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Fit models with TMB
glmmTMB(formula, data = NULL, family = gaussian(), ziformula = ~0,
dispformula = ~1, weights = NULL, offset = NULL, na.action = na.fail,
se = TRUE, verbose = FALSE, doFit = TRUE, control = glmmTMBControl(),
REML = FALSE)combined fixed and random effects formula, following lme4 syntax
data frame
a family function, a character string naming a family function, or the result of a call to a family function family (variance/link function) information; see family for generic discussion of families or family_glmmTMB for details of glmmTMB-specific families.
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 ~. will set the right-hand side of the zero-inflation
formula identical to the right-hand side of the main (conditional effects)
formula; terms can also be added or subtracted. Offset terms
will automatically be dropped from the conditional effects formula when using ~.
The zero-inflation model uses a logit link.
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 dispersion parameter for each family, see (sigma).
The dispersion model uses a log link.
In Gaussian mixed models, dispformula=~0 fixes the parameter to be 0, forcing variance into the random effects.
weights, as in glm. Not automatically scaled to have sum 1.
offset for conditional model (only):
whether to return standard errors
logical indicating if some progress indication should be printed to the console.
whether to fit the full model, or (if FALSE) return the preprocessed data and parameter objects, without fitting the model
control parameters; see glmmTMBControl.
Logical; Use REML estimation rather than maximum likelihood.
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, one would need to multiply by sqrt(nobs(fit)/(1+df.residual(fit))) when comparing with lm ...
by default, vector-valued random effects are fitted with
unstructured (general positive 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 (* Mat<U+00E9>rn process correlation)
toep (* Toeplitz)
(note structures marked with * are experimental/untested)
For backward compatibility, the family argument can also be specified as a list comprising the name of the distribution and the link function (e.g. ‘list(family="binomial", link="logit")’). However, this alternatives is now deprecated (it produces a warning and will be removed at some point in the future). Furthermore, certain capabilities such as Pearson residuals or predictions on the data scale will only be possible if components such as variance and linkfun are present (see family).
Millar, Russell B. Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB. John Wiley & Sons, 2011.
# NOT RUN {
(m1 <- glmmTMB(count~ mined + (1|site),
zi=~mined,
family=poisson, data=Salamanders))
summary(m1)
# }
# NOT RUN {
## Zero-inflated negative binomial model
(m2 <- glmmTMB(count~spp + mined + (1|site),
zi=~spp + mined,
family=nbinom2, Salamanders))
## Hurdle Poisson model
(m3 <- glmmTMB(count~spp + mined + (1|site),
zi=~spp + mined,
family=truncated_poisson, Salamanders))
## Binomial model
data(cbpp, package="lme4")
(tmbm1 <- glmmTMB(cbind(incidence, size-incidence) ~ period + (1 | herd),
data=cbpp, family=binomial))
## Dispersion model
sim1=function(nfac=40, nt=100, facsd=.1, tsd=.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$REt
return(dat)
}
set.seed(101)
d1 = sim1(mu=100, residsd =10)
d2 = sim1(mu=200, residsd =5)
d1$sd="ten"
d2$sd="five"
dat = rbind(d1, d2)
m0 = glmmTMB(x~sd+(1|t), dispformula=~sd, dat)
fixef(m0)$disp
c(log(5^2), log(10^2)-log(5^2)) #expected dispersion model coefficients
# }
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