MRIaggr (version 1.1.3)

calcBlockW: Find disjoint spatial blocks of sites

Description

Partition the space into disjoint spatial blocks of sites. Call the C++ function calcOrderSite_hpp. For internal use.

Usage

calcBlockW(W, site_order = NULL, dist.center = NULL, dist.max = Inf, 
         verbose = optionsMRIaggr("verbose"))

Arguments

W

the neighbourhood matrix. dgCMatrix. REQUIRED.

site_order

a specific order to go all over the sites. integer vector.

dist.center

the distance between each point and a reference point. numeric vector.

dist.max

the neighbourhood range. numeric vector.

verbose

Should the process be verbose over iterations ? logical.

Value

• An list containing :
  • [[ls_groups]]: alistcontaining the index of the sites for each independant group.
  • [[size_groups]]: avectorcontaining the size of each independant group.
  • [[n_groups]]: anintegergiving the number of independant groups.

concept

calc.

Details

This function requires to have installed the Matrix and the spam package to work. If no specific order is set, sites are visitating from the first to the last, according to the neighbourhood matrix.

Examples

#### spatial field
n <- 100
n <- 10
coords <- data.frame(which(matrix(0, nrow = n, ncol = n) == 0,arr.ind = TRUE), 1)
optionsMRIaggr(quantiles.legend = FALSE, axes = FALSE, num.main = FALSE, bg = "white")

#### 1- neighbourhood matrix (king) ####
W_king <- calcW(coords, range = 1.001, row.norm = TRUE)$W

#### find independant groups
Block_king <- calcBlockW(W_king)

## check groups
# diagonal : percent of neighborhing sites whithin group
# extra-diagonal : percent of neighborhing sites between groups
sapply(1:Block_king$n_groups, function(x){
  sapply(1:Block_king$n_groups, function(y){
    sum(spam::rowSums(W_king[Block_king$ls_groups[[x]], Block_king$ls_groups[[y]]] > 0) > 0)
  }) / length(Block_king$ls_groups[[x]])
}
)

## diplay sparse matrix
spam::image(W_king)
spam::image(W_king[unlist(Block_king$ls_groups), unlist(Block_king$ls_groups)])

## display site blocks
col_sites <- unlist(lapply(1:Block_king$n_groups, function(x){
	rep(rainbow(Block_king$n_groups)[x], Block_king$size_groups[x])
}))

multiplot(coords[unlist(Block_king$ls_groups),],
          xlim = c(0,30),ylim = c(0,30),
          col = col_sites, legend = FALSE)


#### 2- neighbourhood matrix (Queen) ####
W_queen <- calcW(coords, range = sqrt(2) + 0.001, row.norm = TRUE)$W

#### find independant groups
Block_queen <- calcBlockW(W_queen)

## check groups
# diagonal : percent of neighborhing sites whithin group
# extra-diagonal : percent of neighborhing sites between groups
sapply(1:Block_queen$n_groups, function(x){
  sapply(1:Block_queen$n_groups, function(y){
    sum(spam::rowSums(W_queen[Block_queen$ls_groups[[x]], Block_queen$ls_groups[[y]]] > 0) > 0)
  }) / length(Block_queen$ls_groups[[x]])
}
)

## diplay sparse matrix
spam::image(W_queen)
spam::image(W_queen[unlist(Block_queen$ls_groups), unlist(Block_queen$ls_groups)])

## display site blocks
col_sites <- unlist(lapply(1:Block_queen$n_groups, function(x){
	rep(rainbow(Block_queen$n_groups)[x], Block_queen$size_groups[x])
}))

multiplot(coords[unlist(Block_queen$ls_groups),],
          xlim = c(0,30), ylim = c(0,30),
          col = col_sites, legend = FALSE)

#### 3- neighbourhood matrix (Regional) ####
W_Regional <- calcW(coords, range = 3, row.norm = TRUE)$W

#### find independant groups
system.time(
  Block_Regional <- calcBlockW(W_Regional)
)

system.time(
Block_Regional_test1 <- calcBlockW(W_Regional, 
     dist.center = sqrt(spam::rowSums(sweep(coords, MARGIN = 2, 
	                                  STATS = apply(coords, 2, median), FUN = "-")^2))
     )
)
system.time(
  Block_Regional_test2 <- calcBlockW(W_Regional, 
     dist.center = sqrt(spam::rowSums(sweep(coords, MARGIN = 2,
                            	      STATS = apply(coords, 2, median), FUN = "-")^2)),
     dist.max = 3
  )
)
# all(unlist(Block_Regional_test1$ls_groups) == unlist(Block_Regional_test2$ls_groups))


## check groups
# diagonal : percent of neighborhing sites whithin group
# extra-diagonal : percent of neighborhing sites between groups
sapply(1:Block_Regional$n_groups,function(x){
   sapply(1:Block_Regional$n_groups,function(y){
    if(length(Block_Regional$ls_groups[[x]]) > 1){
      sum(spam::rowSums(as.matrix(W_Regional[Block_Regional$ls_groups[[x]],
	                                   Block_Regional$ls_groups[[y]]]) > 0) > 0)
    }else{
      sum(W_Regional[Block_Regional$ls_groups[[x]],
	                 Block_Regional$ls_groups[[y]]] > 0) > 0
    }
  }) / length(Block_Regional$ls_groups[[x]])
}
)
# clustering could be improved

## diplay sparse matrix
spam::image(W_Regional)
spam::image(W_Regional[unlist(Block_Regional$ls_groups), unlist(Block_Regional$ls_groups)])

## display site blocks
col_sites <- unlist(lapply(1:Block_Regional$n_groups, function(x){
rep(rainbow(Block_Regional$n_groups)[x], Block_Regional$size_groups[x])
}))

multiplot(coords[unlist(Block_Regional$ls_groups),],
          xlim = c(0,30), ylim = c(0,30),
          col = col_sites, legend = FALSE)