infer_jointDens: Infer as (summary) likelihood surface from a simulation table

Description Usage Arguments Value Examples

Description

This infers the likelihood surface from a simulation table where each simulated data set is drawn for a distinct (vector-valued) parameter, as is usual for reference tables in ABC. A parameter density is inferred, as well as a joint density of parameters and summary statistics, and the likelihood surface is inferred from these two densities. This is not yet extensively tested, nor the code has been optimized.

Usage

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infer_SLik_joint(data, stat.obs, logLname = Infusion.getOption("logLname"), 
                Simulate = attr(data, "Simulate"), 
                nbCluster= Infusion.getOption("nbCluster"),
                verbose = list(most = interactive(), final = FALSE))

Arguments

data

A data frame, whose rows contain a vector of parameters and one realization of the summary statistics for these parameters.

stat.obs

Named numeric vector of observed values of summary statistics.

logLname

The name to be given to the log Likelihood in the return object, or the root of the latter name in case of conflict with other names in this object.

Simulate

Either NULL or the name of the simulation function if it can be called from the R session.

nbCluster

nbCluster argument of mixmodCluster

verbose

A list as shown by the default, or simply a vector of booleans, indicating respectively whether to display (1) some information about progress; (2) a final summary of the results after all elements of simuls have been processed.

Value

An object of class SLik_j, which is a list including two mixmodCluster objects, and additional members not documented here.

Examples

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if (Infusion.getOption("example_maxtime")>50) {
  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'
  set.seed(123)
  # pseudo-sample with stands for the actual data to be analyzed:  
  ssize <- 40
  Sobs <- myrnorm(mu=4,s2=1,sample.size=ssize) 
  # Uniform sampling in parameter space:
  npoints <- 600
  parsp <- data.frame(mu=runif(npoints,min=2.8,max=5.2),
                      s2=runif(npoints,min=0.4,max=2.4),sample.size=ssize)
  # Build simulation table:
  simuls <- add_reftable(Simulate="myrnorm",par.grid=parsp)
  # Infer surface:
  densv <- infer_SLik_joint(simuls,stat.obs=Sobs)
  # Usual workflow using inferred suface:
  slik_j <- MSL(densv) ## find the maximum of the log-likelihood surface
  slik_j <- refine(slik_j,maxit=5)
  plot(slik_j)
  # etc:
  profile(slik_j,c(mu=4)) ## profile summary logL for given parameter value
  confint(slik_j,"mu") ## compute 1D confidence interval for given parameter
  plot1Dprof(slik_j,pars="s2",gridSteps=40) ## 1D profile
}

Infusion documentation built on Nov. 17, 2017, 7:14 a.m.