lsr (version 0.1.1)

quantileCut: Cut by quantiles

Description

Cuts a variable into equal sized categories

Usage

quantileCut(x,n,...)

Arguments

x

A vector containing the observations.

n

Number of categories

...

Additional arguments to cut

Value

• 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.

Details

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.

See Also

cut, quantile

Examples

### 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
#
# (-0.015,2.67]   (2.67,5.67]     (5.67,15] 
#             3             3             3
# 

# Notice the uneven bin sizes: category 1 covers a range from 0 to 2.67 and 
# category 2 covers a similarly sized range from 2.67 to 5.67, but the third 
# category covers a much larger range, from 5.67 to 15. These categories might
# be useful in some contexts (e.g., the data are ordinal scale), but it is
# important to check that this is so.

# 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)
#
# (-0.015,5]     (5,10]    (10,15] 
#          5          3          1
#

# This time  the categories cover an equal range but have highly unequal
# frequencies.