The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

CARME

Stan based functions to estimate CAR-MM models. These models allow to estimate Generalised Linear Models with CAR (conditional autoregressive) spatial random effects for spatially and temporally misaligned data, provided a suitable Multiple Membership matrix.

The main references are:

Installation

# CRAN installation
install.packages("CARME")

# Or the development version from GitHub:
# install.packages("devtools")
devtools::install_github("markgrama/CARME")

Usage

We start by setting the number of membership we wish to use. In this case we take the adjacency matrix from the South East London (SEL) dataset used in Gramatica et al. (2023). It consists of \(m = 153\) memberships and \(n = 152\) areas.

set.seed(455)
    #---- Load data
    data(W_sel)
    ## Number of areas
    n <- nrow(W_sel)
    ## Number of memberships
    m <- 153
    #---- Simulate covariates
    X <- cbind(rnorm(nrow(W_sel)), rnorm(nrow(W_sel)))
    ## Min-max normalisation
    X_cent <- apply(X, 2, function(x) (x - min(x))/diff(range(x)))

Then, we simulate a \((153 \times 15)2\) Multiple Membership matrix, together with the observed outcomes generated using a poisson distribution and a CAR prior.

    #---- Simulate MM matrix
    w_ord <- c(.5, .35, .15) # Weight of each neighbours orders
    ord <- length(w_ord) - 1 # Order of neighbours to include
    H_sel_sim <- sim_MM_matrix(
      W = W_sel, m = m, ord = ord, w_ord = w_ord, id_vec = rep(1, nrow(W_sel))
    )
    
    #---- Simulate outcomes
    ## Linear term parameters
    gamma <- -.5 # Intercept
    beta <- c(1, .5) # Covariates coefficients
    ## CAR random effects
    phi_car <- sim_car(W = W_sel, alpha = .9, tau = 5)
    # Areal log relative risks
    l_RR <- X_cent %*% beta + phi_car
    ## Membership log relative risks
    l_RR_mm <- as.numeric(apply(H_sel_sim, 1, function(x) x %*% l_RR))
    ## Expected rates
    exp_rates <- rpois(m, lambda = 20)
    ## Outcomes
    y <- rpois(m, lambda = exp_rates*exp(l_RR_mm))
    #---- Create dataset for stan function
    d_sel <- list(
      # Number of areas
      n = nrow(W_sel),
      # Covariates
      k = ncol(X_cent),
      X_cov = X_cent,
      # Adjacency
      W_n = sum(W_sel) / 2,
      # Number of neighbour pairs
      W = W_sel,
      # Memberships
      m = nrow(H_sel_sim),
      H = H_sel_sim,
          # Outcomes
    y = y,
    log_offset = log(exp_rates),
    # Prior parameters
    ## Intercept (mean and sd of normal prior)
    mu_gamma = 0, sigma_gamma = 1,
    ## Covariates (mean and sd of normal prior)
    mu_beta = 0, sigma_beta = 1,
    ## Marginal precision gamma prior
    tau_shape = 2,
    tau_rate = 0.2
)

Finally, we can run the model:

 #---- HMC parameters
  niter <- 1E4
  nchains <- 4
  #---- Stan sampling
  fit <- car_mm(
    d_list = d_sel,
    # arguments passed to sampling
    iter = niter, chains = nchains, refresh = 500,
    control = list(adapt_delta = .99, max_treedepth = 15)
)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.