population-level predict glmmtmb with poly()

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 at 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))

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--
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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