Re: st: zero-inflated analyses: when do you decide that is zero-inflated?

["Cris Dogaru (Oregon State University)" <statamplus@gmail.com>]

Thanks a lot David.
It's a good idea to use a transformed scale for the regression part. I
could probably use the link(log) in a glm model?..
There's the tpm user-written command
tpm outcome predictors, firstpart(logit) secondpart(glm,
family(gaussian) link(log))

Cris

On Tue, Jul 16, 2013 at 3:24 PM, David Hoaglin <dchoaglin@gmail.com> wrote:
> Dear Cris,
>
> Since you have the actual size of the skin reaction, a two-part model
> seems a good choice.  It would be of interest to compare the result of
> using actual sizes that are < 3 mm with the result of recoding those
> sizes to 0.  If those results differ in interesting ways, you could
> see what happens with some lower thresholds than 3 mm.
>
> In the regression part of the two-part model, you may want to consider
> using a transformed scale for the size.  For example, the variability
> in size may be greater for larger wheals.  If so, the square-root
> scale or the log scale may be appropriate (either by actual
> transformation or by a version of generalized linear models known as
> quasi-likelihood, which can be done, as I understand it, with the
> -poisson- command).
>
> To get a graphical indication of whether a set of frequencies
> resembles a Poisson distribution (or, for example, has excess zeros),
> you could try the "Poisonness plot" (Hoaglin 1980, Hoaglin and Tukey
> 1985 --- pardon the shameless plug).  The basic version would be easy
> to do.  The 1985 chapter discusses a similar plot for negative
> binomial distributions, once one chooses a value for one of the
> parameters.
>
> David Hoaglin
>
> Hoaglin, D.C. (1980).  A Poissonness plot.  The American Statistician
> 34:146-149.
>
> Hoaglin D.C. and Tukey J.W. (1985).  Checking the shape of discrete
> distributions.  In Exploring Data Tables, Trends, and Shapes (D.C.
> Hoaglin, F. Mosteller, and J.W. Tukey, eds.).  New York: Wiley, pp.
> 345-416.
>
> On Tue, Jul 16, 2013 at 5:55 AM, Cris Dogaru (Oregon State University)
> <statamplus@gmail.com> wrote:
>> Dear David,
>> I see what you are saying, and you are actually right. Theoretically I
>> can still consider it a truncated version (we could have administered
>> 10 or 20 skin prick test to separate allergens), but indeed,
>> conceptually my outcome is not a count variable (counting events), but
>> rather a set of indicator variables for a latent construct (atopy or
>> sensitization); this leaving aside that the decision for a "positive"
>> test is arbitrary (skin reaction is 3mm in diameter or larger). The
>> tests are indeed associated, as one would actually expect. From the
>> literature (using factor analysis technique), they tend to cluster
>> (indoor, outdoor, food, inhaled, etc allergens).
>>
>> I will settle, probably, for a two-part model, as Peter Lachenbruch
>> suggests, but I will do it for each test individually, taking the
>> actual size of the skin reaction, in mm. There's plenty of zeros (and
>> I can recode those <3 mm to 0 as well, to stick with the commonly used
>> threshold), so I will have a two-part model with a logit/regress
>> combination (I can use the user-written tpm program).
>>
>> One of the co-authors suggested to analyze "number of positive tests",
>> and that got me into the negative binomial/Poisson approaches. An
>> ordinal logit model seems more appropriate indeed.
>>
>> Many thanks
>>
>> Cristian Dogaru
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