[R-sig-ME] population-level predict glmmtmb with poly()

[John Wilson]

Thank you, Dimitris. I poked around a bit but didn't see mention of
autocorrelation structures - are those supported as well?

I was wondering if this issue was related to the problem logged here:
https://github.com/glmmTMB/glmmTMB/issues/402. Can anyone comment on
whether what I'm seeing is a bug or just a user (=me) mistake? When I
change the toy example to be a simple linear form (rather than poly()), the
two predictions are identical, so I'm pretty sure it's a poly() issue...

On Tue, Oct 23, 2018 at 4:31 PM D. Rizopoulos <d.rizopoulos using erasmusmc.nl>
wrote:

> You could fit the same model also using the GLMMadaptive package in
> which the predict() method can produce predictions for the mean subject
> (i.e., the one with random effects values equal to 0), marginal
> predictions (i.e., averaged over the subjects), and subject-specific
> predictions.
>
> For more info you can have a look at:
>
> https://drizopoulos.github.io/GLMMadaptive/
>
>
> https://drizopoulos.github.io/GLMMadaptive/articles/Methods_MixMod.html#predictions
>
> and potentially also
>
>
> https://drizopoulos.github.io/GLMMadaptive/articles/Dynamic_Predictions.html
>
> if you're interested in dynamic predictions.
>
> Best,
> Dimitris
>
>
>
> On 10/23/2018 7:46 PM, John Wilson wrote:
> > Hello,
> >
> > I'm working on a glmmtmb() model with multiple continuous and categorical
> > predictors. Two of the predictors are orthogonal polynomials (I just saw
> > that the package was updated yesterday (!) to correctly handle those).
> One
> > of the polynomials has an interaction with another covariate.
> >
> > Since predict(re.form = 0) doesn't work just yet and one has to use
> > the model.matrix(lme4::nobars(formula(mod1)[-2]), newdata) approach - how
> > do I get the correct polynomial predictions out? It looks like my results
> > depend on how I structure the newdata data frame - when I use
> > expand.grid(), the predictions are wrong, but when I subset the original
> > data, the predictions are correct.
> >
> > Thanks so much!
> > John
> >
> > Here's an example:
> >
> > library(ggplot2)
> > library(glmmTMB)
> >
> > set.seed(0)
> > x <- 1:20
> > z <- sample(c("a", "b"), length(x), replace = TRUE)
> > y <- round(5 * 2*x + 3 * x^2 + 0.1 * x^3 + rnbinom(length(x), 10, 0.03))
> > group <- sample(c("i", "ii"), length(x), replace = TRUE)
> > df <- data.frame(x = x, y = y, z = z, group = group)
> > m <- glmmTMB(y ~ poly(x, 3) * z +
> > (1 | group),
> > family = nbinom2,
> > data = df)
> > # prediction on a new grid
> > newdata <- expand.grid(x = 1:20, z = unique(df$z))
> > X.cond = model.matrix(lme4::nobars(formula(m)[-2]), newdata)
> > beta.cond = fixef(m)$cond
> > newdata$Pred1 = as.numeric(X.cond %*% beta.cond)
> > # prediction in original data frame
> > X.cond = model.matrix(lme4::nobars(formula(m)[-2]))
> > beta.cond = fixef(m)$cond
> > df$Pred1 = as.numeric(X.cond %*% beta.cond)
> >
> > # the newdata preds are obviously off
> > ggplot(df) +
> > geom_point(aes(x = x, y = y, colour = z)) +
> > geom_line(data = newdata, aes(x = x, y = exp(Pred1), colour = z), size =
> 2)
> > +
> > geom_line(aes(x = x, y = exp(Pred1), colour = z, linetype = group))
> >
> > # if the new grid is defined like this, then the predictions are ok
> > newdata <- unique(select(df, x, z))
> > X.cond = model.matrix(lme4::nobars(formula(m)[-2]), newdata)
> > beta.cond = fixef(m)$cond
> > newdata$Pred1 = as.numeric(X.cond %*% beta.cond)
> > ggplot(df) +
> > geom_point(aes(x = x, y = y, colour = z)) +
> > geom_line(data = newdata, aes(x = x, y = exp(Pred1), colour = z), size =
> 2)
> > +
> > geom_line(aes(x = x, y = exp(Pred1), colour = z, linetype = group))
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-mixed-models using r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
>
> --
> Dimitris Rizopoulos
> Professor of Biostatistics
> Department of Biostatistics
> Erasmus University Medical Center
>
> Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
> Tel: +31/(0)10/7043478
> Fax: +31/(0)10/7043014
> Web (personal): http://www.drizopoulos.com/
> Web (work): http://www.erasmusmc.nl/biostatistiek/
> Blog: http://iprogn.blogspot.nl/

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