stFit: Fit the remote effects spatial process (RESP) model

In jmhewitt/telefit: Estimation and Prediction for Remote Effects Spatial Process Models

Description

Fit the remote effects spatial process (RESP) model

Usage

stFit(
  stData = NULL,
  priors,
  maxIt,
  X = stData$X,
  Y = stData$Y,
  Z = stData$Z,
  coords.s = stData$coords.s,
  coords.r = stData$coords.r,
  rw.initsd = NULL,
  returnll = T,
  miles = T,
  C = 1,
  alpha = 0.44,
  localOnly = F,
  varying = F,
  remoteOnly = F,
  coords.knots
)

Arguments

stData	Object with class 'stData' containing data needed to fit this model. The data need only be manually entered if not using a stData object.
priors	A list containing parameters for the prior distributions. The list needs to contain the following values beta list(Lambda=matrix) specifying the prior covariance matrix for the local effects if varying==F, otherwise list(Psi=matrix, nu=double) specifying the Inverse wishart prior distribution for the spatially varying coefficient process if varying==T. cov.s list(smoothness=double, range=c(min, max), variance=c(shape, rate), nugget=c(shape, rate)) cov.r list(smoothness=double, range=c(min, max), variance=c(shape, rate), nugget=c(shape, rate))
maxIt	number of iterations to run the MCMC chain for
X	[ns, p, nt] array of design matrices with local covariates
Y	[ns, nt] matrix with response data
Z	[nr, nt] matrix with remote covariates
coords.s	matrix with coordinates where responses were observed (lon, lat)
coords.r	matrix with coordinates where remote covariates were observed (lon, lat)
rw.initsd	A list containing initial standard deviation parameters for the MCMC parameters requiring random walk updates cov.s list(range=double, nugget=double) cov.r list(range=double, variance=double, nugget=double)
returnll	TRUE to compute the model log-likelihood at each iteration
miles	TRUE if covariance matrix distances should be in miles, FALSE for kilometers
C	scaling factor used in adapting random walk proposal variances.
alpha	target acceptance rate for random walk proposals.
localOnly	TRUE to fit the model without the teleconnection effects (typically for evaluating impact of teleconnection effects)
varying	(depreceated) TRUE to fit the model with spatially varying local coefficients
remoteOnly	TRUE to fit the model without local effects. This will fit a local intercept, but will not incorporate local covariates.
coords.knots	matrix with coordinates where remote teleconnections will be based (lon, lat)

Examples

library(dplyr)
library(foreach)
library(itertools)

set.seed(2018)

data("coprecip")
data("coprecip.fit")
attach(coprecip)

coprecip.fit = stFit(stData = coprecip, priors = coprecip.fit$priors, 
                     maxIt = 10, coords.knots = coprecip.fit$coords.knots)

jmhewitt/telefit documentation built on Feb. 9, 2020, 7:15 p.m.