findMCMC_strong_corrs {runMCMCbtadjust} | R Documentation |
findMCMC_strong_corrs
Description
finds the couples of parameters of a MCMC.list object that have at least a minCorr level of (absolute) correlation
Usage
findMCMC_strong_corrs(
mcmcList,
corrMethod = "pearson",
minCorr = 0.3,
namesToRemove = NULL
)
Arguments
mcmcList |
R object of type mcmc.list that contains the MCMC output |
corrMethod |
character: designates the kind of correlation calculated among "pearson" (the default, for linear relationships), "spearman" (for monotone relationships) or "hoeffd" (for general associations - i.e. dependencies - between parameters) |
minCorr |
double, between 0 and 1: minimum level of (absolute) correlation to report. |
namesToRemove |
R object (can be a vector, matrix, array, list...) all components of which must be of character type: will remove parameters whose names partially match one of tghese components. |
Details
In case corrMethod equals "hoeffd", the hoeffd function in the Hmisc package will be used. This can be very slow. Therefore a warning message is printed in this case.
Examples
## Not run:
#generating data
set.seed(1)
y1000<-rnorm(n=1000,mean=600,sd=30)
ModelData <-list(mass = y1000,nobs = length(y1000))
#writing the Jags code as a character chain in R
modeltotransfer<-"model {
# Priors
population.mean ~ dunif(0,5000)
population.sd ~ dunif(0,100)
# Precision = 1/variance: Normal distribution parameterized by precision in Jags
population.variance <- population.sd * population.sd
precision <- 1 / population.variance
# Likelihood
for(i in 1:nobs){
mass[i] ~ dnorm(population.mean, precision)
}
}"
#specifying the initial values
ModelInits <- function()
{list (population.mean = rnorm(1,600,90), population.sd = runif(1, 1, 30))}
params <- c("population.mean", "population.sd", "population.variance")
K<-3
set.seed(1)
Inits<-lapply(1:K,function(x){ModelInits()})
# running runMCMC_btadjust with MCMC_language="Jags":
set.seed(1)
out.mcmc.Coda<-runMCMC_btadjust(MCMC_language="Jags", code=modeltotransfer,
data=ModelData,
Nchains=K, params=params, inits=Inits,
niter.min=1000, niter.max=300000,
nburnin.min=100, nburnin.max=200000,
thin.min=1, thin.max=1000,
neff.min=1000, conv.max=1.05,
control=list(print.diagnostics=TRUE, neff.method="Coda"))
findMCMC_strong_corrs(out.mcmc.Coda)
## End(Not run)