findTh(x, n = 1, hclustm = "complete", distm = "euclidean", ...)
The purpose of this function is to automatically find calibration thresholds for a numerical causal condition, to be split into separate groups.
The process of calibration into crisp sets assumes expert knowledge about the best threshold(s) which separates the raw data into the most meaningful groups.
In the absence of such knowledge, an automatic procedure might help grouping the raw data according to statistical clustering techniques.
The number of groups to split depends on the number of thresholds: one thresholds splits into two groups, two thresholds splits into three groups etc.
Previous versions of this function had an argument named groups
instead
of argument n
, but they are bacwards compatible.
For more details about how many groups can be formed with how many thresholds,
see ?cutree
.
More details about the clustering techniques used in this function are found
using ?hclust
, and also more details about different distance measures
can be found with ?dist
. This function uses their default values.
n
.
# hypothetical list of country GDPs, clearly separated # into either two or three groups gdp <- c(460, 500, 900, 2000, 2100, 2400, 15000, 16000, 20000) # find one threshold to separate into two groups findTh(gdp)[1] 8700# 8700 # find two thresholds to separate into two groups findTh(gdp, n = 2)[1] 8700 18000# using different clustering methods findTh(gdp, n = 2, hclustm = "ward.D2", distm = "canberra")[1] 1450 8700