Description Usage Arguments Value Examples
This infers the likelihood surface from a simulation table where each simulated data set is drawn for a distinct (vectorvalued) 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.
1 2 3 4 5  infer_SLik_joint(data, stat.obs, logLname = Infusion.getOption("logLname"),
Simulate = attr(data, "Simulate"),
nbCluster= Infusion.getOption("nbCluster"),
using = Infusion.getOption("using"),
verbose = list(most = interactive(), final = FALSE))

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 

using 
Either 
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 
An object of class SLik_j
, which is a list including two Rmixmod::mixmodCluster
objects, and additional members not documented here.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26  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)
# pseudosample 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 loglikelihood 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
}

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