This function aggregates \(S\) parameter draws that have been clustered into \(S_{\mathrm{cl}}\) clusters by averaging across the draws that belong to the same cluster. This averaging can be done in a weighted fashion.
An \(S \times P\) matrix of parameter draws, with \(P\) denoting the number of parameters.
A numeric vector of length \(S\), giving the cluster indices for
the draws. The cluster indices need to be values from the set \(\{1,
..., S_{\mathrm{cl}}\}\), except for draws that should be
dropped (e.g., by thinning), in which case NA needs to be provided at the
positions of cl corresponding to these draws.
A numeric vector of length \(S\), giving the weights of the
draws. It doesn't matter whether these are normalized (i.e., sum to 1) or
not because internally, these weights are normalized to sum to 1 within
each cluster. Draws that should be dropped (e.g., by thinning) can (but
must not necessarily) have an NA in wdraws.
A positive numeric value (typically small) which will be
used to improve numerical stability: The weights of the draws within each
cluster are multiplied by 1 - eps_wdraws. The default of 0 should be
fine for most cases; this argument only exists to help in those cases where
numerical instabilities occur (which must be detected by the user; this
function will not detect numerical instabilities itself).
An \(S_{\mathrm{cl}} \times P\) matrix of aggregated parameter draws.
set.seed(323)
S <- 100L
P <- 3L
draws <- matrix(rnorm(S * P), nrow = S, ncol = P)
# Clustering example:
S_cl <- 10L
cl_draws <- sample.int(S_cl, size = S, replace = TRUE)
draws_cl <- cl_agg(draws, cl = cl_draws)
# Clustering example with nonconstant `wdraws`:
w_draws <- rgamma(S, shape = 4)
draws_cl <- cl_agg(draws, cl = cl_draws, wdraws = w_draws)
# Thinning example (implying constant `wdraws`):
S_th <- 50L
idxs_thin <- round(seq(1, S, length.out = S_th))
th_draws <- rep(NA, S)
th_draws[idxs_thin] <- seq_len(S_th)
draws_th <- cl_agg(draws, cl = th_draws)