Simulate Responses From merMod Object
Simulate responses from a "merMod" fitted model object, i.e.,
from the model represented by it.
## 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, ...)
.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 (one-sided) mixed model formula, as described for
|
family |
a GLM family, as in |
weights |
|
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 in
|
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. formula must be a
one-sided formula (i.e. with an empty left-hand side);
in general, if f is a two-sided
formula, f[-2] can be used to drop the LHS.
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 random-effects 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 left-hand 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 matrix-valued left-hand side, in which case they will be
given in matrix form. If a left-hand 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.
## 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$incidence==0),col=2)
##
## simulate from a non-fitted model; in this case we are just
## replicating the previous model, but starting from scratch
params <- list(theta=0.5,beta=c(2,-1,-2,-3))
simdat <- with(cbpp,expand.grid(herd=levels(herd),period=factor(1:4)))
simdat$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),
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