lme4 (version 1.1-8)

simulate.merMod: Simulate Responses From merMod Object

Description

Simulate responses from a "merMod" fitted model object, i.e., from the model represented by it.

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.
weights
prior weights, as in lmer or glmer.
offset
offset, 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.

Details

  • ordinarilysimulateis used to generate new values from an existing, fitted model (merModobject): however, ifformula,newdata, andnewparamsare specified,simulategenerates 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 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.

See Also

bootMer for simulestimate, i.e., where each simulation is followed by refitting the model.

Examples

Run this code
## 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$incidence==0),col=2)
##
## simulate from a non-fitted model; in this case we are just
## replicating the previous model, but
params <- list(theta=0.5,beta=c(2,-1,-2,-3))
simdat <- with(cbpp,expand.grid(herd=levels(herd),period=factor(1:4)))
simdat$size <- 15
simdat$incidence <- sample(0:1,size=nrow(simdat),replace=TRUE)
form <- formula(gm1)[-2]
simulate(form,newdata=simdat,family=binomial,
    newparams=params)

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