ranef.glmmTMB | R Documentation |
Extract random effects from a fitted glmmTMB
model, both
for the conditional model and zero inflation.
## 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, ...)
object |
a |
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 |
For ranef
, an object of class ranef.glmmTMB
with two components:
a list of data frames, containing random effects for the conditional model.
a list of data frames, containing random effects for the zero inflation.
a list of data frames, containing random effects for the dispersion model.
If condVar=TRUE
, the individual list elements within the
cond
, zi
, and disp
components (corresponding to individual
random effects terms) will have associated condVar
attributes
giving 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
part of the model to which the random effects apply (conditional or zero-inflation)
grouping variable
random-effects term (e.g., intercept or slope)
group, or level of the grouping variable
value of the conditional mode
conditional standard deviation
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.
fixef.glmmTMB
.
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)
}
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