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Log-Gaussian Cox processes on metric graphs

David Bolin, Alexandre B. Simas

Created: 2023-01-30. Last modified: 2025-04-21.

Source: vignettes/pointprocess.Rmd
pointprocess.Rmd

Introduction

In this vignette we will introduce how to work with log-Gaussian Cox processes based on Whittle–Matérn fields on metric graphs. To simplify the integration with R-INLA and inlabru hese models are constructed using finite element approximations as implemented in the rSPDE package. The theoretical details will be given in the forthcoming article (Bolin, Simas, and Wallin 2023).

Constructing the graph and the mesh

We begin by loading the rSPDE, MetricGraph and INLA packages:

As an example, we consider the default graph in the package:

graph <- metric_graph$new(tolerance = list(vertex_vertex = 1e-1, vertex_edge = 1e-3, edge_edge = 1e-3),
                          remove_deg2 = TRUE)
graph$plot()

To construct a FEM approximation of a Whittle–Matérn field, we must first construct a mesh on the graph.

graph$build_mesh(h = 0.1)
graph$plot(mesh=TRUE)

The next step is to build the mass and stiffness matrices for the FEM basis.

  graph$compute_fem()

We are now ready to specify the and sample from a log-Gaussian Cox process model with intensity λ=exp(β+u)\lambda = \exp(\beta + u) where β\beta is an intercept and uu is a Gaussian Whittle–Matérn field specified by (κ2−Δ)α/2τu=𝒲. (\kappa^2 - \Delta)^{\alpha/2} \tau u = \mathcal{W}. For this we can use the function graph_lgcp as follows:

  sigma <- 0.5
  range <- 2
  alpha <- 2
  lgcp_sample <- graph_lgcp(intercept = 1, sigma = sigma,
                            range = range, alpha = alpha,
                            graph = graph)

The object returned by the function is a list with the simulated Gaussian process and the points on the graph. We can plot the simulated intensity function as

graph$plot_function(X = exp(lgcp_sample$u), vertex_size = 0)

To plot the simulated points, we can add them to the graph and then plot:

graph$add_observations(data = data.frame(y=rep(1,length(lgcp_sample$edge_loc)),
                                         edge_number = lgcp_sample$edge_numbers,
                                         distance_on_edge = lgcp_sample$edge_loc),
                       normalized = TRUE)
## Adding observations...
## Assuming the observations are normalized by the length of the edge.
graph$plot(vertex_size = 0, data = "y")

Fitting LGCP models in R-INLA

We are now in a position to fit the model with our R-INLA implementation. When working with log-Gaussian Cox processes, the likelihood has a term ∫Γexp(u(s))ds\int_\Gamma \exp(u(s)) ds that needs to be handled separately. This is done by using the mid-point rule as suggested for SPDE models by Simpson et al. (2016) where we approximate ∫Γexp(u(s))ds≈∑i=1pãiexp(u(s̃i)). \int_\Gamma \exp(u(s)) ds \approx \sum_{i=1}^p \widetilde{a}_i \exp\left(u(\widetilde{s}_i)\right). Using the fact that u(s)=∑j=1nφ(s)uiu(s) = \sum_{j=1}^n \varphi(s) u_i from the FEM approximation, we can write the integral as α̃Texp(Ãu)\widetilde{\alpha}^T\exp(\widetilde{A}u) where Ãij=φj(s̃i)\widetilde{A}_{ij} = \varphi_j(\widetilde{s}_i) and ã\widetilde{a} is a vector with integration weights. These quantities can be obtained as

Atilde <- graph$fem_basis(graph$mesh$VtE)
atilde <- graph$mesh$weights

The weights are used as exposure terms in the Poisson likelihiood in R-INLA. Because of this, the easiest way to construct the model is to add the integration points as zero observations in the graph, with corresponding exposure weights. We also need to add the exposure terms (which are zero) for the actual observation locations:

