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From |
Nick Cox <njcoxstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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 <njcoxstata@gmail.com> 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, <mcross@exemail.com.au> 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/

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**Re: st: extract values from kdensity graphic***From:*Nick Cox <njcoxstata@gmail.com>

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