@@ -29,21 +29,21 @@ pctile}</span>
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This function accepts ` by() ` with ` xtile ` and ` pctile ` . However, you
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can simply use it as a replacement for native Stata commands.
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- __ * Equivaent to pctile* __ (store the quantiles of ` exp ` in ` newvar ` ):
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+ __ * Equivalent to pctile* __ (store the quantiles of ` exp ` in ` newvar ` ):
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``` stata
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gquantiles newvar = exp [if] [in] [weight], pctile [nquantiles(#) genp(newvarname) altdef]
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```
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<br >
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- __ * Equivaent to xtile* __ (store the categories of ` exp ` in ` newvar ` ):
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+ __ * Equivalent to xtile* __ (store the categories of ` exp ` in ` newvar ` ):
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``` stata
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gquantiles newvar = exp [if] [in] [weight], xtile [nquantiles(#) cutpoints(numlist) altdef]
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fasterxtile newvar = exp [if] [in] [weight], [nquantiles(#) cutpoints(numlist) altdef]
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```
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<br >
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- __ * Equivaent to \_ pctile* __ (return the percentiles of ` exp ` ):
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+ __ * Equivalent to \_ pctile* __ (return the percentiles of ` exp ` ):
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``` stata
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gquantiles exp [if] [in] [weight], _pctile [nquantiles(#) percentiles(numlist) altdef]
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```
@@ -339,7 +339,7 @@ In theory there is no reason to limit NQ. For example, the question
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well-defined question. Even if there will be at least 996 categories
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contain no values, there is no reason to limit the number of categories
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to 4 (of course, since the 1000 categories are created from those 4
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- values, in practice this might not be adviseable ).
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+ values, in practice this might not be advisable ).
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So why does the limit exist in ` xtile ` ? It is actually a limit in ` pctile ` ,
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which is used internally. Since ` pctile ` stores the percentiles in a variable
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