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## Generate a test likelihood function.
ll <- generateTestDensityMultiNormal(sigma = "no correlation")
## Create a BayesianSetup object from the likelihood
## is the recommended way of using the runMCMC() function.
bayesianSetup <- createBayesianSetup(likelihood = ll, lower = rep(-10, 3), upper = rep(10, 3))
## Finally we can run the sampler and have a look
settings = list(iterations = 1000)
out <- runMCMC(bayesianSetup = bayesianSetup, sampler = "DEzs", settings = settings)
## Correlation density plots:
correlationPlot(out)
## additional parameters can be passed to getSample (see ?getSample for further information)
## e.g. to select which parameters to show or thinning (faster plot)
correlationPlot(out, scaleCorText = FALSE, thin = 100, start = 200, whichParameters = c(1,2))
## text to display correlation will be not scaled to the strength of the correlation
correlationPlot(out, scaleCorText = FALSE)
## We can also switch the method for calculating correllations
correlationPlot(out, scaleCorText = FALSE, method = "spearman")
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