confidence intervals with mvrnorm - upper value equal to inf

   It's hard to troubleshoot this without a reproducible example. Unless
the answer is obvious -- which it's not, to me -- the easiest way to
troubleshoot is to work through the steps one at a time and see where
the infinite values first appear.  Can you create such an example either
by posting your data or by simulating data that looks like your data?

   The posterior predictive simulation approach assumes that the
sampling distributions of the parameters are multivariate normal, which
is likely to be questionable in a low-information setting (which will be
the case if you don't have very many non-zero values ...)


On 10/10/20 7:28 AM, Alessandra Bielli wrote:

Dear list,

I am trying to predict a value and CI for two different treatments from a
glmmTMB fitted model using posterior predictive simulations (mvrnorm
function in the MASS package) as in the Salamander example, Brooks 2017
appendix B
<https://www.biorxiv.org/content/biorxiv/suppl/2017/05/01/132753.DC1/132753-2.pdf>.
The dependent variable is a count, majority of values are zeros but some
positive values appear.

m1 <- glmmTMB(Dolphins.TOT ~ Used_piNgers + offset(log(Effort)) +
(1|Trip_Code), ziformula =~1,
               data=x, family = "truncated_poisson")

     newdata0 = with(x,
                 expand.grid(
                   Used_piNgers = c("Y","N"),
                   Effort=1))

X.cond = model.matrix(lme4::nobars(formula(m1)[-2]), newdata0)

beta.cond = fixef(m1)$cond
pred.cond = X.cond %*% beta.cond
ziformula = m1$modelInfo$allForm$ziformula
X.zi = model.matrix(lme4::nobars(ziformula), newdata0)
beta.zi = fixef(m1)$zi
pred.zi = X.zi %*% beta.zi

pred.ucount = exp(pred.cond)*(1-plogis(pred.zi))


set.seed(101)
pred.condpar.psim = mvrnorm(1000,mu=beta.cond,Sigma=vcov(m1)$cond)
pred.cond.psim = X.cond %*% t(pred.condpar.psim)
pred.zipar.psim = mvrnorm(1000,mu=beta.zi,Sigma=vcov(m1)$zi)
pred.zi.psim = X.zi %*% t(pred.zipar.psim)
pred.ucount.psim = exp(pred.cond.psim)*(1-plogis(pred.zi.psim))
ci.ucount = t(apply(pred.ucount.psim,1,quantile,c(0.025,0.975)))
ci.ucount = data.frame(ci.ucount)
names(ci.ucount) = c("ucount.low","ucount.high")
pred.ucount = data.frame(newdata0, pred.ucount, ci.ucount)

For my upper CI, I get a value equal to Inf:

Used_piNgers Effort  pred.ucount ucount.low ucount.high
1            Y      1 6.758889e-11 0.00000000         Inf
2            N      1 1.575418e-02 0.00223033   0.1096139

Is the Inf caused by the very low variability of values in my dataset? I
tried to lower the upper bound of the CI ci.ucount =
t(apply(pred.ucount.psim,1,quantile,c(0.025,0.975))) and only when reaching
0.475 ci.ucount = t(apply(pred.ucount.psim,1,quantile,c(0.025,0.475))) I
obtained:

Used_piNgers Effort  pred.ucount ucount.low  ucount.high
1            Y      1 6.758889e-11 0.00000000 7.117465e+12
2            N      1 1.575418e-02 0.00223033 1.454579e-02

I found a related post
<https://stackoverflow.com/questions/38272798/bootstrap-confidence-interval-with-inf-in-final-estimates-boot-dplyr-package>
but
the explanation is not clear to me. I would like to publish these results
and I would like to know:

    1. is this a sign that something is wrong? if yes, what is it?
    2. if nothing is wrong, what does the Inf mean and what's the best way
    to report it and plot it in a publication?


I also posted this question on Cross validated
https://stats.stackexchange.com/questions/491196/bootstrap-confidence-interval-with-mvrnorm-upper-value-equal-to-inf

Thanks,
Alessandra

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