Cuts a variable into equal sized categories
It is sometimes convenient (though not always wise) to split a
continuous numeric variable x into a set of n discrete
categories that contain an approximately equal number of cases. The
quantileCut function does exactly this. The actual categorisation
is done by the cut function. However, instead of selecting
ranges of equal sizes (the default behaviour in cut), the
quantileCut function uses the quantile function to
select unequal sized ranges so as to ensure that each of the categories
contains the same number of observations. The intended purpose of the
function is to assist in exploratory data analysis; it is not generally
a good idea to use the output of quantileCut function as a factor
in an analysis of variance, for instance, since the factor levels are not
interpretable and will almost certainly violate homogeneity of variance.
quantileCut(x, n, ...)A vector containing the observations.
Number of categories
Additional arguments to cut
A factor containing n levels. The factor levels are
determined in the same way as for the cut function, and can be
specified manually using the labels argument, which is passed to
the cut function.
# An example illustrating why care is needed
dataset <- c( 0,1,2, 3,4,5, 7,10,15 ) # note the uneven spread of data
x <- quantileCut( dataset, 3 ) # cut into 3 equally frequent bins
table(x) # tabulate
#> x
#> (-0.015,2.67] (2.67,5.67] (5.67,15]
#> 3 3 3
# For comparison purposes, here is the behaviour of the more standard cut
# function when applied to the same data:
y <- cut( dataset, 3 )
table(y)
#> y
#> (-0.015,5] (5,10] (10,15]
#> 6 2 1