Package ‘glmmTMB’

April 5, 2023
Title Generalized Linear Mixed Models using Template Model Builder
Version 1.1.7
Description Fit linear and generalized linear mixed models with various
extensions, including zero-inflation. The models are fitted using maximum
likelihood estimation via 'TMB' (Template Model Builder). Random effects are
assumed to be Gaussian on the scale of the linear predictor and are integrated
out using the Laplace approximation. Gradients are calculated using automatic
differentiation.
License AGPL-3
Depends R (>= 3.2.0)
Imports methods, TMB (>= 1.9.0), lme4 (>= 1.1-18.9000), Matrix, nlme,
numDeriv
LinkingTo TMB, RcppEigen
Suggests knitr, rmarkdown, testthat, MASS, lattice, ggplot2 (>=
2.2.1), mlmRev, bbmle (>= 1.0.19), pscl, coda, reshape2, car
(>= 3.0.6), emmeans (>= 1.4), estimability, DHARMa, multcomp,
MuMIn, effects (>= 4.0-1), dotwhisker, broom, broom.mixed,
plyr, png, boot, texreg, xtable, huxtable, mvabund, parallel
SystemRequirements GNU make
VignetteBuilder knitr, rmarkdown

URL https://github.com/glmmTMB/glmmTMB
LazyData TRUE

BugReports https://github.com/glmmTMB/glmmTMB/issues
NeedsCompilation yes
Encoding UTF-8
RoxygenNote 7.2.3
Author Mollie Brooks [aut, cre] (<https://orcid.org/0000-0001-6963-8326>),
Ben Bolker [aut] (<https://orcid.org/0000-0002-2127-0443>),
Kasper Kristensen [aut],
Martin Maechler [aut] (<https://orcid.org/0000-0002-8685-9910>),

1
2 R topics documented:

Arni Magnusson [aut] (<https://orcid.org/0000-0003-2769-6741>),

Maeve McGillycuddy [ctb],
Hans Skaug [aut] (<https://orcid.org/0000-0003-4235-2592>),
Anders Nielsen [aut] (<https://orcid.org/0000-0001-9683-9262>),
Casper Berg [aut] (<https://orcid.org/0000-0002-3812-5269>),
Koen van Bentham [aut],
Nafis Sadat [ctb] (<https://orcid.org/0000-0001-5715-616X>),
Daniel Lüdecke [ctb] (<https://orcid.org/0000-0002-8895-3206>),
Russ Lenth [ctb],
Joseph O'Brien [ctb] (<https://orcid.org/0000-0001-9851-5077>),
Charles J. Geyer [ctb],
Mikael Jagan [ctb] (<https://orcid.org/0000-0002-3542-2938>),
Brenton Wiernik [ctb] (<https://orcid.org/0000-0001-9560-6336>),
Daniel B. Stouffer [ctb] (<https://orcid.org/0000-0001-9436-9674>)
Maintainer Mollie Brooks <mollieebrooks@gmail.com>
Repository CRAN
Date/Publication 2023-04-05 21:03:31 UTC

R topics documented:
Anova.glmmTMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
checkDepPackageVersion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
confint.glmmTMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
diagnose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
dtruncated_nbinom2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
epil2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
expandGrpVar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
family_params . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
findReTrmClasses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
fitTMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
fixef . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
formatVC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
formula.glmmTMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
getCapabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
getME.glmmTMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
getReStruc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
getXReTrms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
get_cor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
glmmTMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
glmmTMBControl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
isLMM.glmmTMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
nbinom2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
numFactor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
omp_check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Owls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
predict.glmmTMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
print.VarCorr.glmmTMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Anova.glmmTMB 3

profile.glmmTMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
ranef.glmmTMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
reinstalling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
residuals.glmmTMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
RHSForm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Salamanders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
sigma.glmmTMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
simulate.glmmTMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
simulate_new . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
terms.glmmTMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
up2date . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
vcov.glmmTMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
weights.glmmTMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Index 45

Anova.glmmTMB Downstream methods

Description
Methods have been written that allow glmmTMB objects to be used with several downstream packages
that enable different forms of inference. For some methods (Anova and emmeans, but not effects
at present), set the component argument to "cond" (conditional, the default), "zi" (zero-inflation)
or "disp" (dispersion) in order to produce results for the corresponding part of a glmmTMB model.
Support for emmeans also allows additional options component = "response" (response means
taking both the cond and zi components into account), and component = "cmean" (mean of the
[possibly truncated] conditional distribution).
In particular,

• car::Anova constructs type-II and type-III Anova tables for the fixed effect parameters of any
component
• the emmeans package computes estimated marginal means (previously known as least-squares
means) for the fixed effects of any component, or predictions with type = "response" or
type = "component". Note: In hurdle models, component = "cmean" produces means of the
truncated conditional distribution, while component = "cond", type = "response" produces
means of the untruncated conditional distribution.
• the effects package computes graphical tabular effect displays (only for the fixed effects of
the conditional component)

Usage
Anova.glmmTMB(
mod,
type = c("II", "III", 2, 3),
test.statistic = c("Chisq", "F"),
component = "cond",
4 Anova.glmmTMB

vcov. = vcov(mod)[[component]],
singular.ok,
...
)

Effect.glmmTMB(focal.predictors, mod, ...)

Arguments
mod a glmmTMB model
type type of test, "II", "III", 2, or 3. Roman numerals are equivalent to the corre-
sponding Arabic numerals. See Anova for details.
test.statistic unused: only valid choice is "Chisq" (i.e., Wald chi-squared test)
component which component of the model to test/analyze ("cond", "zi", or "disp") or, in
emmeans only, "response" or "cmean" as described in Details.
vcov. variance-covariance matrix (usually extracted automatically)
singular.ok OK to do ANOVA with singular models (unused) ?
... Additional parameters that may be supported by the method.
focal.predictors
a character vector of one or more predictors in the model in any order.

Details
While the examples below are disabled for earlier versions of R, they may still work; it may be
necessary to refer to private versions of methods, e.g. glmmTMB:::Anova.glmmTMB(model, ...).

Examples
warp.lm <- glmmTMB(breaks ~ wool * tension, data = warpbreaks)
salamander1 <- up2date(readRDS(system.file("example_files","salamander1.rds",package="glmmTMB")))
if (require(emmeans)) withAutoprint({
emmeans(warp.lm, poly ~ tension | wool)
emmeans(salamander1, ~ mined, type="response") # conditional means
emmeans(salamander1, ~ mined, component="cmean") # same as above, but re-gridded
emmeans(salamander1, ~ mined, component="zi", type="response") # zero probabilities
emmeans(salamander1, ~ mined, component="response") # response means including both components
})
if (getRversion() >= "3.6.0") {
if (require(car)) withAutoprint({
Anova(warp.lm,type="III")
Anova(salamander1)
Anova(salamander1, component="zi")
})
if (require(effects)) withAutoprint({
plot(allEffects(warp.lm))
plot(allEffects(salamander1))
})
}
checkDepPackageVersion 5

checkDepPackageVersion
Check for version mismatch in dependent binary packages

Description
Check for version mismatch in dependent binary packages

Usage
checkDepPackageVersion(
dep_pkg = "TMB",
this_pkg = "glmmTMB",
write_file = FALSE,
warn = TRUE
)

Arguments
dep_pkg upstream package
this_pkg downstream package
write_file (logical) write version file and quit?
warn give warning?

Value
logical: TRUE if the binary versions match

confint.glmmTMB Calculate confidence intervals

Description
Calculate confidence intervals

Usage
## S3 method for class 'glmmTMB'
confint(
object,
parm = NULL,
level = 0.95,
method = c("wald", "Wald", "profile", "uniroot"),
component = c("all", "cond", "zi", "other"),
estimate = TRUE,
6 confint.glmmTMB

include_mapped = FALSE,
parallel = c("no", "multicore", "snow"),
ncpus = getOption("profile.ncpus", 1L),
cl = NULL,
full = FALSE,
...
)

Arguments
object glmmTMB fitted object.
parm which parameters to profile, specified
• by index (position) [after component selection for confint, if any]
• by name (matching the row/column names of vcov(object,full=TRUE))
• as "theta_" (random-effects variance-covariance parameters), "beta_" (con-
ditional and zero-inflation parameters), or "disp_" or "sigma" (dispersion
parameters)
Parameter indexing by number may give unusual results when some parameters
have been fixed using the map argument: please report surprises to the package
maintainers.
level Confidence level.
method ’wald’, ’profile’, or ’uniroot’: see Details function)
component Which of the three components ’cond’, ’zi’ or ’other’ to select. Default is to
select ’all’.
estimate (logical) add a third column with estimate ?
include_mapped include dummy rows for mapped (i.e. fixed-value) parameters?
parallel method (if any) for parallel computation
ncpus number of CPUs/cores to use for parallel computation
cl cluster to use for parallel computation
full CIs for all parameters (including dispersion) ?
... arguments may be passed to profile.glmmTMB (and possibly from there to
tmbprofile) or tmbroot

Details
Available methods are

"wald" These intervals are based on the standard errors calculated for parameters on the scale of
their internal parameterization depending on the family. Derived quantities such as standard
deviation parameters and dispersion parameters are back-transformed. It follows that confi-
dence intervals for these derived quantities are typically asymmetric.
"profile" This method computes a likelihood profile for the specified parameter(s) using profile.glmmTMB;
fits a spline function to each half of the profile; and inverts the function to find the specified
confidence interval.
diagnose 7

"uniroot" This method uses the uniroot function to find critical values of one-dimensional profile
functions for each specified parameter.

