glmmTMB (version 0.2.0)

glmmTMB: Fit models with TMB

Description

Fit models with TMB

Usage

glmmTMB(formula, data = NULL, family = gaussian(), ziformula = ~0,
  dispformula = ~1, weights = NULL, offset = NULL, se = TRUE,
  verbose = FALSE, doFit = TRUE, control = glmmTMBControl())

Arguments

formula

combined fixed and random effects formula, following lme4 syntax

data

data frame

family

family (variance/link function) information; see family for generic family details or family_glmmTMB for details of glmmTMB specific families. As in glm, family can be specified as (1) a character string referencing an existing family-construction function (e.g. ‘"binomial"’); (2) a symbol referencing such a function (‘binomial’); or (3) the output of such a function (‘binomial()’). In addition, for families such as betabinomial that are special to glmmTMB, family can be specified as (4) a list comprising the name of the distribution and the link function (‘list(family="binomial", link="logit")’). However, the first 3 options are preferable.

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 ~. 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. 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 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

weights, as in glm. Not automatically scaled to have sum 1.

offset

offset

se

whether to return standard errors

verbose

logical indicating if some 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.

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.

• in all cases glmmTMB returns maximum likelihood estimates - random effects variance-covariance matrices are not REML (so use REML=FALSE when comparing with lme4::lmer), and residual standard deviations (sigma) are not bias-corrected. 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)

Examples

# 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
# }