infer_SLik_joint | R Documentation |
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 other forms of simulation-based inference such as Approximate Bayesian Computation. 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.
infer_SLik_joint(data, stat.obs, logLname = Infusion.getOption("logLname"),
Simulate = attr(data, "Simulate"),
nbCluster= seq_nbCluster(nr=nrow(data)),
using = Infusion.getOption("mixturing"),
verbose = list(most=interactive(),pedantic=FALSE,final=FALSE),
marginalize = TRUE,
constr_crits=NULL,
projectors=NULL,
is_trainset)
data |
A data frame, whose each row contains a vector of parameters and one realization of the summary statistics for these parameters. Typically this holds the projected reference table (but see |
stat.obs |
Named numeric vector of observed values of summary statistics. Typically this holds the projected values (but see |
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 (see |
nbCluster |
controls the |
using |
Either |
marginalize |
Boolean; whether to derive the clustering of fitted parameters by marginalization of the joint clustering; if not, a distinct call to a clustering function is performed. It is strongly advised not to change the default. This argument might be deprecated in future versions. |
constr_crits |
NULL, or quoted expression specifying a constraints on parameters, beyond the ones defined by the ranges over each parameter: see |
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) more information whose importance is not clear to me; (3) a final summary of the results after all elements of |
projectors |
if not NULL, this argument may be passed to |
is_trainset |
Passed to |
An object of class SLik_j
, which is a list including an Rmixmod::mixmodCluster
object (or equivalent objects produced by non-default methods), and additional members not documented here. If projection was used, the list includes a data.frame reftable_raw
of cumulated unprojected simulations.
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)
# simulated data 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", parsTable=parsp)
# Infer surface:
densv <- infer_SLik_joint(simuls,stat.obs=Sobs)
# Usual workflow using inferred surface:
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
# With constraints:
heart <- quote({ x <- 3*(mu-4.25); y <- 3*(s2-0.75); x^2+(y-(x^2)^(1/3))^2-1})
c_densv <- infer_SLik_joint(simuls,stat.obs=Sobs, constr_crits = heart)
c_slik_j <- MSL(c_densv, CIs=FALSE)
refine(c_slik_j, target_LR=10, ntot=3000)
}
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