At present, "wald" returns confidence intervals for variance parameters on the standard devia-
tion/correlation scale, while "profile" and "uniroot" report them on the underlying ("theta") scale:
for each random effect, the first set of parameter values are standard deviations on the log scale,
while remaining parameters represent correlations on the scaled Cholesky scale. For a random ef-
fects model with two elements (such as a random-slopes model, or a random effect of factor√ with
two levels), there is a single correlation parameter θ; the correlation is equal to ρ = θ/ 1 + θ2 .
For random-effects terms with more than two elements, the mapping is more complicated: see
https://github.com/glmmTMB/glmmTMB/blob/master/misc/glmmTMB_corcalcs.ipynb

Examples
data(sleepstudy, package="lme4")
model <- glmmTMB(Reaction ~ Days + (1|Subject), sleepstudy)
model2 <- glmmTMB(Reaction ~ Days + (1|Subject), sleepstudy,
dispformula= ~I(Days>8))
confint(model) ## Wald/delta-method CIs
confint(model,parm="theta_") ## Wald/delta-method CIs
confint(model,parm=1,method="profile")

diagnose diagnose model problems

Description
EXPERIMENTAL. For a given model, this function attempts to isolate potential causes of con-
vergence problems. It checks (1) whether there are any unusually large coefficients; (2) whether
there are any unusually scaled predictor variables; (3) if the Hessian (curvature of the negative
log-likelihood surface at the MLE) is positive definite (i.e., whether the MLE really represents an
optimum). For each case it tries to isolate the particular parameters that are problematic.

Usage
diagnose(
fit,
eval_eps = 1e-05,
evec_eps = 0.01,
big_coef = 10,
big_sd_log10 = 3,
big_zstat = 5,
check_coefs = TRUE,
check_zstats = TRUE,
check_hessian = TRUE,
check_scales = TRUE,
explain = TRUE
)
8 dtruncated_nbinom2

Arguments
fit a glmmTMB fit
eval_eps numeric tolerance for ’bad’ eigenvalues
evec_eps numeric tolerance for ’bad’ eigenvector elements
big_coef numeric tolerance for large coefficients
big_sd_log10 numeric tolerance for badly scaled parameters (log10 scale), i.e. for default
value of 3, predictor variables with sd less than 1e-3 or greater than 1e3 will be
flagged)
big_zstat numeric tolerance for Z-statistic
check_coefs identify large-magnitude coefficients? (Only checks conditional-model param-
eters if a (log, logit, cloglog, probit) link is used. Always checks zero-inflation,
dispersion, and random-effects parameters. May produce false positives if pre-
dictor variables have extremely large scales.)
check_zstats identify parameters with unusually large Z-statistics (ratio of standard error to
mean)? Identifies likely failures of Wald confidence intervals/p-values.
check_hessian identify non-positive-definite Hessian components?
check_scales identify predictors with unusually small or large scales?
explain provide detailed explanation of each test?

Details
Problems in one category (e.g. complete separation) will generally also appear in "downstream" cat-
egories (e.g. non-positive-definite Hessians). Therefore, it is generally advisable to try to deal with
problems in order, e.g. address problems with complete separation first, then re-run the diagnostics
to see whether Hessian problems persist.

Value
a logical value based on whether anything questionable was found

dtruncated_nbinom2 truncated distributions

Description
Probability functions for k-truncated Poisson and negative binomial distributions.

Usage
dtruncated_nbinom2(x, size, mu, k = 0, log = FALSE)

dtruncated_poisson(x, lambda, k = 0, log = FALSE)

dtruncated_nbinom1(x, phi, mu, k = 0, log = FALSE)

epil2 9

Arguments
x value
size number of trials/overdispersion parameter
mu mean parameter
k truncation parameter
log (logical) return log-probability?
lambda mean parameter
phi overdispersion parameter

epil2 Seizure Counts for Epileptics - Extended

Description
Extended version of the epil dataset of the MASS package. The three transformed variables Visit,
Base, and Age used by Booth et al. (2003) have been added to epil.

Usage
epil2

Format
A data frame with 236 observations on the following 12 variables:
y an integer vector.
trt a factor with levels "placebo" and "progabide".
base an integer vector.
age an integer vector.
V4 an integer vector.
subject an integer vector.
period an integer vector.
lbase a numeric vector.
lage a numeric vector.
Visit (rep(1:4,59) - 2.5) / 5.
Base log(base/4).
Age log(age).

References
Booth, J.G., G. Casella, H. Friedl, and J.P. Hobert. (2003) Negative binomial loglinear mixed
models. Statistical Modelling 3, 179–191.
10 family_params

Examples

epil2$subject <- factor(epil2$subject)

op <- options(digits=3)
(fm <- glmmTMB(y ~ Base*trt + Age + Visit + (Visit|subject),
data=epil2, family=nbinom2))
meths <- methods(class = class(fm))
if((Rv <- getRversion()) > "3.1.3") {
funs <- attr(meths, "info")[, "generic"]
funs <- setdiff(funs, "profile") ## too slow! pkgdown is trying to run this??
for(fun in funs[is.na(match(funs, "getME"))]) {
cat(sprintf("%s:\n-----\n", fun))
r <- tryCatch( get(fun)(fm), error=identity)
if (inherits(r, "error")) cat("** Error:", r$message,"\n")
else tryCatch( print(r) )
cat(sprintf("---end{%s}--------------\n\n", fun))
}
}
options(op)

expandGrpVar apply

Description
apply

Usage
expandGrpVar(f)

Arguments
f a language object (an atom of a formula) expandGrpVar(quote(x*y)) expandGr-
pVar(quote(x/y))

family_params Retrieve family-specific parameters

Description
Most conditional distributions have only parameters governing their location (retrieved via predict)
and scale (sigma). A few (e.g. Tweedie, Student t, ordered beta) are characterized by one or more
additional parameters.
findReTrmClasses 11

Usage
family_params(object)

Arguments
object glmmTMB object

Value
a named numeric vector

findReTrmClasses list of specials – taken from enum.R

Description
list of specials – taken from enum.R

Usage
findReTrmClasses()

fitTMB Optimize a TMB model and package results

Description
This function (called internally by glmmTMB) runs the actual model optimization, after all of the
appropriate structures have been set up. It can be useful to run glmmTMB with doFit=TRUE, adjust
the components as required, and then finish the fitting process with fitTMB (however, it is the user’s
responsibility to make sure that any modifications create an internally consistent final fitted object).

Usage
fitTMB(TMBStruc, doOptim = TRUE)

Arguments
TMBStruc a list containing lots of stuff ...
doOptim logical; do optimization? If FALSE, return TMB object

Examples
m0 <- glmmTMB(count ~ mined + (1|site),
family=poisson, data=Salamanders, doFit=FALSE)
names(m0)
fitTMB(m0)
12 fixef

fixef Extract fixed-effects estimates

Description

Extract Fixed Effects

Usage

## S3 method for class 'glmmTMB'

fixef(object, ...)

Arguments

object any fitted model object from which fixed effects estimates can be extracted.
... optional additional arguments. Currently none are used in any methods.

Details

Extract fixed effects from a fitted glmmTMB model.

The print method for fixef.glmmTMB object only displays non-trivial components: in particular,
the dispersion parameter estimate is not printed for models with a single (intercept) dispersion
parameter (see examples)

Value

an object of class fixef.glmmTMB comprising a list of components (cond, zi, disp), each contain-
ing a (possibly zero-length) numeric vector of coefficients

Examples

data(sleepstudy, package = "lme4")

fm1 <- glmmTMB(Reaction ~ Days, sleepstudy)
(f1 <- fixef(fm1))
f1$cond
## show full coefficients, including empty z-i model and
## constant dispersion parameter
print(f1, print_trivials = TRUE)
formatVC 13

formatVC Format the ’VarCorr’ Matrix of Random Effects

Description
"format()" the ’VarCorr’ matrix of the random effects – for print()ing and show()ing

Usage
formatVC(
varcor,
digits = max(3, getOption("digits") - 2),
comp = "Std.Dev.",
formatter = format,
useScale = attr(varcor, "useSc"),
...
)

Arguments
varcor a VarCorr (-like) matrix with attributes.
digits the number of significant digits.
comp character vector of length one or two indicating which columns out of "Variance"
and "Std.Dev." should be shown in the formatted output.
formatter the function to be used for formatting the standard deviations and or variances
(but not the correlations which (currently) are always formatted as "0.nnn"
useScale whether to report a scale parameter (e.g. residual standard deviation)
... optional arguments for formatter(*) in addition to the first (numeric vector)
and digits.