#clear the previous data in the graph
graph$clear_observations()

#Add the data together with the exposure terms
graph$add_observations(data = data.frame(y = rep(1,length(lgcp_sample$edge_loc)),
                                         e = rep(0,length(lgcp_sample$edge_loc)),
                                         edge_number = lgcp_sample$edge_number,
                                         distance_on_edge = lgcp_sample$edge_loc),
                       normalized = TRUE)
## Adding observations...
## Assuming the observations are normalized by the length of the edge.
#Add integration points
graph$add_observations(data = data.frame(y = rep(0,length(atilde)),
                                         e = atilde,
                                         edge_number = graph$mesh$VtE[,1],
                                         distance_on_edge = graph$mesh$VtE[,2]),
                       normalized = TRUE)
## Adding observations...
## Assuming the observations are normalized by the length of the edge.

We now create the inla model object with the graph_spde function. For simplicity, we assume that α\alpha is known and fixed to the true value in the model.

rspde_model <- rspde.metric_graph(graph, nu = alpha - 1/2)

Next, we compute the auxiliary data:

data_rspde <- graph_data_rspde(rspde_model, name="field")

We now create the inla.stack object with the inla.stack() function. At this stage, it is important that the data has been added to the graph since it is supplied to the stack by using the graph_spde_data() function.

stk <- inla.stack(data = data_rspde[["data"]], 
                  A = data_rspde[["basis"]], 
                  effects = c(data_rspde[["index"]], list(Intercept = 1)))

We can now fit the model using R-INLA:

spde_fit <- inla(y ~ -1 + Intercept + f(field, model = rspde_model), 
                 family = "poisson", data = inla.stack.data(stk),
                 control.predictor = list(A = inla.stack.A(stk), compute = TRUE),
                 E = inla.stack.data(stk)$e,
                 num.threads = "1:1")

Let us extract the estimates in the original scale by using the spde_metric_graph_result() function, then taking a summary():

spde_result <- rspde.result(spde_fit, "field", rspde_model)

summary(spde_result)
##             mean        sd 0.025quant 0.5quant 0.975quant     mode
## std.dev 0.365558 0.0981158   0.198040 0.358359   0.579154 0.344213
## range   2.082120 0.9200630   0.845135 1.895720   4.397150 1.578380

We will now compare the means of the estimated values with the true values:

  result_df <- data.frame(
    parameter = c("std.dev", "range"),
    true = c(sigma, range),
    mean = c(
      spde_result$summary.std.dev$mean,
      spde_result$summary.range$mean
    ),
    mode = c(
      spde_result$summary.std.dev$mode,
      spde_result$summary.range$mode
    )
  )
  print(result_df)
##   parameter true      mean      mode
## 1   std.dev  0.5 0.3655578 0.3442127
## 2     range  2.0 2.0821166 1.5783788

We can also plot the posterior marginal densities with the help of the gg_df() function:

  posterior_df_fit <- gg_df(spde_result)

  library(ggplot2)

  ggplot(posterior_df_fit) + geom_line(aes(x = x, y = y)) + 
  facet_wrap(~parameter, scales = "free") + labs(y = "Density")

Finally, we can plot the estimated field uu:

n.obs <- length(graph$get_data()$y)
n.field <- dim(graph$mesh$VtE)[1]
u_posterior <- spde_fit$summary.linear.predictor$mean[(n.obs+1):(n.obs+n.field)]
graph$plot_function(X = u_posterior, vertex_size = 0)

This can be compared with the field that was used to generate the data:

graph$plot_function(X = lgcp_sample$u, vertex_size = 0)

An example with replicates

Let us now test show an example with replicates. Let us first simulate replicates of a latent field

  n.rep <- 30
  sigma <- 0.5
  range <- 2
  alpha <- 2
  lgcp_sample <- graph_lgcp(n = n.rep, intercept = 1, sigma = sigma,
                            range = range, alpha = alpha,
                            graph = graph)

