[R-sig-ME] glmmTMB: how to calculate posterior prob. of structural zero?

[Aaron Mackey]

Right -- so that's not what we need, is it? We need the probability of
having actually seen a zero, given the conditional model (regardless of
what the expected count might be). The probability of seeing a zero in the
other part of the mixture is 1.0, and the prior for both of the two models
is the mixture coefficient (and 1-coef). But we still need the likelihood
of the zero, given the conditional model, not the likelihood of seeing the
expected value.

-Aaron

On Fri, Oct 5, 2018 at 4:30 PM Ben Bolker <bbolker using gmail.com> wrote:

> On Fri, Oct 5, 2018 at 4:07 PM Aaron Mackey <ajmackey using gmail.com> wrote:
> >
> > Thanks for this, it seems to provide sensible numbers; but just to
> confirm, does predict(model, type="conditional") actually use the Y
> variable observed count? Or does it generate the expected log count from
> the provided X variables (as in the case when newdata is provided)?
>
>   It generates the expected  *count* (not the log count) *of the
> conditional part of the model only* (i.e. not including structural
> zeros), based on the estimated parameters and a model matrix (either
> the one from the model or a newly generated one, if newdata is
> specified)
>
> >
> > -Aaron
> >
> > On Wed, Oct 3, 2018 at 3:01 PM Ben Bolker <bbolker using gmail.com> wrote:
> >>
> >>
> >>   If you're fitting a zero-inflated Poisson model, it would be
> >> especially easy because the zero probability is simply exp(-lambda), so
> >> I believe the "posterior"(ish) probability that the zero is due to the
> >> structural rather than conditional part of the model would be
> >>
> >>  P(zero|structural)/P(zero) =
> >> P(zero|structural)/(P(zero_structural)+P(zero_conditional))
> >>
> >> or
> >>
> >> prob_struc <- function(model) {
> >>    strucprob <- predict(model, type="zprob")
> >>    condprob <- exp(-predict(model, type="conditional"))
> >>    return(strucprob/(strucprob+condprob))
> >> }
> >>
> >> For a negative binomial ("nbinom2", or quadratic parameterization) the
> >> zero probability is (k/(k+mu))^k (I think ... e.g.
> >> dnbinom(0,mu=0.5,size=0.5) is sqrt(0.5)).  sigma(model) returns the
> >> overdispersion parameter k, so a function as above but with
> >>
> >>   k <- sigma(model)
> >>   condprob <- (k/(k+predict(model,type="conditional")))^k
> >>
> >>  inserted should do the trick.  (Please test these yourself and make
> >> sure they are sensible before proceeding!)
> >>
> >> On 2018-10-03 02:18 PM, Aaron Mackey wrote:
> >> > I'm happily using glmmTMB to fit zero-inflated count models with my
> data,
> >> > but I'd like to also know which zeroes in my data are more likely (or
> not)
> >> > to be structural vs. expected from the conditional distribution. I
> know how
> >> > to use Bayes formula to calculate the posterior, and predict(zinb,
> >> > type="zprob") gives me the prior probabilites for each data point
> being
> >> > structural or not (respecting the zero inflation part of the model),
> and
> >> > the likelihoods for the structural components are 1 (if the data
> point is a
> >> > zero) or 0 (if the data point is not a zero) -- but is there a way to
> >> > extract the likelihood for each zero data point with respect to the
> >> > conditional part of the model?
> >> >
> >> > thanks,
> >> > -Aaron
> >> >
> >> >       [[alternative HTML version deleted]]
> >> >
> >> > _______________________________________________
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> >> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >> >
> >>
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> >> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>

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