Value
a character matrix of formatted VarCorr entries from varc.

formula.glmmTMB Extract the formula of a glmmTMB object

Description
Extract the formula of a glmmTMB object

Usage
## S3 method for class 'glmmTMB'
formula(x, fixed.only = FALSE, component = c("cond", "zi", "disp"), ...)
14 getCapabilities

Arguments

x a glmmTMB object
fixed.only (logical) drop random effects, returning only the fixed-effect component of the
formula?
component formula for which component of the model to return (conditional, zero-inflation,
or dispersion)
... unused, for generic consistency

getCapabilities List model options that glmmTMB knows about

Description

List model options that glmmTMB knows about

Usage

getCapabilities(what = "all", check = FALSE)

Arguments

what (character) which type of model structure to report on ("all","family","link","covstruct")

check (logical) do brute-force checking to test whether families are really implemented
(only available for what="family")

Value

if check==FALSE, returns a vector of the names (or a list of name vectors) of allowable entries; if
check==TRUE, returns a logical vector of working families

Note

these are all the options that are defined internally; they have not necessarily all been implemented
(FIXME!)
getME.glmmTMB 15

getME.glmmTMB Extract or Get Generalize Components from a Fitted Mixed Effects

Model

Description
Extract or Get Generalize Components from a Fitted Mixed Effects Model

Usage
## S3 method for class 'glmmTMB'
getME(object, name = c("X", "Xzi", "Z", "Zzi", "Xd", "theta", "beta"), ...)

Arguments
object a fitted glmmTMB object
name of the component to be retrieved
... ignored, for method compatibility

See Also
getME Get generic and re-export:

getReStruc Calculate random effect structure Calculates number of random ef-

fects, number of parameters, block size and number of blocks. Mostly
for internal use.

Description
Calculate random effect structure Calculates number of random effects, number of parameters,
block size and number of blocks. Mostly for internal use.

Usage
getReStruc(reTrms, ss = NULL, aa = NULL, reXterms = NULL, fr = NULL)

Arguments
reTrms random-effects terms list
ss a character string indicating a valid covariance structure. Must be one of names(glmmTMB:::.valid_covs
default is to use an unstructured variance-covariance matrix ("us") for all blocks).
aa additional arguments (i.e. rank)
reXterms terms objects corresponding to each RE term
fr model frame
16 getXReTrms

Value

a list

blockNumTheta number of variance covariance parameters per term

blockSize size (dimension) of one block
blockReps number of times the blocks are repeated (levels)
covCode structure code

Examples

data(sleepstudy, package="lme4")
rt <- lme4::lFormula(Reaction~Days+(1|Subject)+(0+Days|Subject),
sleepstudy)$reTrms
rt2 <- lme4::lFormula(Reaction~Days+(Days|Subject),
sleepstudy)$reTrms
getReStruc(rt)

getXReTrms Create X and random effect terms from formula

Description

Create X and random effect terms from formula

Usage

getXReTrms(formula, mf, fr, ranOK = TRUE, type = "", contrasts, sparse = FALSE)

Arguments

formula current formula, containing both fixed & random effects

mf matched call
fr full model frame
ranOK random effects allowed here?
type label for model type
contrasts a list of contrasts (see ?glmmTMB)
sparse (logical) return sparse model matrix?
get_cor 17

Value
a list composed of

X design matrix for fixed effects

Z design matrix for random effects
reTrms output from mkReTrms from lme4
ss splitform of the formula
aa additional arguments, used to obtain rank
terms terms for the fixed effects
offset offset vector, or vector of zeros if offset not specified
reXterms terms for the model matrix in each RE term

get_cor translate vector of correlation parameters to correlation values

Description
translate vector of correlation parameters to correlation values

Usage
get_cor(theta)

put_cor(C)

Arguments
theta vector of internal correlation parameters (elements of scaled Cholesky factor, in
row-major order)
C a correlation matrix

Details
These functions follow the definition at http://kaskr.github.io/adcomp/classdensity_1_
1UNSTRUCTURED__CORR__t.html: if L is the lower-triangular matrix with 1 on the diagonal and
the correlation parameters in the lower triangle, then the correlation matrix is defined as Σ =
D−1/2 LL> D−1/2 >
p , where D = diag(LL ). For a single correlation parameter θ0 , this works
out to ρ = θ0 / 1 + θ0 . The get_cor function returns the elements of the lower triangle of the
2

correlation matrix, in column-major order.

Value
a vector of correlation values (get_cor) or glmmTMB scaled-correlation parameters (put_cor)
18 glmmTMB

Examples
th0 <- 0.5
stopifnot(all.equal(get_cor(th0),th0/sqrt(1+th0^2)))
get_cor(c(0.5,0.2,0.5))
C <- matrix(c(1, 0.2, 0.1,
0.2, 1, -0.2,
0.1,-0.2, 1),
3, 3)
## test: round-trip (almostl results in lower triangle only)
stopifnot(all.equal(get_cor(put_cor(C)),
C[lower.tri(C)]))

glmmTMB Fit Models with TMB

Description
Fit a generalized linear mixed model (GLMM) using Template Model Builder (TMB).

Usage
glmmTMB(
formula,
data = NULL,
family = gaussian(),
ziformula = ~0,
dispformula = ~1,
weights = NULL,
offset = NULL,
contrasts = NULL,
na.action,
se = TRUE,
verbose = FALSE,
doFit = TRUE,
control = glmmTMBControl(),
REML = FALSE,
start = NULL,
map = NULL,
sparseX = NULL
)

Arguments
formula combined fixed and random effects formula, following lme4 syntax.
data data frame (tibbles are OK) containing model variables. Not required, but strongly
recommended; if data is not specified, downstream methods such as prediction
glmmTMB 19

with new data (predict(fitted_model, newdata = ...)) will fail. If it is nec-

essary to call glmmTMB with model variables taken from the environment rather
than from a data frame, specifying data=NULL will suppress the warning mes-
sage.
family a family function, a character string naming a family function, or the result of
a call to a family function (variance/link function) information. See family
for a generic discussion of families or family_glmmTMB for details of glmmTMB-
specific families.
ziformula a one-sided (i.e., no response variable) formula for zero-inflation combining
fixed and random effects: the default ~0 specifies no zero-inflation. Specifying
~. sets the zero-inflation formula identical to the right-hand side of formula
(i.e., the conditional effects formula); terms can also be added or subtracted.
When using ~. as the zero-inflation formula in models where the condi-
tional effects formula contains an offset term, the offset term will automat-
ically be dropped. The zero-inflation model uses a logit link.
dispformula a one-sided formula for dispersion containing only fixed effects: the default ~1
specifies the standard dispersion given any family. The argument is ignored for
families that do not have a dispersion parameter. For an explanation of the dis-
persion parameter for each family, see sigma. The dispersion model uses a log
link. In Gaussian mixed models, dispformula=~0 fixes the residual variance to
be 0 (actually a small non-zero value), forcing variance into the random effects.
The precise value can be controlled via control=glmmTMBControl(zero_dispval=...);
the default value is sqrt(.Machine$double.eps).
weights weights, as in glm. Not automatically scaled to have sum 1.
offset offset for conditional model (only).
contrasts an optional list, e.g., list(fac1="contr.sum"). See the contrasts.arg of
model.matrix.default.
na.action a function that specifies how to handle observations containing NAs. The default
action (na.omit, inherited from the ’factory fresh’ value of getOption("na.action"))
strips any observations with any missing values in any variables. Using na.action
= na.exclude will similarly drop observations with missing values while fitting
the model, but will fill in NA values for the predicted and residual values for cases
that were excluded during the fitting process because of missingness.
se whether to return standard errors.
verbose whether progress indication should be printed to the console.
doFit whether to fit the full model, or (if FALSE) return the preprocessed data and
parameter objects, without fitting the model.
control control parameters, see glmmTMBControl.
REML whether to use REML estimation rather than maximum likelihood.
start starting values, expressed as a list with possible components beta, betazi,
betad (fixed-effect parameters for conditional, zero-inflation, dispersion mod-
els); b, bzi (conditional modes for conditional and zero-inflation models); theta,
thetazi (random-effect parameters, on the standard deviation/Cholesky scale,
for conditional and z-i models); psi (extra family parameters, e.g., shape for
Tweedie models).
20 glmmTMB

map a list specifying which parameter values should be fixed to a constant value
rather than estimated. map should be a named list containing factors correspond-
ing to a subset of the internal parameter names (see start parameter). Distinct
factor values are fitted as separate parameter values, NA values are held fixed:
e.g., map=list(beta=factor(c(1,2,3,NA))) would fit the first three fixed-
effect parameters of the conditional model and fix the fourth parameter to its
starting value. In general, users will probably want to use start to specify non-
default starting values for fixed parameters. See MakeADFun for more details.
sparseX a named logical vector containing (possibly) elements named "cond", "zi", "disp"
to indicate whether fixed-effect model matrices for particular model components
should be generated as sparse matrices, e.g. c(cond=TRUE). Default is all FALSE