We now clear the previous data and add the new data together with the exposure terms

  graph$clear_observations()
  df_rep <- data.frame(y=rep(1,length(lgcp_sample[[1]]$edge_loc)),
                                             e = rep(0,length(lgcp_sample[[1]]$edge_loc)),
                                         edge_number = lgcp_sample[[1]]$edge_number,
                                         distance_on_edge = lgcp_sample[[1]]$edge_loc,
                                         rep = rep(1,length(lgcp_sample[[1]]$edge_loc)))

  df_rep <- rbind(df_rep, data.frame(y = rep(0,length(atilde)),
                                         e = atilde,
                                         edge_number = graph$mesh$VtE[,1],
                                         distance_on_edge = graph$mesh$VtE[,2],
                                         rep = rep(1,length(atilde))))
  for(i in 2:n.rep){
    df_rep <- rbind(df_rep, data.frame(y=rep(1,length(lgcp_sample[[i]]$edge_loc)),
                                             e = rep(0,length(lgcp_sample[[i]]$edge_loc)),
                                         edge_number = lgcp_sample[[i]]$edge_number,
                                         distance_on_edge = lgcp_sample[[i]]$edge_loc,
                                         rep = rep(i,length(lgcp_sample[[i]]$edge_loc))))
    df_rep <- rbind(df_rep, data.frame(y = rep(0,length(atilde)),
                                         e = atilde,
                                         edge_number = graph$mesh$VtE[,1],
                                         distance_on_edge = graph$mesh$VtE[,2],
                                         rep = rep(i,length(atilde))))                                        

  }

      graph$add_observations(data = df_rep,
                       normalized = TRUE,
                        group = "rep")
## Adding observations...
## Assuming the observations are normalized by the length of the edge.

We can now define and fit the model as previously

rspde_model <- rspde.metric_graph(graph, nu = alpha - 1/2)

data_rspde <- graph_data_rspde(rspde_model, name = "field", 
                                  repl = ".all", repl_col = "rep")

stk <- inla.stack(data = data_rspde[["data"]], 
                  A = data_rspde[["basis"]], 
                  effects = c(data_rspde[["index"]], list(Intercept = 1)))

spde_fit <- inla(y ~ -1 + Intercept + f(field, model = rspde_model, replicate = field.repl), 
                 family = "poisson", data = inla.stack.data(stk),
                 control.predictor = list(A = inla.stack.A(stk), compute = TRUE),
                 E = inla.stack.data(stk)$e, verbose=TRUE,
                 num.threads = "1:1")

Let’s look at the summaries

spde_result <- rspde.result(spde_fit, "field", rspde_model)
summary(spde_result)
##             mean        sd 0.025quant 0.5quant 0.975quant     mode
## std.dev 0.483559 0.0169498    0.45103 0.483307   0.517582 0.482865
## range   2.168990 0.1814020    1.83326 2.162270   2.545290 2.149350
result_df <- data.frame(
    parameter = c("std.dev", "range"),
    true = c(sigma, range),
    mean = c(
      spde_result$summary.std.dev$mean,
      spde_result$summary.range$mean
    ),
    mode = c(
      spde_result$summary.std.dev$mode,
      spde_result$summary.range$mode
    )
  )
  print(result_df)
##   parameter true      mean      mode
## 1   std.dev  0.5 0.4835589 0.4828649
## 2     range  2.0 2.1689874 2.1493523

References

Bolin, David, Alexandre B. Simas, and Jonas Wallin. 2023. “Log-Cox Gaussian Processes and Space-Time Models on Compact Metric Graphs.” In Preparation.
Simpson, Daniel, Janine B. Illian, Finn Lindgren, Siv H. Sørbye, and Håvard Rue. 2016. “Going Off Grid: Computationally Efficient Inference for Log-Gaussian Cox Processes.” Biometrika.