Usage
## S3 method for class 'merMod':
simulate(object, nsim = 1, seed = NULL,
use.u = FALSE, re.form = NA, ReForm, REForm, REform,
newdata=NULL, newparams=NULL, family=NULL,
allow.new.levels = FALSE, na.action = na.pass, \dots)## S3 method for class 'formula':
simulate(object, nsim = 1 , seed = NULL,
family, weights=NULL, offset=NULL, \dots)
.simulateFun(object, nsim = 1, seed = NULL, use.u = FALSE,
re.form = NA, ReForm, REForm, REform,
newdata=NULL, newparams=NULL,
formula=NULL, family=NULL, weights=NULL, offset=NULL,
allow.new.levels = FALSE, na.action = na.pass, ...)
Arguments
object
(for simulate.merMod
) a fitted model object or
(for simulate.formula
) a (one-sided) mixed model formula, as
described for lmer
. nsim
positive integer scalar - the number of responses to simulate.
seed
an optional seed to be used in set.seed
immediately before the simulation so as to generate a reproducible sample. use.u
(logical) if TRUE
, generate a simulation
conditional on the current random-effects estimates; if FALSE
generate new Normally distributed random-effects values. (Redundant
with re.form
, which is preferred:
re.form
formula for random effects to condition on. If
NULL
, condition on all random effects; if NA
or ~0
,
condition on no random effects. See Details.
ReForm, REForm, REform
deprecated: re.form
is
now the preferred argument name.
newdata
data frame for which to evaluate predictions.
newparams
new parameters to use in evaluating predictions,
specified as in the start
parameter for lmer
or
glmer
-- a list with components theta<
formula
a (one-sided) mixed model formula, as described for
lmer
. family
a GLM family, as in glmer
. allow.new.levels
(logical) if FALSE (default), then any new
levels (or NA
values) detected in newdata
will trigger an
error; if TRUE, then the prediction will use the unconditional
(population-level) values for data with previously un
na.action
what to do with NA
values in new data: see
na.fail
...
optional additional arguments: none are used at present.