Description Usage Arguments Details See Also Examples
Simulate responses from a "merMod"
fitted model object, i.e.,
from the model represented by it.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  ## 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, ...)
## S3 method for class 'formula'
simulate(object, nsim = 1 , seed = NULL,
family, weights=NULL, offset=NULL, ...)
.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,
cond.sim = TRUE, ...)

object 
(for 
nsim 
positive integer scalar  the number of responses to simulate. 
seed 
an optional seed to be used in 
use.u 
(logical) if 
re.form 
formula for random effects to condition on. If

ReForm, REForm, REform 
deprecated: 
newdata 
data frame for which to evaluate predictions. 
newparams 
new parameters to use in evaluating predictions,
specified as in the 
formula 
a (onesided) mixed model formula, as described for

family 
a GLM family, as in 
weights 
prior weights, as in 
offset 
offset, as in 
allow.new.levels 
(logical) if FALSE (default), then any new
levels (or 
na.action 
what to do with 
cond.sim 
(experimental) simulate the conditional
distribution? if 
... 
optional additional arguments: none are used at present. 
ordinarily simulate
is used to generate new
values from an existing, fitted model (merMod
object):
however, if formula
, newdata
, and newparams
are
specified, simulate
generates the appropriate model
structure to simulate from.
The re.form
argument allows the user to specify how the
random effects are incorporated in the simulation. All of the
random effects terms included in re.form
will be
conditioned on  that is, the conditional modes of those
random effects will be included in the deterministic part of the
simulation. (If new levels are used (and allow.new.levels
is TRUE
), the conditional modes for these levels will be
set to the population mode, i.e. values of zero will be used for
the random effects.) Conversely, the random effect terms that are
not included in re.form
will be simulated
from  that is, new values will be chosen for each group based on
the estimated randomeffects variances.
The default behaviour (using re.form=NA
) is to condition on
none of the random effects, simulating new values for all of the
random effects.
For Gaussian fits, sigma
specifies the residual
standard deviation; for Gamma fits, it specifies the shape
parameter (the rate parameter for each observation i
is calculated as shape/mean(i)). For negative binomial fits,
the overdispersion parameter is specified via the family,
e.g. simulate(..., family=negative.binomial(theta=1.5))
.
For binomial models, simulate.formula
looks for the
binomial size first in the weights
argument (if it's supplied),
second from the lefthand side of the formula (if the formula has been
specified in success/failure form), and defaults to 1 if neither of
those have been supplied.
Simulated responses will be given as proportions, unless the supplied
formula has a matrixvalued lefthand side, in which case they will be
given in matrix form. If a lefthand side is given, variables in that
expression must be available in newdata
.
For negative binomial models, use the negative.binomial
family (from the MASS
package)
and specify the overdispersion parameter via the
theta
(sic) parameter of the family function, e.g.
simulate(...,family=negative.binomial(theta=1))
to simulate
from a geometric distribution (negative binomial with
overdispersion parameter 1).
bootMer
for “simulestimate”, i.e., where each
simulation is followed by refitting the model.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  ## test whether fitted models are consistent with the
## observed number of zeros in CBPP data set:
gm1 < glmer(cbind(incidence, size  incidence) ~ period + (1  herd),
data = cbpp, family = binomial)
gg < simulate(gm1,1000)
zeros < sapply(gg,function(x) sum(x[,"incidence"]==0))
plot(table(zeros))
abline(v=sum(cbpp$incidence==0),col=2)
##
## simulate from a nonfitted model; in this case we are just
## replicating the previous model, but starting from scratch
params < list(theta=0.5,beta=c(2,1,2,3))
simdat < with(cbpp,expand.grid(herd=levels(herd),period=factor(1:4)))
simdat$size < 15
simdat$incidence < sample(0:1,size=nrow(simdat),replace=TRUE)
form < formula(gm1)[2] ## RHS of equation only
simulate(form,newdata=simdat,family=binomial,
newparams=params)
## simulate from negative binomial distribution instead
simulate(form,newdata=simdat,family=negative.binomial(theta=2.5),
newparams=params)

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