SUMMER (version 2.0.0)

rst: 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
)

Value

a matrix (for spatial or temporal) or a three-dimensional array (for spatial-temporal) of the random effects.

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

Author

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

if (FALSE) {
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))
}