Calculates the Recurrence Rate versus Recurrence Time power-law
Usage
rn_strengthDist(g, mode = c("in", "out", "all")[3], doPlot = TRUE)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 3 125 0.4771213 2.0969100 1.990599 0.993944
#> 2 4 171 0.6020600 2.2329961 2.114781 0.993944
#> 3 3 136 0.4771213 2.1335389 1.990599 0.993944
#> 4 2 136 0.3010300 2.1335389 1.815574 0.993944
#> 5 1 31 0.0000000 1.4913617 1.516367 0.993944
#> 6 4 225 0.6020600 2.3521825 2.114781 0.993944
#> 7 6 350 0.7781513 2.5440680 2.289806 0.993944
#> 8 1 58 0.0000000 1.7634280 1.516367 0.993944
#> 9 1 58 0.0000000 1.7634280 1.516367 0.993944
#> 10 4 180 0.6020600 2.2552725 2.114781 0.993944
#> 11 4 141 0.6020600 2.1492191 2.114781 0.993944
#> 12 8 302 0.9030900 2.4800069 2.413988 0.993944
#> 13 1 23 0.0000000 1.3617278 1.516367 0.993944
#> 14 1 58 0.0000000 1.7634280 1.516367 0.993944
#> 15 4 187 0.6020600 2.2718416 2.114781 0.993944
#> 16 4 170 0.6020600 2.2304489 2.114781 0.993944
#> 17 4 131 0.6020600 2.1172713 2.114781 0.993944
#> 18 17 443 1.2304489 2.6464037 2.739364 0.993944
#> 19 19 643 1.2787536 2.8082110 2.787377 0.993944
#> 20 14 403 1.1461280 2.6053050 2.655554 0.993944
#> 21 15 442 1.1760913 2.6454223 2.685336 0.993944
#> 22 15 434 1.1760913 2.6374897 2.685336 0.993944
#> 23 17 439 1.2304489 2.6424645 2.739364 0.993944
#> 24 11 302 1.0413927 2.4800069 2.551453 0.993944
#> 25 7 261 0.8450980 2.4166405 2.356347 0.993944
#> 26 1 65 0.0000000 1.8129134 1.516367 0.993944
#> 27 3 105 0.4771213 2.0211893 1.990599 0.993944
#> 28 1 23 0.0000000 1.3617278 1.516367 0.993944
#> 29 1 52 0.0000000 1.7160033 1.516367 0.993944
#> 30 18 409 1.2552725 2.6117233 2.764038 0.993944
#> 31 6 182 0.7781513 2.2600714 2.289806 0.993944
#> 32 2 35 0.3010300 1.5440680 1.815574 0.993944
#> 33 2 51 0.3010300 1.7075702 1.815574 0.993944
#> 34 1 41 0.0000000 1.6127839 1.516367 0.993944
#> 35 1 3 0.0000000 0.4771213 1.516367 0.993944
#> 36 2 48 0.3010300 1.6812412 1.815574 0.993944
#> 37 11 243 1.0413927 2.3856063 2.551453 0.993944
#> 38 17 436 1.2304489 2.6394865 2.739364 0.993944
#> 39 9 226 0.9542425 2.3541084 2.464831 0.993944
#> 40 4 136 0.6020600 2.1335389 2.114781 0.993944
#> 41 2 59 0.3010300 1.7708520 1.815574 0.993944
#> 42 1 18 0.0000000 1.2552725 1.516367 0.993944
#> 43 4 167 0.6020600 2.2227165 2.114781 0.993944
#> 44 14 373 1.1461280 2.5717088 2.655554 0.993944
#> 45 10 280 1.0000000 2.4471580 2.510311 0.993944
#> 46 4 125 0.6020600 2.0969100 2.114781 0.993944
#> 47 9 263 0.9542425 2.4199557 2.464831 0.993944
#> 48 13 322 1.1139434 2.5078559 2.623564 0.993944
#> 49 11 315 1.0413927 2.4983106 2.551453 0.993944
#> 50 7 143 0.8450980 2.1553360 2.356347 0.993944
#> 51 1 58 0.0000000 1.7634280 1.516367 0.993944
#> 52 2 68 0.3010300 1.8325089 1.815574 0.993944
#> 53 3 89 0.4771213 1.9493900 1.990599 0.993944
#> 54 2 19 0.3010300 1.2787536 1.815574 0.993944
#> 55 2 24 0.3010300 1.3802112 1.815574 0.993944
#> 56 14 358 1.1461280 2.5538830 2.655554 0.993944
#> 57 4 109 0.6020600 2.0374265 2.114781 0.993944
#> 58 1 58 0.0000000 1.7634280 1.516367 0.993944
#> 59 1 10 0.0000000 1.0000000 1.516367 0.993944
#> 60 5 201 0.6989700 2.3031961 2.211104 0.993944
#> 61 9 254 0.9542425 2.4048337 2.464831 0.993944
#> 62 11 231 1.0413927 2.3636120 2.551453 0.993944
#> 63 14 463 1.1461280 2.6655810 2.655554 0.993944
#> 64 11 463 1.0413927 2.6655810 2.551453 0.993944
#> 65 12 404 1.0791812 2.6063814 2.589013 0.993944
#> 66 4 170 0.6020600 2.2304489 2.114781 0.993944
#> 67 5 243 0.6989700 2.3856063 2.211104 0.993944
#> 68 6 275 0.7781513 2.4393327 2.289806 0.993944
#> 69 3 145 0.4771213 2.1613680 1.990599 0.993944
#> 70 16 700 1.2041200 2.8450980 2.713195 0.993944
#> 71 11 461 1.0413927 2.6637009 2.551453 0.993944
#> 72 7 342 0.8450980 2.5340261 2.356347 0.993944
#> 73 4 234 0.6020600 2.3692159 2.114781 0.993944
#> 74 4 320 0.6020600 2.5051500 2.114781 0.993944