runMCMC_: Internal Function for Robust MCMC Algorithms
In geoCount: Analysis and Modeling for Geostatistical Count Data

Description Usage Arguments Details Author(s) See Also

This function is an internal function mainly used by runMCMC to call C++ codes that perform robust MCMC algrothms.

runMCMC_(Y_, L_, T_, D_, run_, nmLan_, fam_, famY_, 
         famSig_, par1_, par2_, ifkappa_, 
         scale_, mscale_, sscale_, ascale_, kscale_, 
         alow_, aup_, mini_, sini_, aini_, kini_)

`Y_`	a vector of length n which indicates the response variables
`L_`	a vector of length n; it indicates the time duration during which the Poisson counts are accumulated, or the total number of trials for Binomial response; if 0 is found in the vector, 1 will be used to replace all the values in the vector
`T_`	a n \times 2 matrix which indicates the coordinates of locations
`D_`	a n \times p covariate matrix; the default value "NULL" indicates no covariate
`run_`	the number of iterations
`nmLan_`	the number of internal iterations for latern variables
`fam_`	take the value of `"rhoPowerExp"`, `"rhoMatern"`, or `"rhoSph"` which indicates the powered exponential, Matern, or Spherical correlation function is used
`famY_`	take the value of `"Poisson"` or `"Binomial"` which indicates Poisson or Binomial distribution for response variables
`famSig_`	the prior distribution for σ, the options include "Halft" (positive-truncated t distribution), "InvGamma" (inverse gamma distribution), and "Reciprocal" (reciprocal distribution)
`par1_`	the parameters for the prior distribution of σ: when priorSigma = "Halft" the first parameter is scale and the second is degree of freedom; when priorSigma = "InvGamma" the first parameter is shape and the second is scale; when priorSigma = "Reciprocal" both parameters are ignored
`par2_`	the parameters for the prior distribution of σ: when priorSigma = "Halft" the first parameter is scale and the second is degree of freedom; when priorSigma = "InvGamma" the first parameter is shape and the second is scale; when priorSigma = "Reciprocal" both parameters are ignored
`ifkappa_`	take zero or non-zero value which indicates whether κ should be sampled
`scale_`	the tuning parameters for S respectively
`mscale_`	the tuning parameters for β respectively
`sscale_`	the tuning parameters for σ respectively
`ascale_`	the tuning parameters for φ respectively
`kscale_`	the tuning parameters for κ respectively
`alow_`	the lower bound for φ respectively
`aup_`	the upper bound for φ respectively
`mini_`	the initial for β respectively
`sini_`	the initial for σ respectively
`aini_`	the initial for φ respectively
`kini_`	the initial for κ respectively