Infusion (version 1.1.0)

Infusion: Inference using simulation

Description

Implements a collection of methods to perform inferences based on simulation of realizations of the model considered. In particular it implements “summary likelihood”, an approach that effectively evaluates and uses the likelihood of simulated summary statistics.

Details

The methods implemented in Infusion by default assume that the summary statistics have densities. Special values of some statistic, having discrete probability mass, can be handled using the boundaries attribute of the observed summary statistics (see handling_NAs for a further use of this attribute).

Examples

# NOT RUN {
## The following example illustrates the workflow.
## However, most steps run longer than requested by the CRAN checks,
## So by default they will not run.
##
## (1) The user must provide the function for simulation of summary statistics
myrnorm <- function(mu,s2,sample.size) {
 s <- rnorm(n=sample.size,mean=mu,sd=sqrt(s2))
 return(c(mean=mean(s),var=var(s)))
} # simulate means and variances of normal samples of size 'sample.size'
#
## pseudo-sample:  
set.seed(123)
Sobs <- myrnorm(mu=4,s2=1,sample.size=40) ## stands for the actual data to be analyzed
#
## (2) Generate, and simulate distributions for, 
##        an irregular grid of parameter values, with some replicates
if (Infusion.getOption("example_maxtime")>45) {
  parsp <- init_grid(lower=c(mu=2.8,s2=0.2,sample.size=40),
                     upper=c(mu=5.2,s2=3,sample.size=40))
  simuls <- add_simulation(NULL,Simulate="myrnorm",par.grid=parsp)
  
  ## (3) infer logL(pars,stat.obs) for each simulated 'pars'
  # Relatively slow, hence saved as data 'densv'
  densv <- infer_logLs(simuls,stat.obs=Sobs)
} else {
  data(densv)
  .Random.seed <- saved_seed
}
#
## (4) infer a log-likelihood surface and its maximum;
##       plot and extract various information. 
if (Infusion.getOption("example_maxtime")>17) {
 slik <- infer_surface(densv)
 slik <- MSL(slik) ## find the maximum of the log-likelihood surface
 plot(slik)
 profile(slik,c(mu=4)) ## profile summary logL for given parameter value
 confint(slik,"mu") ## compute confidence interval for given parameter
 plot1Dprof(slik,pars="s2",gridSteps=40) ## 1D profile
}
#
## (5) ## refine iteratively
if (Infusion.getOption("example_maxtime")>68) {
 slik <- refine(slik) 
}
# }