Description Usage Arguments Details Value Examples
Fit a GLMM
1 2 3 |
formula |
a two-sided linear formula object describing both the
fixed-effects and random-effects part of the model, with the response
on the left of a |
subformula |
a subformula, describing how a substituted variable
depends on covariates, or a list of subformulas, if there is more
than one |
data |
an optional data frame, list or environment containing the
variables named in |
family |
a GLM family, see |
method |
the method used to approximate the likelihood. The options
are |
control |
a list of extra parameters controlling the approximation to the likelihood. See 'Details' for more information. |
weights |
an optional vector of ‘prior weights’ to be used
in the fitting process. Should be |
offset |
this can be used to specify an a priori known
component to be included in the linear predictor during
fitting. This should be |
prev_fit |
a |
verbose |
controls how much detail to print out while fitting the model. For verbose = 0, print nothing. For verbose = 1 (the default), print output approximately once a second during model fitting. For verbose = 2, print out the parameter value and log-likelihood at every stage of optimization. |
lme4_control |
the result of a call to |
The control
argument is a list, used to specify further
arguments controlling the approximation to the likelihood:
nAGQ
the number of adaptive Gaussian quadrature points.
Only used if method = "AGQ"
. Defaults to 15.
nSL
the level of sparse grid storage.
Only used if method = "SR"
. Defaults to 3.
nIS
the number of samples to use for importance sampling.
Only used if method = "IS"
. Defaults to 1000.
order
the order of Laplace approxiation.
only used if method = "Laplace"
. Defaults to 1.
check_Laplace
should quality of first-order Laplace
approximation be checked? Only used if method = "Laplace"
and order = 1
. Defaults to TRUE.
divergence_threshold
if check_Laplace = TRUE
,
warn about quality of inference using the first-order Laplace
approximation if measure of divergence from inference with
second-order Laplace approximation exceeds divergence_threshold
.
Defaults to 0.1.
An object of the class glmmFit
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | # Fit a three-level model with the Laplace approximation to the likelihood
(mod_Laplace <- glmm(response ~ covariate + (1 | cluster) + (1 | group),
data = three_level, family = binomial,
method = "Laplace"))
# if we try to fit with adaptive Gaussian quadrature, we get an error
## Not run:
(mod_AGQ <- glmm(response ~ covariate + (1 | cluster) + (1 | group),
data = three_level, family = binomial, method = "AGQ",
control = list(nAGQ = 15)))
## End(Not run)
# We can fit with the Sequential Reduction approximation
## Not run:
(mod_SR <- glmm(response ~ covariate + (1 | cluster) + (1 | group),
data = three_level, family = binomial, method = "SR",
control = list(nSL = 3)))
## End(Not run)
# the estimates of the random effects standard deviations
# are larger than those using the Laplace approximation
|
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