Details
• Binomial models with more than one trial (i.e., not binary/Bernoulli) can either be specified in
the form prob ~ ..., weights = N, or in the more typical two-column matrix cbind(successes,failures)~...
form.
• Behavior of REML=TRUE for Gaussian responses matches lme4::lmer. It may also be useful
in some cases with non-Gaussian responses (Millar 2011). Simulations should be done first to
verify.
• Because the df.residual method for glmmTMB currently counts the dispersion parameter,
users should multiply this value by sqrt(nobs(fit) / (1+df.residual(fit))) when com-
paring with lm.
• Although models can be fitted without specifying a data argument, its use is strongly recom-
mended; drawing model components from the global environment, or using df$var notation
within model formulae, can lead to confusing (and sometimes hard-to-detect) errors.
• By default, vector-valued random effects are fitted with unstructured (general symmetric pos-
itive definite) variance-covariance matrices. Structured variance-covariance matrices can be
specified in the form struc(terms|group), where struc is one of
– diag (diagonal, heterogeneous variance)
– ar1 (autoregressive order-1, homogeneous variance)
– cs (compound symmetric, heterogeneous variance)
– ou (* Ornstein-Uhlenbeck, homogeneous variance)
– exp (* exponential autocorrelation)
– gau (* Gaussian autocorrelation)
– mat (* Matérn process correlation)
– toep (* Toeplitz)
– rr (reduced rank/factor-analytic model)
– homdiag (diagonal, homogeneous variance)
Structures marked with * are experimental/untested. See vignette("covstruct", package
= "glmmTMB") for more information.
• For backward compatibility, the family argument can also be specified as a list compris-
ing the name of the distribution and the link function (e.g. list(family="binomial",
link="logit")). However, this alternative is now deprecated; it produces a warning and
will be removed at some point in the future. Furthermore, certain capabilities such as Pear-
son residuals or predictions on the data scale will only be possible if components such as
variance and linkfun are present, see family.
glmmTMB 21

Note
For more information about the glmmTMB package, see Brooks et al. (2017) and the vignette(package="glmmTMB")
collection. For the underlying TMB package that performs the model estimation, see Kristensen et
al. (2016).

References
Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., Skaug,
H. J., Mächler, M. and Bolker, B. M. (2017). glmmTMB balances speed and flexibility among
packages for zero-inflated generalized linear mixed modeling. The R Journal, 9(2), 378–400.
Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H. and Bell, B. (2016). TMB: Automatic differen-
tiation and Laplace approximation. Journal of Statistical Software, 70, 1–21.
Millar, R. B. (2011). Maximum Likelihood Estimation and Inference: With Examples in R, SAS and
ADMB. Wiley, New York.

Examples

(m1 <- glmmTMB(count ~ mined + (1|site),

zi=~mined,
family=poisson, data=Salamanders))
summary(m1)
##' ## Zero-inflated negative binomial model
(m2 <- glmmTMB(count ~ spp + mined + (1|site),
zi=~spp + mined,
family=nbinom2, data=Salamanders))

## Hurdle Poisson model

(m3 <- glmmTMB(count ~ spp + mined + (1|site),
zi=~spp + mined,
family=truncated_poisson, data=Salamanders))

## Binomial model
data(cbpp, package="lme4")
(bovine <- glmmTMB(cbind(incidence, size-incidence) ~ period + (1|herd),
family=binomial, data=cbpp))

## Dispersion model
sim1 <- function(nfac=40, nt=100, facsd=0.1, tsd=0.15, mu=0, residsd=1)
{
dat <- expand.grid(fac=factor(letters[1:nfac]), t=1:nt)
n <- nrow(dat)
dat$REfac <- rnorm(nfac, sd=facsd)[dat$fac]
dat$REt <- rnorm(nt, sd=tsd)[dat$t]
dat$x <- rnorm(n, mean=mu, sd=residsd) + dat$REfac + dat$REt
dat
}
set.seed(101)
d1 <- sim1(mu=100, residsd=10)
d2 <- sim1(mu=200, residsd=5)
d1$sd <- "ten"
22 glmmTMBControl

d2$sd <- "five"

dat <- rbind(d1, d2)
m0 <- glmmTMB(x ~ sd + (1|t), dispformula=~sd, data=dat)
fixef(m0)$disp
c(log(5^2), log(10^2)-log(5^2)) # expected dispersion model coefficients

## Using 'map' to fix random-effects SD to 10

m1_map <- update(m1, map=list(theta=factor(NA)),
start=list(theta=log(10)))
VarCorr(m1_map)

glmmTMBControl Control parameters for glmmTMB optimization

Description
Control parameters for glmmTMB optimization

Usage
glmmTMBControl(
optCtrl = NULL,
optArgs = list(),
optimizer = nlminb,
profile = FALSE,
collect = FALSE,
parallel = getOption("glmmTMB.cores", 1L),
eigval_check = TRUE,
zerodisp_val = log(sqrt(.Machine$double.eps)),
start_method = list(method = NULL, jitter.sd = 0),
rank_check = c("warning", "adjust", "stop", "skip"),
conv_check = c("warning", "skip")
)

Arguments
optCtrl Passed as argument control to optimizer. Default value (if default nlminb
optimizer is used): list(iter.max=300, eval.max=400)
optArgs additional arguments to be passed to optimizer function (e.g.: list(method="BFGS")
when optimizer=optim)
optimizer Function to use in model fitting. See Details for required properties of this
function.
profile (logical) Experimental option to improve speed and robustness when a model
has many fixed effects
glmmTMBControl 23

collect (logical) Experimental option to improve speed by recognizing duplicated ob-

servations.
parallel (integer) Set number of OpenMP threads to evaluate the negative log-likelihood
in parallel. The default is to evaluate models serially (i.e. single-threaded); users
can set a default value for an R session via options(glmmTMB.cores=<value>).
At present reduced-rank models (i.e., a covariance structure using rr(...)) can-
not be fitted in parallel; the number of threads will be automatically set to 1, with
a warning if this overrides the user-specified value.
eigval_check Check eigenvalues of variance-covariance matrix? (This test may be very slow
for models with large numbers of fixed-effect parameters.)
zerodisp_val value of the dispersion parameter when dispformula=~0 is specified
start_method (list) Options to initialize the starting values when fitting models with reduced-
rank (rr) covariance structures; jitter.sd adds variation to the starting values
of latent variables when method = "res".
rank_check Check whether all parameters in fixed-effects models are identifiable? This test
may be slow for models with large numbers of fixed-effect parameters, therefore
default value is ’warning’. Alternatives include ’skip’ (no check), ’stop’ (throw
an error), and ’adjust’ (drop redundant columns from the fixed-effect model
matrix).
conv_check Do basic checks of convergence (check for non-positive definite Hessian and
non-zero convergence code from optimizer). Default is ’warning’; ’skip’ ignores
these tests (not recommended for general use!)

Details
By default, glmmTMB uses the nonlinear optimizer nlminb for parameter estimation. Users may
sometimes need to adjust optimizer settings in order to get models to converge. For instance, the
warning ‘iteration limit reached without convergence’ may be fixed by increasing the number of
iterations using (e.g.)
glmmTMBControl(optCtrl=list(iter.max=1e3,eval.max=1e3)).
Setting profile=TRUE allows glmmTMB to use some special properties of the optimization problem
in order to speed up estimation in cases with many fixed effects.
Control parameters may depend on the model specification. The value of the controls is evaluated
inside an R object that is derived from the output of the mkTMBStruc function. For example, to
specify that profile should be enabled if the model has more than 5 fixed-effect parameters, specify
profile=quote(length(parameters$beta)>=5)
The optimizer argument can be any optimization (minimizing) function, provided that:

• the first three arguments, in order, are the starting values, objective function, and gradient
function;
• the function also takes a control argument;
• the function returns a list with elements (at least) par, objective, convergence (0 if conver-
gence is successful) and message (glmmTMB automatically handles output from optim(), by
renaming the value component to objective)
24 isLMM.glmmTMB

Examples
## fit with default (nlminb) and alternative (optim/BFGS) optimizer
m1 <- glmmTMB(count~ mined, family=poisson, data=Salamanders)
m1B <- update(m1, control=glmmTMBControl(optimizer=optim,
optArgs=list(method="BFGS")))
## estimates are *nearly* identical:
all.equal(fixef(m1), fixef(m1B))

isLMM.glmmTMB support methods for parametric bootstrapping

Description
see refit and isLMM for details

Usage
## S3 method for class 'glmmTMB'
isLMM(x, ...)

