Corridor analysis

Create a corridor around the main diagonal. For long time series it may not make sense to evaluate recurrences on the longest time scales.

createCorridor(mat, lower, upper, value = NA, silent = TRUE)

Arguments

mat

A Matrix

lower

Lower diagonal to be included in the corridor (should be \(\le 0\))

upper

Upper diagonal to be included in the corridor (should be \(\ge 0\))

value

A single value to replace all values outside the corridor (default = NA)

silent

Operate in silence, only (some) warnings will be shown (default = TRUE)

Value

A matrix in which the values outside the corridor have been replaced

See also

Other Distance matrix operations (recurrence plot): bandReplace(), mat_di2bi(), mat_di2ch(), mat_di2we(), mat_hamming(), rp(), rp_lineDist(), rp_nzdiags(), rp_plot(), rp_size()

Author

Fred Hasselman

Examples

# Create a 10 by 10 matrix
library(Matrix)
m <- Matrix(rnorm(10),10,10)

createCorridor(m,-7,7,0)   # Set diagonals 10 9 and 8 to 0.
#> 10 x 10 sparse Matrix of class "dgCMatrix"
#>                                                                        
#>  [1,] -1.5123997 -1.5123997 -1.5123997 -1.5123997 -1.5123997 -1.5123997
#>  [2,]  0.9353632  0.9353632  0.9353632  0.9353632  0.9353632  0.9353632
#>  [3,]  0.1764886  0.1764886  0.1764886  0.1764886  0.1764886  0.1764886
#>  [4,]  0.2436855  0.2436855  0.2436855  0.2436855  0.2436855  0.2436855
#>  [5,]  1.6235489  1.6235489  1.6235489  1.6235489  1.6235489  1.6235489
#>  [6,]  0.1120381  0.1120381  0.1120381  0.1120381  0.1120381  0.1120381
#>  [7,] -0.1339970 -0.1339970 -0.1339970 -0.1339970 -0.1339970 -0.1339970
#>  [8,] -1.9100875 -1.9100875 -1.9100875 -1.9100875 -1.9100875 -1.9100875
#>  [9,] -0.2792372 -0.2792372 -0.2792372 -0.2792372 -0.2792372 -0.2792372
#> [10,] -0.3134460 -0.3134460 -0.3134460 -0.3134460 -0.3134460 -0.3134460
#>                                                  
#>  [1,] -1.5123997 -1.5123997 -1.5123997 -1.5123997
#>  [2,]  0.9353632  0.9353632  0.9353632  0.9353632
#>  [3,]  0.1764886  0.1764886  0.1764886  0.1764886
#>  [4,]  0.2436855  0.2436855  0.2436855  0.2436855
#>  [5,]  1.6235489  1.6235489  1.6235489  1.6235489
#>  [6,]  0.1120381  0.1120381  0.1120381  0.1120381
#>  [7,] -0.1339970 -0.1339970 -0.1339970 -0.1339970
#>  [8,] -1.9100875 -1.9100875 -1.9100875 -1.9100875
#>  [9,] -0.2792372 -0.2792372 -0.2792372 -0.2792372
#> [10,] -0.3134460 -0.3134460 -0.3134460 -0.3134460