Usage
runMCMCpartialPois_(Y_, L_, T_, D_, run_, nmLan_, fam_, famY_, famT_, ifkappa_, scale_, mscale_, sscale_, ascale_, kscale_, alow_, aup_, mini_, sini_, aini_, kini_)
Arguments
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
famT_
take the value of 1, 2, or 3 which indicates the type of partial posterior sampling: 1 means "mean" diagnostic statistic is used, 2 means "maximum", and 3 means "minimum"; ignored if partial=FALSE
ifkappa_
take zero or non-zero value which indicates whether $\kappa$ should be sampled
scale_
the tuning parameters for $S$ respectively
mscale_
the tuning parameters for $\beta$ respectively
sscale_
the tuning parameters for $\sigma$ respectively
ascale_
the tuning parameters for $\phi$ respectively
kscale_
the tuning parameters for $\kappa$ respectively
alow_
the lower bound for $\phi$ respectively
aup_
the upper bound for $\phi$ respectively
mini_
the initial for $\beta$ respectively
sini_
the initial for $\sigma$ respectively
aini_
the initial for $\phi$ respectively
kini_
the initial for $\kappa$ respectively