Calculates the Recurrence Rate versus Recurrence Time power-law
rn_strengthDist(g, mode = c("in", "out", "all")[3], doPlot = TRUE)Arguments
- g
an igraph object representing a weighted Recurrence Network
- mode
Evaluate the "in", "out" degree, or "all" edges (default =
"all")- doPlot
Plot the scaling relation? (default = TRUE)
Value
A data frame with local vertex strength and vertex degree, including
Examples
y <- rnorm(100)
RN <- rn(y, emLag=1, emDim=3, emRad=NA, weighted = TRUE, weightedBy = "rt", returnGraph = TRUE)
rn_strengthDist(RN$g)
#> xDegree yStrength xDegree_log10 yStrength_log10 PowerLaw PowerLawExponent
#> 1 4 148 0.6020600 2.1702617 2.044780 1.17196
#> 2 4 89 0.6020600 1.9493900 2.044780 1.17196
#> 3 2 138 0.3010300 2.1398791 1.691985 1.17196
#> 4 3 121 0.4771213 2.0827854 1.898357 1.17196
#> 5 4 180 0.6020600 2.2552725 2.044780 1.17196
#> 6 4 213 0.6020600 2.3283796 2.044780 1.17196
#> 7 4 203 0.6020600 2.3074960 2.044780 1.17196
#> 8 2 49 0.3010300 1.6901961 1.691985 1.17196
#> 9 10 391 1.0000000 2.5921768 2.511150 1.17196
#> 10 1 9 0.0000000 0.9542425 1.339190 1.17196
#> 11 1 9 0.0000000 0.9542425 1.339190 1.17196
#> 12 3 123 0.4771213 2.0899051 1.898357 1.17196
#> 13 3 69 0.4771213 1.8388491 1.898357 1.17196
#> 14 1 26 0.0000000 1.4149733 1.339190 1.17196
#> 15 3 57 0.4771213 1.7558749 1.898357 1.17196
#> 16 9 327 0.9542425 2.5145478 2.457524 1.17196
#> 17 11 375 1.0413927 2.5740313 2.559660 1.17196
#> 18 1 9 0.0000000 0.9542425 1.339190 1.17196
#> 19 2 30 0.3010300 1.4771213 1.691985 1.17196
#> 20 4 64 0.6020600 1.8061800 2.044780 1.17196
#> 21 7 294 0.8450980 2.4683473 2.329611 1.17196
#> 22 11 448 1.0413927 2.6512780 2.559660 1.17196
#> 23 12 503 1.0791812 2.7015680 2.603947 1.17196
#> 24 9 355 0.9542425 2.5502284 2.457524 1.17196
#> 25 4 114 0.6020600 2.0569049 2.044780 1.17196
#> 26 4 151 0.6020600 2.1789769 2.044780 1.17196
#> 27 7 280 0.8450980 2.4471580 2.329611 1.17196
#> 28 7 244 0.8450980 2.3873898 2.329611 1.17196
#> 29 4 103 0.6020600 2.0128372 2.044780 1.17196
#> 30 1 8 0.0000000 0.9030900 1.339190 1.17196
#> 31 1 25 0.0000000 1.3979400 1.339190 1.17196
#> 32 11 371 1.0413927 2.5693739 2.559660 1.17196
#> 33 7 181 0.8450980 2.2576786 2.329611 1.17196
#> 34 6 207 0.7781513 2.3159703 2.251152 1.17196
#> 35 10 346 1.0000000 2.5390761 2.511150 1.17196
#> 36 9 247 0.9542425 2.3926970 2.457524 1.17196
#> 37 5 94 0.6989700 1.9731279 2.158355 1.17196
#> 38 1 36 0.0000000 1.5563025 1.339190 1.17196
#> 39 2 54 0.3010300 1.7323938 1.691985 1.17196
#> 40 1 51 0.0000000 1.7075702 1.339190 1.17196
#> 41 1 29 0.0000000 1.4623980 1.339190 1.17196
#> 42 4 150 0.6020600 2.1760913 2.044780 1.17196
#> 43 3 120 0.4771213 2.0791812 1.898357 1.17196
#> 44 3 91 0.4771213 1.9590414 1.898357 1.17196
#> 45 9 157 0.9542425 2.1958997 2.457524 1.17196
#> 46 11 224 1.0413927 2.3502480 2.559660 1.17196
#> 47 15 321 1.1760913 2.5065050 2.717522 1.17196
#> 48 14 336 1.1461280 2.5263393 2.682406 1.17196
#> 49 10 193 1.0000000 2.2855573 2.511150 1.17196
#> 50 1 19 0.0000000 1.2787536 1.339190 1.17196
#> 51 1 19 0.0000000 1.2787536 1.339190 1.17196
#> 52 2 28 0.3010300 1.4471580 1.691985 1.17196
#> 53 4 66 0.6020600 1.8195439 2.044780 1.17196
#> 54 1 30 0.0000000 1.4771213 1.339190 1.17196
#> 55 2 4 0.3010300 0.6020600 1.691985 1.17196
#> 56 9 172 0.9542425 2.2355284 2.457524 1.17196
#> 57 9 199 0.9542425 2.2988531 2.457524 1.17196
#> 58 11 227 1.0413927 2.3560259 2.559660 1.17196
#> 59 1 6 0.0000000 0.7781513 1.339190 1.17196
#> 60 1 28 0.0000000 1.4471580 1.339190 1.17196
#> 61 14 354 1.1461280 2.5490033 2.682406 1.17196
#> 62 13 263 1.1139434 2.4199557 2.644687 1.17196
#> 63 4 97 0.6020600 1.9867717 2.044780 1.17196
#> 64 1 6 0.0000000 0.7781513 1.339190 1.17196
#> 65 1 29 0.0000000 1.4623980 1.339190 1.17196
#> 66 2 69 0.3010300 1.8388491 1.691985 1.17196
#> 67 5 186 0.6989700 2.2695129 2.158355 1.17196
#> 68 5 186 0.6989700 2.2695129 2.158355 1.17196
#> 69 3 141 0.4771213 2.1492191 1.898357 1.17196
#> 70 2 132 0.3010300 2.1205739 1.691985 1.17196
#> 71 1 52 0.0000000 1.7160033 1.339190 1.17196
#> 72 3 145 0.4771213 2.1613680 1.898357 1.17196
#> 73 4 195 0.6020600 2.2900346 2.044780 1.17196
#> 74 5 260 0.6989700 2.4149733 2.158355 1.17196
#> 75 13 400 1.1139434 2.6020600 2.644687 1.17196
#> 76 13 331 1.1139434 2.5198280 2.644687 1.17196
#> 77 7 305 0.8450980 2.4842998 2.329611 1.17196
#> 78 8 240 0.9030900 2.3802112 2.397575 1.17196
#> 79 7 269 0.8450980 2.4297523 2.329611 1.17196
#> 80 13 495 1.1139434 2.6946052 2.644687 1.17196
#> 81 16 528 1.2041200 2.7226339 2.750370 1.17196
#> 82 15 481 1.1760913 2.6821451 2.717522 1.17196
#> 83 3 109 0.4771213 2.0374265 1.898357 1.17196
#> 84 2 52 0.3010300 1.7160033 1.691985 1.17196
#> 85 4 273 0.6020600 2.4361626 2.044780 1.17196
#> 86 3 129 0.4771213 2.1105897 1.898357 1.17196
#> 87 2 82 0.3010300 1.9138139 1.691985 1.17196