## S3 method for class 'glmmTMB'

refit(object, newresp, ...)

Arguments
x a fitted glmmTMB object
... additional arguments (for generic consistency; ignored)
object a fitted glmmTMB object
newresp a new response vector

Details
These methods are still somewhat experimental (check your results carefully!), but they should
allow parametric bootstrapping. They work by copying and replacing the original response column
in the data frame passed to glmmTMB, so they will only work properly if (1) the data frame is still
available in the environment and (2) the response variable is specified as a single symbol (e.g.
proportion or a two-column matrix constructed on the fly with cbind(). Untested with binomial
models where the response is specified as a factor.

Examples
if (requireNamespace("lme4")) {
## Not run:
fm1 <- glmmTMB(count~mined+(1|spp),
ziformula=~mined,
data=Salamanders,
family=nbinom1)
nbinom2 25

## single parametric bootstrap step: refit with data simulated from original model
fm1R <- refit(fm1, simulate(fm1)[[1]])
## the bootMer function from lme4 provides a wrapper for doing multiple refits
## with a specified summary function
b1 <- lme4::bootMer(fm1, FUN=function(x) fixef(x)$zi, nsim=20, .progress="txt")
if (requireNamespace("boot")) {
boot.ci(b1,type="perc")
}
## can run in parallel: may need to set up cluster explicitly,
## use clusterEvalQ() to load packages on workers
if (requireNamespace("parallel")) {
cl <- parallel::makeCluster(2)
parallel::clusterEvalQ(cl, library("lme4"))
parallel::clusterEvalQ(cl, library("glmmTMB"))
b2 <- lme4::bootMer(fm1, FUN = function(x) fixef(x)$cond,
nsim = 10, ncpus = 2, cl = cl, parallel = "snow")
}

## End(Not run)
}

nbinom2 Family functions for glmmTMB

Description
Family functions for glmmTMB

Usage
nbinom2(link = "log")

nbinom1(link = "log")

compois(link = "log")

truncated_compois(link = "log")

genpois(link = "log")

truncated_genpois(link = "log")

truncated_poisson(link = "log")

truncated_nbinom2(link = "log")

truncated_nbinom1(link = "log")

beta_family(link = "logit")
26 nbinom2

betabinomial(link = "logit")

tweedie(link = "log")

ziGamma(link = "inverse")

t_family(link = "identity")

ordbeta(link = "logit")

Arguments
link (character) link function for the conditional mean ("log", "logit", "probit", "in-
verse", "cloglog", "identity", or "sqrt")

Details
If specified, the dispersion model uses a log link. Denoting the variance as V , the dispersion
parameter as φ = exp(η) (where η is the linear predictor from the dispersion model), and the
predicted mean as µ:

gaussian (from base R): constant V = φ

Gamma (from base R) phi is the shape parameter. V = µφ
ziGamma a modified version of Gamma that skips checks for zero values, allowing it to be used to
fit hurdle-Gamma models
nbinom2 Negative binomial distribution: quadratic parameterization (Hardin & Hilbe 2007). V =
µ(1 + µ/φ) = µ + µ2 /φ.
nbinom1 Negative binomial distribution: linear parameterization (Hardin & Hilbe 2007). V =
µ(1 + φ)
truncated_nbinom2 Zero-truncated version of nbinom2: variance expression from Shonkwiler
2016. Simulation code (for this and the other truncated count distributions) is taken from C.
Geyer’s functions in the aster package; the algorithms are described in this vignette.
compois Conway-Maxwell Poisson distribution: parameterized with the exact mean (Huang 2017),
which differs from the parameterization used in the COMPoissonReg package (Sellers &
Shmueli 2010, Sellers & Lotze 2015). V = µφ.
genpois Generalized Poisson distribution (Consul & Famoye 1992). V = µ exp(η). (Note that
Consul & Famoye (1992) define φ differently.) Our implementation is taken from the HMMpa
package, based on Joe and Zhu (2005) and implemented by Vitali Witowski.
beta Beta distribution: parameterization of Ferrari and Cribari-Neto (2004) and the betareg pack-
age (Cribari-Neto and Zeileis 2010); V = µ(1 − µ)/(φ + 1)
betabinomial Beta-binomial distribution: parameterized according to Morris (1997). V = µ(1 −
µ)(n(φ + n)/(φ + 1))
tweedie Tweedie distribution: V = φµp ower. The power parameter is restricted to the interval
1 < power < 2. Code taken from the tweedie package, written by Peter Dunn.
nbinom2 27

t_family Student-t distribution with adjustable scale and location parameters (also called a Pear-
son type VII distribution). The shape (degrees of freedom parameter) is fitted with a log link; it
may be often be useful to fix the shape parameter using start = list(psi = log(fixed_df)),
map = list(psi = factor(NA)).
ordbeta Ordered beta regression from Kubinec (2022); fits continuous (e.g. proportion) data in the
closed interval [0,1].

Value

returns a list with (at least) components

family length-1 character vector giving the family name

link length-1 character vector specifying the link function
variance a function of either 1 (mean) or 2 (mean and dispersion parameter) arguments
giving a value proportional to the predicted variance (scaled by sigma(.))

References

• Consul PC & Famoye F (1992). "Generalized Poisson regression model." Communications in

Statistics: Theory and Methods 21:89–109.
• Ferrari SLP, Cribari-Neto F (2004). "Beta Regression for Modelling Rates and Proportions."
J. Appl. Stat. 31(7), 799-815.
• Hardin JW & Hilbe JM (2007). "Generalized linear models and extensions." Stata Press.
• Huang A (2017). "Mean-parametrized Conway–Maxwell–Poisson regression models for dis-
persed counts." Statistical Modelling 17(6), 1-22.
• Joe H, Zhu R (2005). "Generalized Poisson Distribution: The Property of Mixture of Poisson
and Comparison with Negative Binomial Distribution." Biometrical Journal 47(2): 219–29.
doi: 10.1002/bimj.200410102.
• Morris W (1997). "Disentangling Effects of Induced Plant Defenses and Food Quantity on
Herbivores by Fitting Nonlinear Models." American Naturalist 150:299-327.
• Kubinec R (2022). "Ordered Beta Regression: A Parsimonious, Well-Fitting Model for Con-
tinuous Data with Lower and Upper Bounds." Political Analysis. doi:10.1017/pan.2022.20.
• Sellers K & Lotze T (2015). "COMPoissonReg: Conway-Maxwell Poisson (COM-Poisson)
Regression". R package version 0.3.5. https://CRAN.R-project.org/package=COMPoissonReg
• Sellers K & Shmueli G (2010) "A Flexible Regression Model for Count Data." Annals of
Applied Statistics 4(2), 943–61. doi: 10.1214/09AOAS306.
• Shonkwiler, J. S. (2016). "Variance of the truncated negative binomial distribution." Journal
of Econometrics 195(2), 209–210. doi: 10.1016/j.jeconom.2016.09.002.
28 numFactor

numFactor Factor with numeric interpretable levels.

Description
Create a factor with numeric interpretable factor levels.

Usage
numFactor(x, ...)

parseNumLevels(levels)

Arguments
x Vector, matrix or data.frame that constitute the coordinates.
... Additional vectors, matrices or data.frames that constitute the coordinates.
levels Character vector to parse into numeric values.

Details
Some glmmTMB covariance structures require extra information, such as temporal or spatial coor-
dinates. numFactor allows to associate such extra information as part of a factor via the factor
levels. The original numeric coordinates are recoverable without loss of precision using the func-
tion parseNumLevels. Factor levels are sorted coordinate wise from left to right: first coordinate is
fastest running.

Value
Factor with specialized coding of levels.

Examples
## 1D example
numFactor(sample(1:5,20,TRUE))
## 2D example
coords <- cbind( sample(1:5,20,TRUE), sample(1:5,20,TRUE) )
(f <- numFactor(coords))
parseNumLevels(levels(f)) ## Sorted
## Used as part of a model.matrix
model.matrix( ~f )
## parseNumLevels( colnames(model.matrix( ~f )) )
## Error: 'Failed to parse numeric levels: (Intercept)'
parseNumLevels( colnames(model.matrix( ~ f-1 )) )
omp_check 29

omp_check Check OpenMP status

Description
Checks whether OpenMP has been successfully enabled for this installation of the package. (Use the
parallel argument to glmmTMBControl, or set options(glmmTMB.cores=[value]), to specify
that computations should be done in parallel.)

