mcstrga_mala | R Documentation |
Draw MCMC samples from the transformed Gaussian model with known link function
mcstrga_mala(
formula,
data,
weights,
subset,
offset,
atsample,
corrfcn = "matern",
linkp,
phi,
omg,
kappa,
Nout,
Nthin = 1,
Nbi = 0,
betm0,
betQ0,
ssqdf,
ssqsc,
tsqdf,
tsqsc,
corrpriors,
corrtuning,
malatuning,
longlat = FALSE,
test = FALSE
)
formula |
A representation of the model in the form
|
data |
An optional data frame containing the variables in the model. |
weights |
An optional vector of weights. Number of replicated samples. |
subset |
An optional vector specifying a subset of observations to be used in the fitting process. |
offset |
See |
atsample |
A formula in the form |
corrfcn |
Spatial correlation function. See
|
linkp |
Parameter of the link function. A scalar value. |
phi |
Optional starting value for the MCMC for the
spatial range parameter |
omg |
Optional starting value for the MCMC for the
relative nugget parameter |
kappa |
Optional starting value for the MCMC for the
spatial correlation parameter |
Nout |
Number of MCMC samples to return. This can be a vector for running independent chains. |
Nthin |
The thinning of the MCMC algorithm. |
Nbi |
The burn-in of the MCMC algorithm. |
betm0 |
Prior mean for beta (a vector or scalar). |
betQ0 |
Prior standardised precision (inverse variance) matrix. Can be a scalar, vector or matrix. The first two imply a diagonal with those elements. Set this to 0 to indicate a flat improper prior. |
ssqdf |
Degrees of freedom for the scaled inverse chi-square prior for the partial sill parameter. |
ssqsc |
Scale for the scaled inverse chi-square prior for the partial sill parameter. |
tsqdf |
Degrees of freedom for the scaled inverse chi-square prior for the measurement error parameter. |
tsqsc |
Scale for the scaled inverse chi-square prior for the measurement error parameter. |
corrpriors |
A list with the components |
corrtuning |
A vector or list with the components |
malatuning |
Tuning parameter for the MALA updates. |
longlat |
How to compute the distance between locations. If
|
test |
Whether this is a trial run to monitor the acceptance
ratio of the random walk for |
Simulates from the posterior distribution of this model.
A list containing the objects MODEL
, DATA
,
FIXED
, MCMC
and call
. The MCMC samples are
stored in the object MCMC
as follows:
z
A matrix containing the MCMC samples for the
spatial random field. Each column is one sample.
mu
A matrix containing the MCMC samples for the
mean response (a transformation of z). Each column is one sample.
beta
A matrix containing the MCMC samples for the
regressor coefficients. Each column is one sample.
ssq
A vector with the MCMC samples for the partial
tsq
A vector with the MCMC samples for the
measurement error variance.
phi
A vector with the MCMC samples for the spatial
range parameter, if sampled.
omg
A vector with the MCMC samples for the relative
nugget parameter, if sampled.
logLik
A vector containing the value of the
log-likelihood evaluated at each sample.
acc_ratio
The acceptance ratio for the joint update
of the parameters phi
and omg
, if sampled.
sys_time
The total computing time for the MCMC sampling.
Nout
, Nbi
, Nthin
As in input. Used
internally in other functions.
The other objects contain input variables. The object call
contains the function call.
## Not run:
### Load the data
data(rhizoctonia)
rhiz <- na.omit(rhizoctonia)
rhiz$IR <- rhiz$Infected/rhiz$Total # Incidence rate of the
# rhizoctonia disease
### Define the model
corrf <- "spherical"
ssqdf <- 1
ssqsc <- 1
tsqdf <- 1
tsqsc <- 1
betm0 <- 0
betQ0 <- diag(.01, 2, 2)
phiprior <- c(200, 1, 1000, 100) # U(100, 300)
phisc <- 1
omgprior <- c(3, 1, 1000, 0) # U(0, 3)
omgsc <- 1
linkp <- 1
## MCMC parameters
Nout <- 100
Nbi <- 0
Nthin <- 1
samplt <- mcstrga_mala(Yield ~ IR, data = rhiz,
atsample = ~ Xcoord + Ycoord, corrf = corrf,
Nout = Nout, Nthin = Nthin,
Nbi = Nbi, betm0 = betm0, betQ0 = betQ0,
ssqdf = ssqdf, ssqsc = ssqsc,
tsqdf = tsqdf, tsqsc = tsqsc,
corrprior = list(phi = phiprior, omg = omgprior),
linkp = linkp,
corrtuning = list(phi = phisc, omg = omgsc, kappa = 0),
malatuning = .0002, test=10)
sample <- update(samplt, test = FALSE)
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.