Usage
gpPost(y, ipr, Omega_t, Omega_s, gp_mod, jitter, gp_shape, gp_rate, noise_shape, noise_rate, lower, upper, w, n_slice_iter, y_index, n.iter, n.burn, n.thin, n.tune, progress)
Arguments
y
An N x T matrix of N observations of T x 1 functions
ipr
An optional input vector of inclusion probabilities for each observation unit in the case
the observed data were acquired through an informative sampling design, so that unbiased
inference about the population requires adjustments to the observed sample. Defaults to
ipr = rep(1,nrow(y)) indicating an iid sample.
Omega_t
A T x T matrix of squared Eucidean distances for T time points
Omega_s
A list object of length L_s, where each contains the T x T
matrix of Euclidean distances associated to each seasonal covariance term.
gp_mod
An L x 1 numeric vector denoting the selected covariance function for each
of L terms. gp_mod = 1 is "rq". gp_mod = 2 is "se".
gp_mod = 3 is "sn".
jitter
Numeric value added to diagonals of GP covariance matrix to stabilize inversion.
gp_shape
The shape parameter of the Gamma base distribution for the kappa_star
locations used to sample the DP prior on the P GP covariance parameters,
kappa, for each experimental unit.
gp_rate
The rate parameter of the Gamma base distribution for the kappa_star
locations used to sample the DP prior on the P GP covariance parameters,
kappa, for each experimental unit.
noise_shape
The shape parameter of the Gamma base distribution on tau_e, the
model noise precision parameter. Defaults to noise_shape = 3.
noise_rate
The rate parameter of the Gamma base distribution on tau_e, the model
noise precision parameter. Defaults to noise_rate = 1.
lower
Minimum in range of support for GP covariance parameters, kappa.
upper
Maximum in range of support for GP covariance parameters, kappa.
w
Tuning parameter for slice sampling interval width used for GP
covariance parameters, kappa.
n_slice_iter
Maximum number of steps to widen slice samplind width for
GP covariance parameters, kappa.
y_index
List object where each contains index of time points to use in n
progressively coarser distribution for sampling kappa in tempered update steps.
n.iter
The number of MCMC sampling iterations
n.burn
The number of warm-up iterations to discard
n.thin
The interval or step size of post-burn-in samples to return
n.tune
The number of tuning iterations to update the slice sampler width, w.
progress
An indicator in {0,1} denoting whether to display a progress bar during model execution.
progress = 1 displays a progress bar. Defaults to progress = 1.