MCMC sampling from a given dataset
Usage
mcmcSampling(
dataset = NULL,
dimensions = list(""),
densityFunction = alwaysOne,
proposalFunction = addHighDimGaussian(dim = length(dimensions)),
n.sample.points = 0,
burnIn = 1000,
verbose = TRUE,
covariance.correction = 1
)Arguments
- dataset
sf dataframe from which the points are sampled
- dimensions
string vector containing the dimensions that should be included in the random walk
- densityFunction
Function that can take a point given as a sf dataframe as a input and returns the target density at that location.
- proposalFunction
Function that can take a point given as a sf dataframe and a vector of strings specifying the row names that should be changed as a input and returns a new proposed point
- n.sample.points
Number of points to be sampled
- burnIn
Integer, sets the number of samples per adaptive burn in step. If set to 0, burn in is skipped
- verbose
Boolean to toggle progress updates
- covariance.correction
Integer, initial value of the covariance correction.