NEWS | R Documentation |
updated for R-devel changes (R 4.0.0 will set stringsAsFactors=FALSE by default)
The 1.0.0 release does not introduce any major changes or incompatibilities, but signifies that glmmTMB is considered stable and reliable for general use.
new map
argument to glmmTMB
allows for some
parameter values to be fixed
(see ?TMB::MakeADFun
for details)
new optimizer
and
optArgs
arguments to glmmTMBControl
allow use of
optimizers other than nlminb
predict
can make population-level predictions
(i.e., setting all random effects to zero).
See ?predict.glmmTMB
for details.
beta_family
now allows zero-inflation;
new ziGamma
family (minor modification of
stats::Gamma
) allows zero-inflation
(i.e., Gamma-hurdle models)
vcov(., full=TRUE)
(and hence profiling) now work for models with dispformula=~0
Documentation fix: when family=genpois
, the
index of dispersion is known as phi^2.
Anova
now respects the component
argument (GH
#494, from @eds-slim)
predict
now works when contrasts are set on factors
in original data (GH #439, from @cvoeten)
bootMer
now works with models with Bernoulli
responses (even though simulate()
returns a two-column
matrix in this case) (GH #529, @frousseu)
better support for emmeans
applied to zero-inflation
or dispersion models (correct link functions) (Russ Lenth)
sigma(.)
now returns NA
for models with
non-trivial dispersion models (i.e. models with more than one
dispersion parameter) (raised by GH #533, from @marek-tph)
VarCorr
no longer prints residual variances for
models with dispformula=~0
the model.matrix()
and terms()
methods
for glmmTMB
objects have been slightly modified
ranef
now returns information about conditional variances (as
attributes of the individual random effects terms) by default;
this information can easily be retrieved by
as.data.frame(ranef(.))
.
coef
method now available: as in lme4
, returns
sum of fixed + random effects for each random-effects
level. (Conditional variances for coef
not yet available.)
simulate works for models with genpois family
parametric bootstrapping should work, using
bootMer
from the lme4
package as a front end.
models with multiple types of RE (e.g. ar1 and us) may have failed previously (GH #329)
predict
was not handling data-dependent predictors (e.g. poly
, spline
, scale
) correctly
profile
now works for models without random effects
The value returned from simulate
for binomial models
is now a non-standard data frame where each element contains a
two-column matrix (as in the base-R simulate
method for
binomial GLMS).
REML is now an option (GH #352). It is typically only for Gaussian response variables, but can also be useful for some non-Gaussian response variables if used with caution (i.e. simulate a test case first).
Because family functions are now available for all
families that have been implemented in the underlying TMB
code, specifying the family
argument as a raw list (rather than as a family
function, the name of a family function, or the output of such a
function) is now deprecated.
likelihood profiles (via profile
) and likelihood
profile confidence intervals (via confint(profile(.))
)
can now be computed;
confint(fitted,method="profile")
and
confint(fitted,method="uniroot")
(find CIs by using
a root-finding algorithm on the likelihood profile)
offsets are now allowed in the zero-inflation and dispersion
formulas as well as in the main (conditional-mean) formula
(if offset
is specified as a separate argument, it applies
only to the conditional mean)
zero-truncated generalized Poisson family=truncated_genpois
zero-truncated Conway-Maxwell-Poisson
family=truncated_compois
predict
now allows type
("link", "response",
"conditional", "zprob", "zlink")
built-in betar()
family for Beta regression fixed
(and name changed to beta_family()
) (GH #278)
fixed segfault in predict method when response is specified as two columns (GH #289)
fixed summary-printing bug when some random effects have covariance terms and others don't (GH #291)
fix bugs in binomial residuals and prediction (GH #307)
in predict.glmmTMB
,
the zitype
argument has been rolled into the new
type
argument: default prediction type is now
"link" instead of "response", in order to match glm()
default