Re: st: St: Dropping variables with mostly missing values

[Nick Cox <njcoxstata@gmail.com>]

However, -nmissing- and -npresent- (also SJ) allow you to go something like

npresent, min(20)

or even something like

npresent, min(`=ceil(_N/5)')

after which

keep `r(varlist)'

would -keep- what you wanted. Alternatively, use -nmissing- to count
missings and -drop- unwanted variables afterwards. The help indicates
that

"A question by Eric Uslaner led to the addition of r(varlist) as a
saved result."

and a search flags http://www.stata.com/statalist/archive/2005-02/msg00297.html

so this question comes nicely full circle. Thanks again, Eric, for
provoking that addition.

Nick
njcoxstata@gmail.com

On 8 February 2014 00:00, Nick Cox <njcoxstata@gmail.com> wrote:

> Good solutions to this came from Jeph Herrin, Amirsa and Richard Goldstein.
>
> Meanwhile, anyone interested in -dropmiss-, which doesn't do this,
> should please note that it comes from the Stata Journal, not SSC.

On 7 February 2014 20:40, Jeph Herrin <info@flyingbuttress.net> wrote:

>> To drop all variables missing more than 80% of the time:
>>
>> foreach V of varlist _all {
>>         count if !mi(`V')
>>         drop if r(N)/_N < 0.2
>> }
>>
>>
>> This works for string and numeric variables. Change 0.2 to whatever level
>> you want.

On 2/7/2014 3:11 PM, Eric M. Uslaner wrote:

>>> I know that this has been discussed before, but a long search doesn't find
>>> a solution for me (my own fault in searching, most likely).
>>>
>>> I have a data set (not my own) with 161 cases over a long time period.
>>> But  most of the variables are largely made up of missing values
>>> (information wasn't available a long time ago).  I have used Nick Cox's
>>> dropmiss (from SSC) to drop variables with all missing values.  But a large
>>> number of variables remain with few observations.  I would like to delete
>>> any variable with fewer than 20 cases.  But I can't figure out how to do
>>> this (especially since I have a large number of variables, most of which
>>> have very few cases).  Any help would be appreciated.
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