Usage
omp_check()

Value
TRUE or FALSE depending on availability of OpenMP

See Also
benchmark, glmmTMBControl

Owls Begging by Owl Nestlings

Description
Begging by owl nestlings

Usage
data(Owls)

Format
The Owls data set is a data frame with 599 observations on the following variables:

Nest a factor describing individual nest locations

FoodTreatment (factor) food treatment: Deprived or Satiated
SexParent (factor) sex of provisioning parent: Female or Male
ArrivalTime a numeric vector
SiblingNegotiation a numeric vector
BroodSize brood size
NegPerChick number of negotations per chick
30 predict.glmmTMB

Note
Access to data kindly provided by Alain Zuur

Source
Roulin, A. and L. Bersier (2007) Nestling barn owls beg more intensely in the presence of their
mother than in the presence of their father. Animal Behaviour 74 1099–1106. doi: 10.1016/
j.anbehav.2007.01.027; http://www.highstat.com/Books/Book2/ZuurDataMixedModelling.zip

References
Zuur, A. F., E. N. Ieno, N. J. Walker, A. A. Saveliev, and G. M. Smith (2009) Mixed Effects Models
and Extensions in Ecology with R; Springer.

Examples
data(Owls, package = "glmmTMB")
require("lattice")
bwplot(reorder(Nest,NegPerChick) ~ NegPerChick | FoodTreatment:SexParent,
data=Owls)
dotplot(reorder(Nest,NegPerChick) ~ NegPerChick| FoodTreatment:SexParent,
data=Owls)
## Not run:
## Fit negative binomial model with "constant" Zero Inflation :
owls_nb1 <- glmmTMB(SiblingNegotiation ~ FoodTreatment*SexParent +
(1|Nest)+offset(log(BroodSize)),
family = nbinom1(), zi = ~1, data=Owls)
owls_nb1_bs <- update(owls_nb1,
. ~ . - offset(log(BroodSize)) + log(BroodSize))
fixef(owls_nb1_bs)

## End(Not run)

predict.glmmTMB prediction

Description
prediction

Usage
## S3 method for class 'glmmTMB'
predict(
object,
newdata = NULL,
newparams = NULL,
se.fit = FALSE,
predict.glmmTMB 31

cov.fit = FALSE,
re.form = NULL,
allow.new.levels = FALSE,
type = c("link", "response", "conditional", "zprob", "zlink", "disp"),
zitype = NULL,
na.action = na.pass,
fast = NULL,
debug = FALSE,
...
)

Arguments
object a glmmTMB object
newdata new data for prediction
newparams new parameters for prediction
se.fit return the standard errors of the predicted values?
cov.fit return the covariance matrix of the predicted values?
re.form NULL to specify individual-level predictions; ~0 or NA to specify population-level
predictions (i.e., setting all random effects to zero)
allow.new.levels
allow previously unobserved levels in random-effects variables? see details.
type Denoting mu as the mean of the conditional distribution and p as the zero-
inflation probability, the possible choices are:
"link" conditional mean on the scale of the link function, or equivalently the
linear predictor of the conditional model
"response" expected value; this is mu ∗ (1 − p) for zero-inflated models and
mu otherwise
"conditional" mean of the conditional response; mu for all models (i.e., syn-
onymous with "response" in the absence of zero-inflation
"zprob" the probability of a structural zero (returns 0 for non-zero-inflated
models)
"zlink" predicted zero-inflation probability on the scale of the logit link func-
tion (returns -Inf for non-zero-inflated models)
"disp" dispersion parameter however it is defined for that particular family as
described in sigma.glmmTMB
zitype deprecated: formerly used to specify type of zero-inflation probability. Now
synonymous with type
na.action how to handle missing values in newdata (see na.action); the default (na.pass)
is to predict NA
fast predict without expanding memory (default is TRUE if newdata and newparams
are NULL and population-level prediction is not being done)
debug (logical) return the TMBStruc object that will be used internally for debugging?
... unused - for method compatibility
32 print.VarCorr.glmmTMB

Details
• To compute population-level predictions for a given grouping variable (i.e., setting all random
effects for that grouping variable to zero), set the grouping variable values to NA. Finer-scale
control of conditioning (e.g. allowing variation among groups in intercepts but not slopes
when predicting from a random-slopes model) is not currently possible.
• Prediction of new random effect levels is possible as long as the model specification (fixed
effects and parameters) is kept constant. However, to ensure intentional usage, a warning is
triggered if allow.new.levels=FALSE (the default).
• Prediction using "data-dependent bases" (variables whose scaling or transformation depends
on the original data, e.g. poly, ns, or poly) should work properly; however, users are advised
to check results extra-carefully when using such variables. Models with different versions of
the same data-dependent basis type in different components (e.g. formula= y ~ poly(x,3),
dispformula= ~poly(x,2)) will probably not produce correct predictions.

Examples
data(sleepstudy,package="lme4")
g0 <- glmmTMB(Reaction~Days+(Days|Subject),sleepstudy)
predict(g0, sleepstudy)
## Predict new Subject
nd <- sleepstudy[1,]
nd$Subject <- "new"
predict(g0, newdata=nd, allow.new.levels=TRUE)
## population-level prediction
nd_pop <- data.frame(Days=unique(sleepstudy$Days),
Subject=NA)
predict(g0, newdata=nd_pop)

print.VarCorr.glmmTMB Printing The Variance and Correlation Parameters of a glmmTMB

Description
Printing The Variance and Correlation Parameters of a glmmTMB

Usage
## S3 method for class 'VarCorr.glmmTMB'
print(
x,
digits = max(3, getOption("digits") - 2),
comp = "Std.Dev.",
formatter = format,
...
)
profile.glmmTMB 33

Arguments
x a result of VarCorr(<glmmTMB>).
digits number of significant digits to use.
comp a string specifying the component to format and print.
formatter a function.
... optional further arguments, passed the next print method.

profile.glmmTMB Compute likelihood profiles for a fitted model

Description
Compute likelihood profiles for a fitted model

Usage
## S3 method for class 'glmmTMB'
profile(
fitted,
parm = NULL,
level_max = 0.99,
npts = 8,
stepfac = 1/4,
stderr = NULL,
trace = FALSE,
parallel = c("no", "multicore", "snow"),
ncpus = getOption("profile.ncpus", 1L),
cl = NULL,
...
)

## S3 method for class 'profile.glmmTMB'

confint(object, parm = NULL, level = 0.95, ...)

Arguments
fitted a fitted glmmTMB object
parm which parameters to profile, specified
• by index (position)
• by name (matching the row/column names of vcov(object,full=TRUE))
• as "theta_" (random-effects variance-covariance parameters) or "beta_"
(conditional and zero-inflation parameters)
level_max maximum confidence interval target for profile
npts target number of points in (each half of) the profile (approximate)
34 profile.glmmTMB

stepfac initial step factor (fraction of estimated standard deviation)

stderr standard errors to use as a scaling factor when picking step sizes to compute the
profile; by default (if stderr is NULL, or NA for a particular element), uses the
estimated (Wald) standard errors of the parameters
trace print tracing information? If trace=FALSE or 0, no tracing; if trace=1, print
names of parameters currently being profiled; if trace>1, turn on tracing for the
underlying tmbprofile function
parallel method (if any) for parallel computation
ncpus number of CPUs/cores to use for parallel computation
cl cluster to use for parallel computation
... additional arguments passed to tmbprofile
object a fitted profile (profile.glmmTMB) object
level confidence level

Details
Fits natural splines separately to the points from each half of the profile for each specified parameter
(i.e., values above and below the MLE), then finds the inverse functions to estimate the endpoints
of the confidence interval

Value
An object of class profile.glmmTMB, which is also a data frame, with columns .par (parameter
being profiled), .focal (value of focal parameter), value (negative log-likelihood).

Examples
## Not run:
m1 <- glmmTMB(count~ mined + (1|site),
zi=~mined, family=poisson, data=Salamanders)
salamander_prof1 <- profile(m1, parallel="multicore",
ncpus=2, trace=1)
## testing
salamander_prof1 <- profile(m1, trace=1,parm=1)
salamander_prof1M <- profile(m1, trace=1,parm=1, npts = 4)
salamander_prof2 <- profile(m1, parm="theta_")

## End(Not run)
salamander_prof1 <- readRDS(system.file("example_files","salamander_prof1.rds",package="glmmTMB"))
if (require("ggplot2")) {
ggplot(salamander_prof1,aes(.focal,sqrt(value))) +
geom_point() + geom_line()+
facet_wrap(~.par,scale="free_x")+
geom_hline(yintercept=1.96,linetype=2)
}
salamander_prof1 <- readRDS(system.file("example_files","salamander_prof1.rds",package="glmmTMB"))
confint(salamander_prof1)
confint(salamander_prof1,level=0.99)
ranef.glmmTMB 35

ranef.glmmTMB Extract Random Effects

Description
Extract random effects from a fitted glmmTMB model, both for the conditional model and zero infla-
tion.

