DeaR all,
I am using mob() for model based partitioning, with a dichotomous variable
(participant's correct/incorrect response to a test item) regressed onto a
continuous predictor related to a given property of the test item. Although
this variable is continuous, the value of this variable for many items in this
particular analysis is 0. The partitioning criterion is self-reported ability
in a related area.
> mob1 <- mob(
correct ~ circular.mean | srp.dimension,
control=mob_control(alpha=.001),
model=glinearModel,
family=binomial()
)
> plot(mob1)
Error in cut.default(x, breaks = breaks, include.lowest = TRUE) :
'breaks' are not unique
The same persists if I specify either a desired number of breaks, or explicit
breakpoints (e.g. breaks=3 or breaks=c(-0.1,0.1,0.5)). I guess this is to do
with the funny distribution of the predictor variable, but I'm not sure what to
do about it.
Many thanks and apologies if this doesn't fit the mailing list---it is my first
posting!
Jason Musil
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