correlationPlot {BayesianTools} | R Documentation |
Flexible function to create correlation density plots
correlationPlot(mat, density = "smooth", thin = "auto", method = "pearson", whichParameters = NULL, scaleCorText = T, ...)
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 |
Florian Hartig
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.
marginalPlot
plotTimeSeries
tracePlot
## 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")