Usage
## S3 method for class 'glmmTMB'
ranef(object, condVar = TRUE, ...)

## S3 method for class 'ranef.glmmTMB'

as.data.frame(x, ...)

## S3 method for class 'glmmTMB'

coef(object, condVar = FALSE, ...)

Arguments
object a glmmTMB model.
condVar whether to include conditional variances in result.
... some methods for this generic function require additional arguments (they are
unused here and will trigger an error)
x a ranef.glmmTMB object (i.e., the result of running ranef on a fitted glmmTMB
model)

Value
• For ranef, an object of class ranef.glmmTMB with two components:
cond a list of data frames, containing random effects for the conditional model.
zi a list of data frames, containing random effects for the zero inflation.
If condVar=TRUE, the individual list elements within the cond and zi components (corre-
sponding to individual random effects terms) will have associated condVar attributes giv-
ing the conditional variances of the random effects values. These are in the form of three-
dimensional arrays: see ranef.merMod for details. The only difference between the packages
is that the attributes are called ‘postVar’ in lme4, vs. ‘condVar’ in glmmTMB.
• For coef.glmmTMB: a similar list, but containing the overall coefficient value for each level,
i.e., the sum of the fixed effect estimate and the random effect value for that level. Conditional
variances are not yet available as an option for coef.glmmTMB.
• For as.data.frame: a data frame with components
component part of the model to which the random effects apply (conditional or zero-inflation)
grpvar grouping variable
36 reinstalling

term random-effects term (e.g., intercept or slope)

grp group, or level of the grouping variable
condval value of the conditional mode
condsd conditional standard deviation

Note
When a model has no zero inflation, the ranef and coef print methods simplify the structure shown,
by default. To show the full list structure, use print(ranef(model),simplify=FALSE) or the
analogous code for coef. In all cases, the full list structure is used to access the data frames, see
example.

See Also
fixef.glmmTMB.

Examples
if (requireNamespace("lme4")) {
data(sleepstudy, package="lme4")
model <- glmmTMB(Reaction ~ Days + (1|Subject), sleepstudy)
rr <- ranef(model)
print(rr, simplify=FALSE)
## extract Subject conditional modes for conditional model
rr$cond$Subject
as.data.frame(rr)
}

reinstalling Reinstalling binary dependencies

Description
The glmmTMB package depends on several upstream packages, which it uses in a way that depends
heavily on their internal (binary) structure. Sometimes, therefore, installing an update to one of
these packages will require that you re-install a binary-compatible version of glmmTMB, i.e. a version
that has been compiled with the updated version of the upstream package.
• If you have development tools (compilers etc.) installed, you should be able to re-install a
binary-compatible version of the package by running install.packages("glmmTMB", type="source").
If you want to install the development version of glmmTMB instead, you can use remotes::install_github("glmmTMB/
(On Windows, you can install development tools following the instructions at https://
cran.r-project.org/bin/windows/Rtools/; on MacOS, see https://mac.r-project.
org/tools/.)
• If you do not have development tools and can’t/don’t want to install them (and so can’t install
packages with compiled code from source), you have two choices:
– revert the upstream package(s) to their previous binary version. For example, using the
checkpoint package:
residuals.glmmTMB 37

## load (installing if necessary) the checkpoint package

while (!require("checkpoint")) install.packages("checkpoint")
## retrieve build date of installed version of glmmTMB
bd <- as.character(asDateBuilt(
packageDescription("glmmTMB",fields="Built")))
oldrepo <- getOption("repos")
use_mran_snapshot(bd) ## was setSnapshot() pre-checkpoint v1.0.0
install.packages("TMB")
options(repos=oldrepo) ## restore original repo
A similar recipe (substituting Matrix for TMB and TMB for glmmTMB) can be used if you
get warnings about an incompatibility between TMB and Matrix.
– hope that the glmmTMB maintainers have posted a binary version of the package that
works with your system; try installing it via install.packages("glmmTMB",repos="https://glmmTMB.github.
If this doesn’t work, please file an issue (with full details about your operating system and
R version) asking the maintainers to build and post an appropriate binary version of the
package.

residuals.glmmTMB Compute residuals for a glmmTMB object

Description
Compute residuals for a glmmTMB object

Usage
## S3 method for class 'glmmTMB'
residuals(object, type = c("response", "pearson", "working"), ...)

Arguments
object a “glmmTMB” object
type (character) residual type
... ignored, for method compatibility

RHSForm extract right-hand side of a formula

Description
extract right-hand side of a formula

Usage
RHSForm(form, as.form = FALSE)
38 Salamanders

Arguments
form a formula object
as.form (logical) return a formula (TRUE) or as a call/symbolic object (FALSE) ?

Salamanders Repeated counts of salamanders in streams

Description
A data set containing counts of salamanders with site covariates and sampling covariates. Each of
23 sites was sampled 4 times. When using this data set, please cite Price et al. (2016) as well as the
Dryad data package (Price et al. 2015).

Usage
data(Salamanders)

Format
A data frame with 644 observations on the following 10 variables:
site name of a location where repeated samples were taken
mined factor indicating whether the site was affected by mountain top removal coal mining
cover amount of cover objects in the stream (scaled)
sample repeated sample
DOP Days since precipitation (scaled)
Wtemp water temperature (scaled)
DOY day of year (scaled)
spp abbreviated species name, possibly also life stage
count number of salamanders observed

References
Price SJ, Muncy BL, Bonner SJ, Drayer AN, Barton CD (2016) Effects of mountaintop removal
mining and valley filling on the occupancy and abundance of stream salamanders. Journal of Ap-
plied Ecology 53 459–468. doi: 10.1111/13652664.12585
Price SJ, Muncy BL, Bonner SJ, Drayer AN, Barton CD (2015) Data from: Effects of mountaintop
removal mining and valley filling on the occupancy and abundance of stream salamanders. Dryad
Digital Repository. doi: 10.5061/dryad.5m8f6

Examples
require("glmmTMB")
data(Salamanders)

zipm3 = glmmTMB(count~spp * mined + (1|site), zi=~spp * mined, Salamanders, family="poisson")

sigma.glmmTMB 39

sigma.glmmTMB Extract residual standard deviation or dispersion parameter

Description
For Gaussian models, sigma returns the value of the residual standard deviation; for other families,
it returns the dispersion parameter, however it is defined for that particular family. See details for
each family below.

Usage
## S3 method for class 'glmmTMB'
sigma(object, ...)

Arguments
object a “glmmTMB” fitted object
... (ignored; for method compatibility)

Details
The value returned varies by family:
gaussian returns the maximum likelihood estimate of the standard deviation (i.e., smaller than the
results of sigma(lm(...)) by a factor of (n-1)/n)
nbinom1 returns a dispersion parameter (usually denoted α as in Hardin and Hilbe (2007)): such
that the variance equals µ(1 + α).
nbinom2 returns a dispersion parameter (usually denoted θ or k); in contrast to most other families,
larger θ corresponds to a lower variance which is µ(1 + µ/θ).
Gamma Internally, glmmTMB fits Gamma responses by fitting a mean and a shape parameter;
sigma is estimated as (1/sqrt(shape)), which will typically be close (but not identical to) that
estimated by stats:::sigma.default, which uses sqrt(deviance/df.residual)
beta returns the value of φ, where the conditional variance is µ(1 − µ)/(1 + φ) (i.e., increasing φ
decreases the variance.) This parameterization follows Ferrari and Cribari-Neto (2004) (and
the betareg package):
betabinomial This family uses the same parameterization (governing the Beta distribution that
underlies the binomial probabilities) as beta.
genpois returns the index of dispersion φ2 , where the variance is µφ2 (Consul & Famoye 1992)
compois returns the value of 1/ν, When ν = 1, compois is equivalent to the Poisson distribution.
There is no closed form equation for the variance, but it is approximately undersidpersed when
1/ν < 1 and approximately oversidpersed when 1/ν > 1. In this implementation, µ is exactly
the mean (Huang 2017), which differs from the COMPoissonReg package (Sellers & Lotze
2015).
tweedie returns the value of φ, where the variance is φµp . The value of p can be extracted using
family_params
40 simulate.glmmTMB

The most commonly used GLM families (binomial, poisson) have fixed dispersion parameters
which are internally ignored.

