1 5 4 4 ...
$ X3 : num 4 0 1 1 5 0 0 4 4 0 ...Perform the Snell procedure
snell(data) X.3 X.2 X.1 X0 X1 X2 X3
-1.0724177 0.6118877 1.6023234 2.1867387 2.8375348 4.0129725 5.8508914 The originally published scores are: -1.1, 0.6, 1.6, 2.2, 2.8, 4.0, 5.8
The scores in the spreadsheet are: -1.1, 0.611887716, 1.602323384 ,2.186738697, 2.837534795, 4.012972486, 5.84348144
The results are identical to those published except for at the
boundary catagories. This is because rsnell uses an
analytic method to calculate the outermost scores. The analytic method
is from [2].
In the above scenario, the input data was already tabulated as counts
of occurences for each score by group. Commonly, this table will need to
be derived from rawdata. The buildfreqtable function is
included in this function to facilitate such transformations. Suppose we
have raw data like this:
mydata <- data.frame("Groups" = rep(c("A", "B", "C", "D"), 10),
"Scores" = round(runif(40, 0, 5)))
Groups Scores
A 1
B 2
C 4
D 0
A 1
B 1
C 4
D 1
A 3
B 3
1-10 of 40 rowsThis is a simple dataset that only contains a column of subgroup designations and a colunmn of scores, but it could contain and number of additional columns.
To convert this into a frequency table we would use:
freqtable <- buildfreqtable(data = mydata, trait = "Scores", subgroup = "Groups")
freqtable 0 1 2 3 4 5
A 1 2 2 3 2 0
B 0 2 2 3 1 2
C 1 1 0 2 6 0
D 1 2 3 1 2 1With this out of the way, it is now simple to perform the Snell scoring procedure.
snell(freqtable) 0 1 2 3 4 5
-1.0394872 0.7153731 1.8412364 2.7540183 4.1716475 6.1264721