It is important to check for lack of fit of the categorized variable. One
way to do this is to test for the additional predictive ability of the
original continuous variable after adjusting for its categorized version.
It is very uncommon for a categorized continuous variable to fit well,
because
Hi:
x<-runif(100,0,100)
u <- cut(x, breaks = c(0, 3, 4.5, 6, 8, Inf), labels = c(1:5))
Based on the x I obtained,
> table(u)
u
1 2 3 4 5
3 2 1 2 92
cut() or findInterval() are the two basic functions for discretizing a
numeric variable.
HTH,
Dennis
On Fri, Jul 15, 2011 at 2:29 PM, Mi
Dear all,
I have a continuous variable that can take on values between 0 and 100, for
example: x<-runif(100,0,100)
I also have a second variable that defines a series of thresholds, for
example: y<-c(3, 4.5, 6, 8)
I would like to convert my continuous variable into a discrete one using the
thres
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