[R] Creating NA equivalent

[Chris Evans]

Many thanks Wolfgang,

I guess I can see that survival analyses don't have to be time based but 
clearly I need to read up on that.  I can't see an example in the survival 
package.  And it proves to be hard to search for one. Can anyone point me 
to useful resources on that, in {survival} or not?

I am probably straying way off topic and  off list guide here but isn't a 
Tobit only handling censoring at one edge, i.e. the LDL scenario, or the UDL, 
but not both?  I think this may be getting back to Marc's original question
and certainly, again, I would love to be pointed to either Tobit handling
LDL _and_ UDL or to any other existing methods.

TIA,

Chris

----- Original Message -----
> From: "Wolfgang Viechtbauer" <wolfgang.viechtbauer using maastrichtuniversity.nl>
> To: "Chris Evans" <chrishold using psyctc.org>
> Cc: r-help using r-project.org
> Sent: Tuesday, 21 December, 2021 11:31:55
> Subject: RE: Creating NA equivalent

> Hi Chris,
> 
> The survival package provides machinery for handling censored observations.
> Whether time is censored or some other type of variable (e.g., viral load due
> to some lower detection limit) does not make a fundamental difference. In fact,
> the type of model you are thinking of with 2) is a Tobit model, which can be
> fitted using the survival package (or censReg).
> 
> Best,
> Wolfgang
> 
>>-----Original Message-----
>>From: R-help [mailto:r-help-bounces using r-project.org] On Behalf Of Chris Evans
>>Sent: Tuesday, 21 December, 2021 12:17
>>To: Duncan Murdoch
>>Cc: r-help using r-project.org
>>Subject: Re: [R] Creating NA equivalent
>>
>> I am neither a programmer nor a professional statistician but this topic
>> interests me because:
>> 
>> 1) I remember from long, long ago that S had a way to create labels that could
>>    denote multiple ways in which a value could be missing that was sometimes
>>    useful to me as my field sometimes has such situations.  In R I handle this
>>    with a second variable but I can see that using attributes is cleaner and
>>    might have real benefits when doing missing value analyses.  That might
>>    raise questions about whether some of the nice packages that help with
>>    missing value analyses would take on board some standardised use of
>>    attributes for this.
>> 
>> 2) I think Marc's question LDL/UDL is about a very particular sort of value
>>    that isn't missing and _is_ censored but not in survival analysis meaning
>>    of censored. (At least, it's not the same to my mind, perhaps it is?  To me
>>    the difference is that I most often hit the LDL/UDL issue in data that
>>    don't have much, or any, time frame.) Again, this comes up a lot for me
>>    where people are given limited possible answers in questionnaires and I've
>>    often wondered if I should explore simulating probability models for an the
>>    "off the edge" value on a latent variable beneath/behind the measured
>>    responses.  I'd be very grateful to hear of any work in R packages (to stay
>>    only just "off the edge" of the posting    guide).  Or of any work a long
>>    the lines that Duncan offers, that sort of pulls this toward    base R,
>>    though that sounds to me as if it would be a huge undertaking.
>> 
>> I'm very interested to hear any thoughts on either aspect.
>> 
>> Seasonal (mutivalued) greetings to all!
>> 
> > Chris

-- 
Chris Evans (he/him) <chris using psyctc.org> 
Visiting Professor, UDLA, Quito, Ecuador & Honorary Professor, University of Roehampton, London, UK.
Work web site: https://www.psyctc.org/psyctc/ 
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