Error from glmmTMB().

Wow.? This is impressive, and fascinating, albeit somewhat mystifying.

I tried your code and yes indeed it runs without complaint; no errors,
no warnings.

I conclude from this that mixed_model() is capable of fitting models
using the beta binomial distribution, a fact of which I was previously
unaware.? (I previously thought that only the glmmTMB package provided
this capability.)

I gather, after reading TFM a bit more, that mixed_model() can
accommodate custom-built family functions, such as the beta.binomial()
function that you used to produce a fit to my example.

However writing such custom-built family functions is a bit beyond my
very limited capabilities.

I am currently struggling to find suitable models to fit to a number of
rather recalcitrant data sets.? The model presented in my question to
r-sig-mixed-models was just one of a fairly large number with which I
would like to experiment.? The data in question appear to exhibit
over-dispersion w.r.t. the binomial distribution.? The random effects
models that I am fitting seem to produce problematic results; I want to
try using the beta binomial distribution in addition to (or perhaps
instead of) random effects modelling.

With glmmTMB() I can (with *some* data sets) fit models like

??? glmmTMB(cbind(Dead, Alive) ~ (Trt + 0)/Dose + (Dose | Rep),
??????????? data = X, family = betabinomial(link = "cloglog"),
?????????????? dispformula = ~Dose)

I can also set dispformula equal to ~poly(Dose,2) or ~splines::ns(Dose,2).

Can custom-built family functions be created to do this sort of thing
using mixed_model()?

If so, what would the appropriate value of n_phis be?? I am *guessing*
that for dispformula = ~Dose, n_phis would be set equal to 2, and for
dispformula = ~poly(Dose,2) it would be 3.? What about for
dispformula = ~splines::ns(Dose,2) ?

Also (sorry for going on!) with glmmTMB() there is the possibility of
omitting random effects entirely and just trying to accommodate
over-dispersion by means of the beta binomial distribution:

glmmTMB(cbind(Dead, Alive) ~ (Trt + 0)/Dose,data = X,
?????? family = betabinomial(link = "cloglog"),
?????????????? dispformula = ~Dose)

Omitting random effects does not seem to be possible with mixed_model().
Is that correct?? Or have I simply not been using the right syntax?

Thanks for any words of wisdom that you can spare me.

cheers,

Rolf

On 19/03/20 10:03 am, D. Rizopoulos wrote:

The model seems to fit with the GLMMadaptive package, i.e.,

library("GLMMadaptive")

fm <- mixed_model(cbind(Dead, Alive) ~ (Trt + 0) / Dose, data = X,
?????????????????? random = ~ Dose | Rep,
?????????????????? family = beta.binomial(link = "cloglog"), n_phis = 1)

summary(fm)

where

beta.binomial <- function (link = "logit") {
???? .link <- link
???? env <- new.env(parent = .GlobalEnv)
???? assign(".link", link, envir = env)
???? stats <- make.link(link)
???? dbbinom <- function (x, size, prob, phi, log = FALSE) {
???????? A <- phi * prob
???????? B <- phi * (1 - prob)
???????? log_numerator <- lbeta(x + A, size - x + B)
???????? log_denominator <- lbeta(A, B)
???????? fact <- lchoose(size, x)
???????? if (log) {
???????????? fact + log_numerator - log_denominator
???????? } else {
???????????? exp(fact + log_numerator - log_denominator)
???????? }
???? }
???? log_dens <- function (y, eta, mu_fun, phis, eta_zi) {
???????? phi <- exp(phis)
???????? eta <- as.matrix(eta)
???????? mu_y <- mu_fun(eta)
???????? out <- if (NCOL(y) == 2L) {
???????????? dbbinom(y[, 1L], y[, 1L] + y[, 2L], mu_y, phi, TRUE)
???????? } else {
???????????? dbbinom(y, rep(1L, length(y)), mu_y, phi, TRUE)
???????? }
???????? attr(out, "mu_y") <- mu_y
???????? out
???? }
???? score_eta_fun <- function (y, mu, phis, eta_zi) {
???????? phi <- exp(phis)
???????? mu <- as.matrix(mu)
???????? if (NCOL(y) == 2L) {
???????????? size <- y[, 1L] + y[, 2L]
???????????? y <- y[, 1L]
???????? } else {
???????????? size <- rep(1L, length(y))
???????? }
???????? phi_mu <- phi * mu
???????? phi_1mu <- phi * (1 - mu)
???????? comp1 <- (digamma(y + phi_mu) - digamma(size - y + phi_1mu))
* phi
???????? comp2 <- (digamma(phi_mu) - digamma(phi_1mu)) * phi
???????? mu.eta <- switch(.link,
????????????????????????? "logit" = mu - mu * mu,
????????????????????????? "cloglog" = - (1 - mu) * log(1 - mu))
???????? out <- (comp1 - comp2) * mu.eta
???????? out
???? }
???? score_phis_fun <- function (y, mu, phis, eta_zi) {
???????? phi <- exp(phis)
???????? mu <- as.matrix(mu)
???????? if (NCOL(y) == 2L) {
???????????? size <- y[, 1L] + y[, 2L]
???????????? y <- y[, 1L]
???????? } else {
???????????? size <- rep(1L, length(y))
???????? }
???????? mu1 <- 1 - mu
???????? phi_mu <- phi * mu
???????? phi_1mu <- phi * mu1
???????? comp1 <- digamma(y + phi_mu) * mu + digamma(size - y +
phi_1mu) * mu1 -
???????????? digamma(size + phi)
???????? comp2 <- digamma(phi_mu) * mu + digamma(phi_1mu) * mu1 -
digamma(phi)
???????? out <- (comp1 - comp2) * phi
???????? out
???? }
???? structure(list(family = "beta binomial", link = stats$name,
??????????????????? linkfun = stats$linkfun, linkinv = stats$linkinv,
log_dens = log_dens,
??????????????????? score_eta_fun = score_eta_fun,
??????????????????? score_phis_fun = score_phis_fun),
?????????????? class = "family")
}

Best,
Dimitris


-----Original Message-----
From: R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> On
Behalf Of Rolf Turner
Sent: Sunday, March 15, 2020 4:32 AM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Error from glmmTMB().


I am getting an error, that I have no idea what to do about, from
glmmTMB():

library(glmmTMB)
fmla <- cbind(Dead, Alive) ~ (Trt + 0)/Dose + (Dose | Rep)
X??? <- dget("X.txt")
fit? <- glmmTMB(fmla, data = X, family = betabinomial(link = "cloglog"),
???????????????? dispformula = ~1)

Error in optimHess(par.fixed, obj$fn, obj$gr) :
?? gradient in optim evaluated to length 1 not 16 In addition: There
were 16 warnings (use warnings() to see them)

The warnings are all repetitions of

1: In nlminb(start = par, objective = fn, gradient = gr,? ... :
?? NA/NaN function evaluation

The error sounds to me like something is amiss in the code.

Can anyone confirm/deny/suggest what I might do to get this call to
glmmTMB() to run?