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Re: st: extract values from kdensity graphic
From
Nick Cox <[email protected]>
To
[email protected]
Subject
Re: st: extract values from kdensity graphic
Date
Wed, 2 May 2012 10:35:27 +0100
Another way of looking at these data is to apply -group1d- (SSC). In
fact Mike cannot do that himself because it needs Stata 9, but he can
use the results. With a least-squares criterion explained in the help
and references given, -group1d- yields as the best 5 groups
Group Size First Last Mean SD
5 8 23 100.62 30 100.91 100.75 0.09
4 1 22 98.41 22 98.41 98.41 0.00
3 6 16 97.19 21 97.39 97.29 0.06
2 8 8 96.11 15 96.34 96.25 0.07
1 7 1 94.74 7 95.08 94.95 0.11
In fact, just about any method of cluster analysis should find the
same groups if they are genuine, e.g. -cluster kmeans-. Then use
whatever summary you prefer.
Details follow for -group1d-.
. sort size
. group1d size, max(7)
Partitions of 30 data up to 7 groups
1 group: sum of squares 143.60
Group Size First Last Mean SD
1 30 1 94.74 30 100.91 97.43 2.19
2 groups: sum of squares 23.00
Group Size First Last Mean SD
2 9 22 98.41 30 100.91 100.49 0.74
1 21 1 94.74 21 97.39 96.12 0.93
3 groups: sum of squares 6.62
Group Size First Last Mean SD
3 8 23 100.62 30 100.91 100.75 0.09
2 15 8 96.11 22 98.41 96.81 0.66
1 7 1 94.74 7 95.08 94.95 0.11
4 groups: sum of squares 1.26
Group Size First Last Mean SD
4 8 23 100.62 30 100.91 100.75 0.09
3 7 16 97.19 22 98.41 97.45 0.40
2 8 8 96.11 15 96.34 96.25 0.07
1 7 1 94.74 7 95.08 94.95 0.11
5 groups: sum of squares 0.20
Group Size First Last Mean SD
5 8 23 100.62 30 100.91 100.75 0.09
4 1 22 98.41 22 98.41 98.41 0.00
3 6 16 97.19 21 97.39 97.29 0.06
2 8 8 96.11 15 96.34 96.25 0.07
1 7 1 94.74 7 95.08 94.95 0.11
6 groups: sum of squares 0.14
Group Size First Last Mean SD
6 8 23 100.62 30 100.91 100.75 0.09
5 1 22 98.41 22 98.41 98.41 0.00
4 6 16 97.19 21 97.39 97.29 0.06
3 8 8 96.11 15 96.34 96.25 0.07
2 5 3 94.95 7 95.08 95.01 0.05
1 2 1 94.74 2 94.89 94.81 0.08
7 groups: sum of squares 0.10
Group Size First Last Mean SD
7 2 29 100.84 30 100.91 100.88 0.04
6 6 23 100.62 28 100.76 100.71 0.05
5 1 22 98.41 22 98.41 98.41 0.00
4 6 16 97.19 21 97.39 97.29 0.06
3 8 8 96.11 15 96.34 96.25 0.07
2 5 3 94.95 7 95.08 95.01 0.05
1 2 1 94.74 2 94.89 94.81 0.08
Groups Sums of squares
1 143.60
2 23.00
3 6.62
4 1.26
5 0.20
6 0.14
7 0.10
On Wed, May 2, 2012 at 9:34 AM, Nick Cox <[email protected]> wrote:
> In practice,
>
> gen sizer = round(size)
>
> is a simpler way of degrading your data. Check by
>
> scatter sizer size
>
> Nick
>
> On Wed, May 2, 2012 at 9:16 AM, <[email protected]> wrote:
>> * Hi Statalist,
>> * I'm a beginner using version 8.
>> * The following measurements were collected by a machine in my lab...
>> clear
>> input sampling_event size
>> 1 94.74
>> 2 94.89
>> 3 94.95
>> 4 94.97
>> 5 95
>> 6 95.05
>> 7 95.08
>> 8 96.11
>> 9 96.22
>> 10 96.24
>> 11 96.27
>> 12 96.27
>> 13 96.27
>> 14 96.32
>> 15 96.34
>> 16 97.19
>> 17 97.26
>> 18 97.26
>> 19 97.32
>> 20 97.34
>> 21 97.39
>> 22 98.41
>> 23 100.62
>> 24 100.69
>> 25 100.69
>> 26 100.76
>> 27 100.76
>> 28 100.76
>> 29 100.84
>> 30 100.91
>> end
>> list
>> twoway (scatter size sampling_event)
>>
>> * My aim is to class these size values into categories (5 categories in
>> the example shown).
>> * kdensity will generate the following graphic...
>>
>> kdensity size , w(0.1) n(30)
>>
>> * The troughs of this graphic are a good way to define the bounds of each
>> category.
>> * Category_4, for example would include all size values larger than 98 and
>> less than 99.
>> * I'd like to extract these trough points as a kdensity post-estimation
>> and output them as a new variable.
>> * Is this possible?
>>
>> * Look forward to any advice the list has to offer.
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/