[R-sig-ME] Longitudinal logistic regression with continuous-time first-order autocorrelation structure

[Thierry Onkelinx]

Dear Dennis,

Here is an example on how to run such a model with INLA. It took about 3.5
min on my laptop.

library(INLA)
library(ggplot2)
set.seed(20180302)
n_id <- 35
n_day <- 7 * 6
n_prompt <- 5
dataset <- expand.grid(
  ID = seq_len(n_id),
  Day = seq_len(n_day),
  Prompt = seq_len(n_prompt)
)
dataset$Hour <- sample(8:22, size = nrow(dataset), replace = TRUE)
p0 <- rnorm(n_id, mean = -21, sd = 0.5)
p1 <- rnorm(n_id, mean = 2.67, sd = 0.05)
p2 <- rnorm(n_id, mean = -0.08, sd = 0.002)
dataset$eta <-  p2[dataset$ID] * dataset$Hour ^ 2 + p1[dataset$ID] *
dataset$Hour + p0[dataset$ID]
dataset$mu <- plogis(dataset$eta)
ggplot(dataset, aes(x = Hour, y = mu, group = ID)) + geom_line()
dataset$noise <- rnorm(nrow(dataset), sd = 0.1)
dataset$prob <- plogis(dataset$eta + dataset$noise)
dataset$Craving <- rbinom(nrow(dataset), size = 1, prob = dataset$prob)

dataset$HourID <- dataset$Hour
model <- inla(
  Craving ~
    f(Hour, model = "rw1") +
    f(HourID, model = "rw1", replicate = ID),
  family = "binomial",
  data = dataset
)
plot(model)

Best regards,

Thierry


ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

///////////////////////////////////////////////////////////////////////////////////////////
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey
///////////////////////////////////////////////////////////////////////////////////////////

<https://www.inbo.be>

2018-03-01 2:44 GMT+01:00 Dennis Ruenger <dennis.ruenger at gmail.com>:

> Thanks, Alain and Ben, for your replies.
>
> My understanding is that for the kind of intensive longitudinal data I'm
> dealing with, a mixed model with both random intercepts and slopes for the
> time effect *and *autoregressive errors are recommended.
>
> I'd like to follow Alain's suggestion and give glmmTMB a try. Based on a
> description of the covariance structures available with glmmTMB (link
> below), it looks like the Ornstein–Uhlenbeck covariance structure might be
> what I'm looking for (i.e., something akin to corrCAR1() that works in a
> GLMM).
>
> So I tried:
>
> df$time_hours <- numFactor(df$time_hours)
> fit  <- glmmTMB(y ~ time_hours + (time_hours|id) + ou(time_hours-1|id),
> family = binomial, data = df)
>
> However, after about 10 minutes, I receive an error message about failed
> memory allocation (on a laptop with a 7th gen Intel Core i5 processor and
> 8GB RAM). The data set includes 34 participants with up to 300 data points
> per participants. Running the model for a subset of 5 participants also
> resulted in memory allocation failure. The same was true for the spatial
> Gaussian and spatial exponential covariance structures.
>
> Does anyone see a way to make this work with glmmTMB?
>
> Thanks a lot.
>
> https://cran.r-project.org/web/packages/glmmTMB/vignettes/covstruct.html
>
>         [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>

	[[alternative HTML version deleted]]