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From |
n j cox <n.j.cox@durham.ac.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: Re: Re: st: drop redundant value labels |

Date |
Sun, 17 Feb 2008 16:56:20 +0000 |

My analysis resembles Sergiy's. If the trimmed down dataset were much smaller than the original, using -decode- on all the variables with labels followed by dropping all the label definitions and then an -encode- on all the -decode-d variables might be one way to go. Not especially attractive, but might be worth consideration.

Nick

n.j.cox@durham.ac.uk

Sergiy Radyakin

===============

Unless there is some information regarding the selection to the final

sample -- brute force is the only way. It may be direct ( cycle

for-each-value-check-if-it-is-there) or it could be more involved, but

with the same thing going on behind the scenes. One thing to concider

however is whether you have more deleted labels or those that are

kept. E.g. in some cases it might be more efficient to cycle through

the observations that are left, than through all the labels,

especially if they (observations) are unique. Example: you have

observations, each representing an occupation, each occupation has a

label, you want to keep only "dangerous" occupations (defined as you

like). There will likely be relatively few of them among all, so go

brute force by observations, and keep the labels, that they are using.

You can also define your labels as a dataset with two fields: numeric

code and string label. After the selection in the data occurred, you

can merge the two datasets to determine, which labels must be kept.

But the overhead from having the labels should not be very large.

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