Last week I introduced Galtung’s AJUS system and its implementation in R. As noted, the tolerance parameter is not a trivial matter, but we can use the provided function ajusCheck to explore how different tolerance parameters affect the outcome. Basically we throw a number of tolerance parameters at the ajus function, and see what happens.
In this example, the default values give type “S” (i.e. multi-peaked). Next, we visually inspect the distribution using ajusPlot:
So maybe the last increase is not significant, so we can try a different tolerance value (e.g. 0.2). We can continue this, or just try them all (e.g. between 0.1 and 2):
library(agrmt) # install from R-Forge
# Data:
V <- c(0,0,1,2,1.5,1.6)
# AJUS
ajus(V) # using default tolerance = 0.1
ajusPlot(V) # visual inspection
ajus(V, tolerance=0.2) # exploring
ajusCheck(V, t=seq(0.1,2,0.1)) # let's try them all
$tolerance
[1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9
[20] 2.0
$type
[1] “S” “A” “A” “A” “J” “J” “J” “J” “J” “J” “J” “J” “J” “J” “J” “F” “F” “F” “F”
[20] “F”
Which is it? Usually the substantive meaning of our numbers is the guide but sometimes we deal with constructs that are more difficult to assess. Using ajusCheck we can easily check whether we should spend hours thinking about whether 0.2 or 0.3 is more appropriate – or whether we get the same result.
Didier Ruedin 