spaMM (version 2.6.1)

simulate.HLfit: Simulate realizations of a fitted model.

Description

From an HLfit object, simulate.HLfit function generates new samples given the estimated fixed effects and dispersion parameters. Simulation may be unconditional (the default, useful in many applications of parametric bootstrap), or conditional on the predicted values of random effects, or may draw from the conditional distribution of random effects given the observed response. Simulations may be run for the original values of fixed-effect predictor variables and of random effect levels (spatial locations for spatial random effects), or for new values of these.

Usage

# S3 method for HLfit
simulate(object, nsim = 1, seed = NULL, newdata = NULL, 
                         type = "marginal", re.form, conditional = NULL, 
                         verbose = TRUE, sizes = NULL, resp_testfn = NULL, 
                         phi_type = "predict", prior.weights = object$prior.weights, 
                         ...)
# S3 method for HLfitlist
simulate(object, nsim = 1, seed = NULL, 
                             newdata = object[[1]]$data, sizes = NULL, ...)

Arguments

object

The return object of HLfit or similar function.

nsim

number of response vectors to simulate. Defaults to '1'.

seed

A seed for set.seed. If such a value is provided, the initial state of the random number generator at a global level is restored on exit from simulate.

newdata

A data frame closely matching the original data, except that response values are not needed. May provide new values of fixed predictor variables, new spatial locations, or new individuals within a block.

re.form

formula for random effects to condition on. If non-missing, this argument overrides the type argument. As it is missing by default, the joint default re.form and type effect is the latter's default, i.e., unconditional simulation. re.form=NULL conditions on all random effects (as type="residual"), and re.form=NA conditions on none of the random effects (as type="marginal" or re.form=~0).

type

character string specifying the type of simulation for mixed models. "marginal" is for simulation from the marginal distribution of the random effect; "residual" acounts only for the residual variation of the fitted model; and "(ranef|response)" accounts both for residual variation and for the conditional distribution of the random effects given the response and the point estimates of model parameters. This distribution is known exactly in LMMs, and otherwise approximated as a Gaussian distribution with mean vector and covariance matrix given as per the Laplace approximation.

conditional

Obsolete and will be deprecated. Boolean; TRUE and FALSE are equivalent to type="residual" and type="marginal", respectively.

verbose

Boolean; whether to print some information or not.

sizes

A vector of sample sizes to simulate in the case of a binomial fit. Defaults to the sizes in the original data.

resp_testfn

NULL, or a function that tests a condition which simulated samples should satisfy. This function takes a response vector as argument and return a boolean (TRUE indicating that the sampel satisfies the condition).

phi_type

Character string, either "predict" or one of the values possible for type. This controls the residual variance parameter \(\phi\). The default is to use predicted \(\phi\) values from the fit, which are the fitted \(\phi\) values except when a structured-dispersion model is involved together with non-NULL newdata. However, when a structured-dispersion model is involved, it is also possible to simulate new \(\phi\) values, and for a mixed-effects structured-dispersion model, the same types of simulation controlled by type for the main response can be performed as controlled by phi_type. For a fixed-effects structured-dispersion model, these types cannot be distinguished, and any phi_type distinct from "predict" will imply simulation under the fixed-effect model (see Examples).

prior.weights

Prior weights that may be substituted to those of the original fit, with the same effect on the residual variance.

…

further arguments passed to or from other methods.

Value

For the HLfitlist method (i.e., the result of a multinomial fit), a list of simulated responses. Otherwise, a vector (if nsim=1) or a matrix with nsim columns, each containing a simulated response.

Examples

# NOT RUN {
data("Loaloa")
HLC <- HLCor(cbind(npos,ntot-npos)~Matern(1|longitude+latitude),
           data=Loaloa,family=binomial(),
           ranPars=list(lambda=1,nu=0.5,rho=1/0.7)) 
simulate(HLC,nsim=2)

## Structured dispersion model 
data("wafers")
hl <- HLfit(y ~X1+X2+X1*X3+X2*X3+I(X2^2)+(1|batch),family=Gamma(log),
            resid.model = ~ X3+I(X3^2) ,data=wafers)
simulate(hl,type="marginal",phi_type="simulate",nsim=2)
# }