R help - Jul 2011 - Convert continuous variable into discrete variable

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
threshold variables:

If x is between 0 and 3 the discrete variable should be 1
If x is between 3 and 4.5 the discrete variable should be 2
If x is between 4.5 and 6 the discrete variable should be 3
If x is between 6 and 8 the discrete variable should be 4
If x is larger than 8 the discrete variable should be 5

Is there a straightforward way of doing this (besides working with several
if statements in a row)?

Thanks,

Michael

	[[alternative HTML version deleted]]

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, Michael Haenlein
<haenlein at escpeurope.eu> wrote:
> 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
> threshold variables:
>
> If x is between 0 and 3 the discrete variable should be 1
> If x is between 3 and 4.5 the discrete variable should be 2
> If x is between 4.5 and 6 the discrete variable should be 3
> If x is between 6 and 8 the discrete variable should be 4
> If x is larger than 8 the discrete variable should be 5
>
> Is there a straightforward way of doing this (besides working with several
> if statements in a row)?
>
> Thanks,
>
> Michael
>
> ? ? ? ?[[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

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 its assumed discontinuities seldom exist in nature and most
relationships are not piecewise flat.
Frank

levanovd wrote
> Or even simpler (no need to specify labels):
> 
> x<-runif(100,0,100) 
> u <- cut(x, breaks = c(0, 3, 4.5, 6, 8, Inf), labels = FALSE)




-----
Frank Harrell
Department of Biostatistics, Vanderbilt University
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