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.viechtba...@maastrichtuniversity.nl>
> To: "Chris Evans" <chrish...@psyctc.org>
> Cc: r-help@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-boun...@r-project.org] On Behalf Of Chris Evans
>>Sent: Tuesday, 21 December, 2021 12:17
>>To: Duncan Murdoch
>>Cc: r-help@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) <ch...@psyctc.org>
Visiting Professor, UDLA, Quito, Ecuador & Honorary Professor, University of
Roehampton, London, UK.
Work web site: https://www.psyctc.org/psyctc/
CORE site: https://www.coresystemtrust.org.uk/
Personal site: https://www.psyctc.org/pelerinage2016/
OMbook: https://ombook.psyctc.org/book/
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