example_raw | R Documentation |
Example of the workflow with add_simulation
), implementing the method described in the original publication
(Rousset et al. 2017 <doi:10.1111/1755-0998.12627>).
## The following example illustrates the workflow. ## However, most steps run longer than accepted 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")>40) { 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")>11) { 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")>39) { slik <- refine(slik) }
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