gibbs {RGeostats}  R Documentation 
Perform simulations using gibbs sampler
gibbs(db = NA, model = model.input(), seed = 232131, nbsimu = 1, rank.grf = 1, nboot = 10, niter = 100, flag.norm=FALSE, flag.propagation=FALSE, nostat=NA, percent=0.1, toleps = 1, radix = "Gibbs", modify.target = db.locmod())
db 
The 
model 
The 
seed 
Seed used for the random number generator. When 0, the seed is not initialized. 
nbsimu 
Number of simulations 
rank.grf 
Rank of the gaussian random function to be simulated 
nboot 
Number of iterations performed for bootstrap 
niter 
Maximum number of iterations for calculating the conditional expectation 
flag.norm 
When TRUE, the model is normalized (as it should correspond to a normalized gaussian). 
flag.propagation 
When TRUE, the Gibbs sampler using the propagation algorithm is used. This method is incompatible with the presence of bounds. 
nostat 
List of nonstationary parameters. For details see

percent 
Amount of nugget effect added to the model if this initial model only contains Gaussian structures. If set to zero, the initial model is kept unchanged. This amount is defined as a percentage of the global variance. 
toleps 
Relative difference between the previous and the new value at a sample location below which the sample is considered as immobile 
radix 
Radix of the name given to the variables storing the results in the target Db. 
modify.target 
Decides whether or not the newly created variables will have their
locator defined or not. For more information, see 
Some parameters are passed by the 'keypair' mechanism (in the case of Gibbs Propagation algorithm):
gibbsPropaR: value of the coefficient 'r' used to generate the updated value. Default 0.
gibbsEps: the threshold (to consider that samples are not correlated) is calculated as the C(0) * gibbsEps. Default 0.
The data Db where the simulated variable has been added