Sparse adjacency matrix for CAR?

Interfaces brms
,
1

Hi,

I want to use brms to fit a model on data from spatial polygons. I want to use CAR to account for autocorrelation in the response. Unfortunately my adjacency matrix is too large to fit into memory, that is I can compute it but brms returns the error (with traceback) when I fit the model.

cannot allocate vector of size 20.8 Gb
▆

  1. └─brms::brm(…)
  2. └─brms:::validate_data2(…)
  3. └─brms:::validate_car_matrix(get_from_data2(M, data2))

  4.   └─Matrix::Matrix(M, sparse = TRUE)

  5.     └─methods::as(data, if (sparse) "CsparseMatrix" else "unpackedMatrix")

  6.       └─Matrix (local) asMethod(object)

  7.         └─Matrix:::.m2sparse.checking(from, ".", "C")

  8.           ├─base::isSymmetric(from, ...)

  9.           ├─base::isSymmetric(from, ...)

  10.           └─base::isSymmetric.matrix(from, ...)

  11.             ├─base::all.equal(object, t(object), tolerance = tol, ...)

  12.             └─base::all.equal.numeric(object, t(object), tolerance = tol, ...)

  13.               └─base::as.vector(current)

Is there a way to use a sparse matrix representation for the adjacency matrix in brms? From the traceback it seems like brms already transforms the matrix into a sparse one but then the object should be smaller.

Thanks!
Jonathan

2

I’m not familiar with brms, but it could be helpful to share some code. More specifically, is your matrix simply something you set up and pass as an argument? I am assuming it is not, but otherwise you could make it as sparse as you need it.

In Stan, for instance, you could just use the diagonal of a matrix as a vector, and set up a diagonal matrix from it whenever you need it.

Without more information, I’m afraid I cannot be more helpful.