Simulate Responses From merMod Object
Description
Simulate responses from a "merMod"
fitted model object, i.e.,
from the model represented by it.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ## 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, ...)

Arguments
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 
... 
optional additional arguments: none are used at present. 
Details
ordinarily
simulate
is used to generate new values from an existing, fitted model (merMod
object): however, ifformula
,newdata
, andnewparams
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 inre.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 (andallow.new.levels
isTRUE
), 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 inre.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))
.
See Also
bootMer
for “simulestimate”, i.e., where each
simulation is followed by refitting the model.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  ## 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$incidence==0),col=2)
##
## simulate from a nonfitted model; in this case we are just
## replicating the previous model, but
params < list(theta=0.5,beta=c(2,1,2,3))
simdat < with(cbpp,expand.grid(herd=levels(herd),period=factor(1:4)))
simdat$size < 15
simdat$incidence < sample(0:1,size=nrow(simdat),replace=TRUE)
form < formula(gm1)[2]
simulate(form,newdata=simdat,family=binomial,
newparams=params)
