Count number of times edges have not been observed For empirical scanList, an edge that is observed during a scan is not NA (it will be either 0 or 1). This function counts how many time this was the case in the inputted scan.list for each edge.

Written as a S3 method to be applied to scanList or sLlist (list of scanList) objects.

count_nonNA(scan.list, ...)

Arguments

scan.list	a scanList or sLlist object, where an unobserved edge (whether it is 0 or 1) is NA. See objects returned by simunet()
...	additional arguments to be passed. At the moment scale_scans() does not use At the moment count_nonNA() does not use additional argument, arguments passed will be ignored.

Value

an integer matrix, or list of such, representing how many time each edge has been sampled (i.e. was not NA). Inherits from weightedAdj and the previous scanList class (theoretical or empirical, inheriting from scanList), and keeps track of the scan.list's list of attributes attrs.

See also

simunet(), design_exp(), perform_exp()], count_NA().

Examples

set.seed(42)
n <- 5L
samp.effort <- 241L

# Adjacency matrix import
## random directed adjacency matrix
Adj <- sample(1:samp.effort,n * n) |>
  matrix(nrow = 5,dimnames = list(letters[1:n],letters[1:n]))
diag(Adj) <- 0L
Adj
#>     a   b   c   d   e
#> a   0 228  47 165 111
#> b 229   0  24 110 131
#> c  65 122   0  20  41
#> d 153 241 100   0 227
#> e  74 128  89 114   0

# social network simulations
## theoretical scans
sL <- simunet(Adj = Adj,samp.effort = samp.effort,mode = "directed",n.scans = 120L)
sL
#> 
#> scan: 1           
#> a . 1 . 1 .
#> b 1 . . . .
#> c . . . . .
#> d . 1 1 . 1
#> e . . 1 . .
#> 
#> scan: 2           
#> a . 1 . . .
#> b 1 . . 1 .
#> c 1 1 . . .
#> d . 1 . . 1
#> e . . 1 1 .
#> 
#> ... ( 117  more scans)
#> 
#> scan: 120           
#> a . 1 . 1 1
#> b 1 . . 1 .
#> c . 1 . . .
#> d 1 1 . . 1
#> e . 1 1 . .
#> 
#> 
#> Hidden attributes:
#> scanList.type - raw.scanList - Adj - samp.effort - n.scans - mode
#> Adj.subfun - edge.Prob

## group-scan sampling
sL |> perform_exp(design_sampling("group",.6)) |> count_nonNA()
#> 
#> Weighted adjacency matrix                
#> a  . 71 66 76 65
#> b 70  . 79 72 75
#> c 77 64  . 66 66
#> d 75 63 65  . 80
#> e 67 67 71 75  .
#> 
#> 
#> Hidden attributes:
#> scanList.type - raw.scanList - Adj - samp.effort - n.scans - mode
#> Adj.subfun - edge.Prob - obs.P - theoretical.scanList