On Apr 3, 2012, at 8:47 AM, Val wrote:
Hi all,
Assume that I have the following 10 data points.
x=c( 46, 125 , 36 ,193, 209, 78, 66, 242 , 297 , 45)
sort x and get the following
y= (36 , 45 , 46, 66, 78, 125,193, 209, 242, 297)
The methods below do not require a sorting step.
I want to group the sorted data point (y) into equal number of
observation per group. In this case there will be three groups. The
first
two groups will have three observation and the third will have four
observations
group 1 = 34, 45, 46
group 2 = 66, 78, 125
group 3 = 193, 209, 242,297
Finally I want to calculate the group mean
group 1 = 42
group 2 = 87
group 3 = 234
I hope those weren't answers from SAS.
Can anyone help me out?
I usually do this with Hmisc::cut2 since it has a `g = <n>` parameter
that auto-magically calls the quantile splitting criterion but this is
done in base R.
split(x, cut(x, quantile(x, prob=c(0, .333, .66 ,1)) ,
include.lowest=TRUE) )
$`[36,65.9]`
[1] 36 45 46
$`(65.9,189]`
[1] 66 78 125
$`(189,297]`
[1] 193 209 242 297
> lapply( split(x, cut(x, quantile(x, prob=c(0, .333, .66 ,1)) ,
include.lowest=TRUE) ), mean)
$`[36,65.9]`
[1] 42.33333
$`(65.9,189]`
[1] 89.66667
$`(189,297]`
[1] 235.25
Or to get a table instead of a list:
> tapply( x, cut(x, quantile(x, prob=c(0, .333, .66 ,1)) ,
include.lowest=TRUE) , mean)
[36,65.9] (65.9,189] (189,297]
42.33333 89.66667 235.25000
In SAS I used to do it using proc rank.
?quantile isn't equivalent to Proc Rank but it will provide a useful
basis for splitting or tabling functions.
thanks in advance
Val
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