Description Usage Arguments Value See Also Examples
Estimation the spatial regularization parameters on external data using mean field approximation.
1 2 3 |
Y |
a matrix containing the observations (by rows) for the various groups (by columns). REQUIRED. |
W_SR |
the local neighbourhood matrix. |
rho_max |
Maximum possible rho value (numeric), minimum is 0. |
prior_prevalence |
should a prior on class prevalence be including when estimating the regularisation parameters ? logical. |
test.regional |
Should regional regularization be considered. logical. |
W_LR |
the regional neighbourhood matrix. dgCMatrix. Should be contains the distances between the observations (0 indicating infinite distance). |
distance.ref |
the interval of distance defining the several neighbourhood orders in |
threshold |
the minimum value of the posterior probability for group G for being considered as lesioned when forming the spatial groups. double. |
nbGroup_min |
the minimum group size of the spatial groups required for computing the regional potential. integer. |
coords |
coordinates of each site. matrix. |
regionalGroups |
how should the regional potential be computed : last group versus the others ( |
multiV |
should the regional neighbourhood range be computed for each spatial group ? logical. |
A numericVector containing the estimated regularisation parameter(s).
calcW
to compute the neighbourhood matrix,
simulPotts
to draw simulations from a Potts model.
rhoLvfree
to estimate the regularization parameters using mean field approximation.
calcPottsParameter
general interface for estimating the regularization parameters.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | # spatial field
## Not run:
n <- 50
## End(Not run)
G <- 3
coords <- which(matrix(0, nrow = n * G, ncol = n * G) == 0,arr.ind = TRUE)
# neighbourhood matrix
W_SR <- calcW(as.data.frame(coords), range = sqrt(2), row.norm = TRUE)$W
W_LR <- calcW(as.data.frame(coords), range = 10, row.norm = FALSE)$W
# initialisation
set.seed(10)
sample <- simulPotts(W_SR, G = 3, rho = 3.5, iter_max = 500,
site_order = TRUE)$simulation
multiplot(as.data.frame(coords), sample,palette = "rgb")
# estimation
rho <- rhoMF(Y=sample, W_SR = W_SR)
rho
# the regional potential is computed for each group
rho <- rhoMF(Y = sample, W_SR = W_SR,
test.regional = TRUE, W_LR = W_LR, distance.ref = seq(1, 10, 0.5),
coords = coords, regionalGroups = "each")
rho
# the regional potential is computed for the last group vs. the others
rho <- rhoMF(Y = sample, W_SR = W_SR,
test.regional = TRUE, W_LR = W_LR, distance.ref = seq(1, 10, 0.5),
coords = coords, regionalGroups = "last_vs_others")
rho
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