References
• Consul PC, and Famoye F (1992). "Generalized Poisson regression model. Communications
in Statistics: Theory and Methods" 21:89–109.
• Ferrari SLP, Cribari-Neto F (2004). "Beta Regression for Modelling Rates and Proportions."
J. Appl. Stat. 31(7), 799-815.
• Hardin JW & Hilbe JM (2007). "Generalized linear models and extensions." Stata press.
• Huang A (2017). "Mean-parametrized Conway–Maxwell–Poisson regression models for dis-
persed counts. " Statistical Modelling 17(6), 1-22.
• Sellers K & Lotze T (2015). "COMPoissonReg: Conway-Maxwell Poisson (COM-Poisson)
Regression". R package version 0.3.5. https://CRAN.R-project.org/package=COMPoissonReg

simulate.glmmTMB Simulate from a glmmTMB fitted model

Description
Simulate from a glmmTMB fitted model

Usage
## S3 method for class 'glmmTMB'
simulate(object, nsim = 1, seed = NULL, ...)

Arguments
object glmmTMB fitted model
nsim number of response lists to simulate. Defaults to 1.
seed random number seed
... extra arguments

Details
Random effects are also simulated from their estimated distribution. Currently, it is not possible to
condition on estimated random effects.

Value
returns a list of vectors. The list has length nsim. Each simulated vector of observations is the same
size as the vector of response variables in the original data set. In the binomial family case each
simulation is a two-column matrix with success/failure.
simulate_new 41

simulate_new Simulate from covariate/metadata in the absence of a real data set

(EXPERIMENTAL)

Description
See vignette("sim", package = "glmmTMB") for more details and examples, and vignette("covstruct",
package = "glmmTMB") for more information on the parameterization of different covariance struc-
tures.

Usage
simulate_new(
object,
nsim = 1,
seed = NULL,
newdata,
newparams,
...,
show_pars = FALSE
)

Arguments
object a one-sided model formula (e.g. ~ a + b + c (peculiar naming is for consistency
with the generic function, which typically takes a fitted model object)
nsim number of simulations
seed random-number seed
newdata a data frame containing all variables listed in the formula, including the response
variable (which needs to fall within the domain of the conditional distribution,
and should probably not be all zeros, but whose value is otherwise irrelevant)
newparams a list of parameters containing sub-vectors (beta, betazi, betad, theta, etc.)
to be used in the model
... other arguments to glmmTMB (e.g. family)
show_pars (logical) print structure of parameter vector and stop without simulating?

Examples
## use Salamanders data for structure/covariates
simulate_new(~ mined + (1|site),
zi = ~ mined,
newdata = Salamanders, show_pars = TRUE)
sim_count <- simulate_new(~ mined + (1|site),
newdata = Salamanders,
zi = ~ mined,
family = nbinom2,
42 up2date

newparams = list(beta = c(2, 1),

betazi = c(-0.5, 0.5), ## logit-linear model for zi
betad = log(2), ## log(NB dispersion)
theta = log(1)) ## log(among-site SD)
)
head(sim_count[[1]])

terms.glmmTMB Methods for extracting developer-level information from glmmTMB

models

Description
Methods for extracting developer-level information from glmmTMB models

Usage
## S3 method for class 'glmmTMB'
terms(x, component = "cond", part = "fixed", ...)

## S3 method for class 'glmmTMB'

model.matrix(object, component = "cond", part = "fixed", ...)

Arguments
x a fitted glmmTMB object
component model component ("cond", "zi", or "disp"; not all models contain all compo-
nents)
part whether to return results for the fixed or random effect part of the model (at
present only part="fixed" is implemented for most methods)
... additional arguments (ignored or passed to model.frame)
object a fitted glmmTMB object

up2date conditionally update glmmTMB object fitted with an old TMB version

Description
conditionally update glmmTMB object fitted with an old TMB version
Load data from system file, updating glmmTMB objects

Usage
up2date(oldfit)

gt_load(fn, verbose = FALSE, mustWork = FALSE)

vcov.glmmTMB 43

Arguments

oldfit a fitted glmmTMB object

fn partial path to system file (e.g. test_data/foo.rda)
verbose print names of updated objects?
mustWork fail if file not found?

vcov.glmmTMB Calculate Variance-Covariance Matrix for a Fitted glmmTMB model

Description

Calculate Variance-Covariance Matrix for a Fitted glmmTMB model

Usage

## S3 method for class 'glmmTMB'

vcov(object, full = FALSE, include_mapped = FALSE, ...)

Arguments

object a “glmmTMB” fit

full return a full variance-covariance matrix?
include_mapped include mapped variables? (these will be given variances and covariances of
NA)
... ignored, for method compatibility

Value

By default (full==FALSE), a list of separate variance-covariance matrices for each model compo-
nent (conditional, zero-inflation, dispersion). If full==TRUE, a single square variance-covariance
matrix for all top-level model parameters (conditional, dispersion, and variance-covariance param-
eters)
44 weights.glmmTMB

weights.glmmTMB Extract weights from a glmmTMB object

Description
Extract weights from a glmmTMB object

Usage
## S3 method for class 'glmmTMB'
weights(object, type = "prior", ...)

Arguments
object a fitted glmmTMB object
type weights type
... additional arguments (not used; for methods compatibility)

Details
At present only explicitly specified prior weights (i.e., weights specified in the weights argument)
can be extracted from a fitted model.
• Unlike other GLM-type models such as glm or glmer, weights() does not currently return
the total number of trials when binomial responses are specified as a two-column matrix.
• Since glmmTMB does not fit models via iteratively weighted least squares, working weights
(see weights.glm) are unavailable.
Index

∗ datasets findReTrmClasses, 11
epil2, 9 fitTMB, 11
Owls, 29 fixef, 12
Salamanders, 38 fixef.glmmTMB, 36
∗ models formatVC, 13
fixef, 12 formula.glmmTMB, 13
function, 13, 33
Anova, 4
Anova.glmmTMB, 3 genpois (nbinom2), 25
as.data.frame.ranef.glmmTMB get_cor, 17
(ranef.glmmTMB), 35 getCapabilities, 14
getME, 15
benchmark, 29 getME (getME.glmmTMB), 15
beta_family (nbinom2), 25 getME.glmmTMB, 15
betabinomial (nbinom2), 25 getReStruc, 15
getXReTrms, 16
checkDepPackageVersion, 5 glm, 44
coef.glmmTMB (ranef.glmmTMB), 35 glmer, 44
compois (nbinom2), 25 glmmTMB, 11, 18, 23
confint.glmmTMB, 5 glmmTMBControl, 19, 22, 29
confint.profile.glmmTMB gt_load (up2date), 42
(profile.glmmTMB), 33
isLMM, 24
df.residual, 20 isLMM.glmmTMB, 24
diagnose, 7
downstream_methods (Anova.glmmTMB), 3 MakeADFun, 20
dtruncated_nbinom1 mkReTrms, 17
(dtruncated_nbinom2), 8 mkTMBStruc, 23
dtruncated_nbinom2, 8 model.frame, 42
dtruncated_poisson model.matrix.default, 19
(dtruncated_nbinom2), 8 model.matrix.glmmTMB (terms.glmmTMB), 42

Effect.glmmTMB (Anova.glmmTMB), 3 na.action, 31

emmeans.glmmTMB (Anova.glmmTMB), 3 nbinom1 (nbinom2), 25
epil2, 9 nbinom2, 25
expandGrpVar, 10 nlminb, 23
ns, 32
family, 19, 20 numFactor, 28
family_glmmTMB, 19
family_glmmTMB (nbinom2), 25 omp_check, 29
family_params, 10 ordbeta (nbinom2), 25

45
46 INDEX

OwlModel (Owls), 29
OwlModel_nb1_bs (Owls), 29
OwlModel_nb1_bs_mcmc (Owls), 29
Owls, 29

parseNumLevels (numFactor), 28
poly, 32
predict.glmmTMB, 30
print, 33
print.VarCorr.glmmTMB, 32
profile.glmmTMB, 6, 33
put_cor (get_cor), 17

ranef (ranef.glmmTMB), 35
ranef.glmmTMB, 35
ranef.merMod, 35
refit, 24
refit.glmmTMB (isLMM.glmmTMB), 24
reinstalling, 36
residuals.glmmTMB, 37
RHSForm, 37

Salamanders, 38
sigma, 19
sigma (sigma.glmmTMB), 39
sigma.glmmTMB, 31, 39
simulate.glmmTMB, 40
simulate_new, 41

t_family (nbinom2), 25
terms.glmmTMB, 42
tmbprofile, 6, 34
tmbroot, 6
truncated_compois (nbinom2), 25
truncated_genpois (nbinom2), 25
truncated_nbinom1 (nbinom2), 25
truncated_nbinom2 (nbinom2), 25
truncated_poisson (nbinom2), 25
tweedie (nbinom2), 25

uniroot, 7
up2date, 42

VarCorr, 13, 33
vcov.glmmTMB, 43

weights.glm, 44
weights.glmmTMB, 44

ziGamma (nbinom2), 25