rst {SUMMER} | R Documentation |
Simulate spatial and temporal random effects
Description
This function simulates spatial and temporal random effects with mean zero. The method is described in Algorithm 3.1 of Rue & Held 2015.
Usage
rst(
n = 1,
type = c("s", "t", "st")[1],
type.s = "ICAR",
type.t = c("RW1", "RW2")[2],
Amat = NULL,
n.t = NULL,
scale.model = TRUE
)
Arguments
n |
sample size |
type |
type of random effects: temporal (t), spatial (s), or spatial-temporal (st) |
type.s |
type of spatial random effect, currently only ICAR is available |
type.t |
type of temporal random effect, currently only RW1 and RW2 are available |
Amat |
adjacency matrix for the spatial regions |
n.t |
number of time points for the temporal random effect |
scale.model |
logical indicator of whether to scale the random effects to have unit generalized variance. See Sørbye 2013 for more details |
Value
a matrix (for spatial or temporal) or a three-dimensional array (for spatial-temporal) of the random effects.
Author(s)
Zehang Richard Li
References
Rue, H., & Held, L. (2005). Gaussian Markov random fields: theory and applications. CRC press.
Sørbye, S. H. (2013). Tutorial: Scaling IGMRF-models in R-INLA. Department of Mathematics and Statistics, University of Tromsø.
Examples
## Not run:
data(DemoMap)
## Spatial random effects
out <- rst(n=10000, type = "s", Amat = DemoMap$Amat)
# To verify the mean under the conditional specification
mean(out[,1] - apply(out[,c(2,3,4)], 1, mean))
mean(out[,2] - apply(out[,c(1,3)], 1, mean))
mean(out[,3] - apply(out[,c(1,2,4)], 1, mean))
mean(out[,4] - apply(out[,c(1,3)], 1, mean))
## Temporal random effects (RW1)
out <- rst(n=1, type = "t", type.t = "RW1", n.t = 200, scale.model = FALSE)
par(mfrow = c(1,2))
plot(1:dim(out)[2], out, col = 1, type = "l", xlab = "Time", ylab = "Random effects")
# verify the first order difference is normally distributed
first_diff <- diff(as.numeric(out[1,]))
qqnorm(first_diff )
abline(c(0,1))
## Temporal random effects (RW2)
out <- rst(n=1, type = "t", type.t = "RW2", n.t = 200, scale.model = FALSE)
par(mfrow = c(1,2))
plot(1:dim(out)[2], out, col = 1, type = "l", xlab = "Time", ylab = "Random effects")
# verify the second order difference is normally distributed
first_diff <- diff(as.numeric(out[1,]))
second_diff <- diff(first_diff)
qqnorm(second_diff)
abline(c(0,1))
## Spatial-temporal random effects
out <- rst(n=1, type = "st", type.t = "RW2", Amat = DemoMap$Amat, n.t = 50)
dimnames(out)
par(mfrow = c(1,1))
plot(1:dim(out)[3], out[1,1,], col = 1,
type = "l", ylim = range(out), xlab = "Time", ylab = "Random effects")
for(i in 2:4) lines(1:dim(out)[3], out[1,i,], col = i)
legend("bottomright", colnames(DemoMap$Amat), col = c(1:4), lty = rep(1,4))
## End(Not run)