correlationPlot {BayesianTools}

Flexible function to create correlation density plots

Description

Flexible function to create correlation density plots

Usage

correlationPlot(
  mat,
  density = "smooth",
  thin = "auto",
  method = "pearson",
  whichParameters = NULL,
  scaleCorText = T,
  ...
)

Arguments

mat

object of class "bayesianOutput" or a matrix or data frame of variables

density

type of plot to do. Either "smooth" (default), "corellipseCor", or "ellipse"

thin

thinning of the matrix to make things faster. Default is to thin to 5000

method

method for calculating correlations. Possible choices are "pearson" (default), "kendall" and "spearman"

whichParameters

indices of parameters that should be plotted

scaleCorText

should the text to display correlation be scaled to the strength of the correlation

...

additional parameters to pass on to the getSample, for example parametersOnly =F, or start = 1000

Author(s)

Florian Hartig

References

The code for the correlation density plot originates from Hartig, F.; Dislich, C.; Wiegand, T. & Huth, A. (2014) Technical Note: Approximate Bayesian parameterization of a process-based tropical forest model. Biogeosciences, 11, 1261-1272.

See Also

marginalPlot
plotTimeSeries
tracePlot

Examples

## 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")