Description Usage Arguments Examples
Compute log likelihood for model
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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. |
stFit |
Object with class 'stFit' containing posterior parameter samples needed to composition sample the teleconnection effects and generate posterior predictions. The data needed from stFit need only be manually entered if not using a stData object. |
beta |
values of β at which to evaluate the likelihood |
sigmasq_y |
values of σ^2_w at which to evaluate the likelihood |
sigmasq_r |
values of σ^2_α at which to evaluate the likelihood |
sigmasq_eps |
values of σ^2_\varepsilon at which to evaluate the likelihood |
rho_y |
values of ρ_w at which to evaluate the likelihood |
rho_r |
values of ρ_α at which to evaluate the likelihood |
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) |
coords.knots |
matrix with coordinates of knots for remote covariates (lon, lat) |
miles |
TRUE if distances should be computed in miles (kilometers otherwise) |
sigmasq_r_eps |
values of σ^2_{α_\varepsilon} at which to evaluate the likelihood |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | library(dplyr)
library(foreach)
library(itertools)
set.seed(2018)
data("coprecip")
data("coprecip.fit")
attach(coprecip)
ests = coef(coprecip.fit, burn = 50)
ll = stLL(stData = coprecip, stFit = coprecip.fit,
beta = matrix(ests$beta, ncol = 2),
sigmasq_y = ests$sigmasq_y, sigmasq_r = ests$sigmasq_r,
sigmasq_eps = ests$sigmasq_eps,
rho_y = ests$rho_y, rho_r = ests$rho_r,
sigmasq_r_eps = 0)
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