In order to better work with two-dimensional Gaussian process models in `brms`

I would like to generate data with known parameters. Given that `brms`

only has the exponential quadratic kernel implemented, in particular I’d like to see if I can recover the `lscale`

parameter of this kernel.

In the following code, I generate data on a grid, and attempt to recover `lscale = 0.5`

, and `sdx1x2 = 0.25`

. Despite a sample size of 100 (the highest I want to go due to speed constraints), I am not able to recover a parameter value close to the `lscale`

I used to generate the data, even with the `gp(..., scale=FALSE)`

argument set. The `sdx1x2`

parameter is also off. The model samples well.

Is this because I am misinterpreting the `lscale`

parameter in some way, or have generated the data incorrectly. Alternatively, is it a sample size issue (i.e. more data are required to ensure generated data converges on intended parameters)?

```
library(MASS)
library(brms)
## Generate data with a exponential quadratic kernel
## covariance structure in two dimensions
set.seed(186429)
## Specify parameters
N <- 100
lscale <- 0.5
sdx1x2 <- 0.25
b0 <- 0
## Generate exponential quadratic kernel covariance given lscale
exp_quad <- function(D, lscale, eps=sqrt(.Machine$double.eps)) {
Sigma <- exp(-D/2*lscale^2) + diag(eps, nrow(D))
}
rows <- seq(0, 1, length.out=trunc(sqrt(N)))
cols <- seq(0, 1, length.out=trunc(sqrt(N)))
df <- expand.grid(x1=rows, x2=cols)
Sigma <- exp_quad(as.matrix(dist(cbind(df$x1, df$x2))), lscale)
## Ensure variance reflects sdx1x2
Sigma <- Sigma * sdx1x2^2
## Draw from this Gaussian process
df$gpx1x2 <- mvrnorm(n=1, mu=rep(0, nrow(Sigma)), Sigma=Sigma)
## Generate data y ~ b0 + gpx1x2
df$y <- b0 + df$gpx1x2
## Try to recover parameters using brms
## set gp(..., scale=FALSE) to maximize interpretability
m <- brm(y ~ gp(x1, x2, scale=FALSE), data=df,
iter=2000, cores=4, refresh=1)
summary(m)
Family: gaussian
Links: mu = identity; sigma = identity
Formula: y ~ gp(x1, x2, scale = FALSE)
Data: df (Number of observations: 100)
Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup samples = 4000
Gaussian Process Terms:
Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
sdgp(gpx1x2) 0.06 0.02 0.04 0.10 1043 1.00
lscale(gpx1x2) 0.25 0.03 0.19 0.33 643 1.00
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
Intercept 0.38 0.03 0.33 0.43 1502 1.00
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
sigma 0.02 0.00 0.02 0.03 1818 1.00
Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
is a crude measure of effective sample size, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
```