plotly-master/data-raw/mic.R

In franciscorichter/dmea: Diversification and Macro-Evolution Analysis

r = c(1, 0.995, 0.978, 0.951, 0.914, 0.866, 0.809, 0.743, 0.669, 0.588, 0.5, 0.407, 0.309, 0.208, 0.105, 0, 0.105, 0.208, 0.309, 0.407, 0.5, 0.588, 0.669, 0.743, 0.809, 0.866, 0.914, 0.951, 0.978, 0.995, 1, 0.995, 0.978, 0.951, 0.914, 0.866, 0.809, 0.743, 0.669, 0.588, 0.5, 0.407, 0.309, 0.208, 0.105, 0, 0.105, 0.208, 0.309, 0.407, 0.5, 0.588, 0.669, 0.743, 0.809, 0.866, 0.914, 0.951, 0.978, 0.995, 1)
t = c(0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96, 102, 108, 114, 120, 126, 132, 138, 144, 150, 156, 162, 168, 174, 180, 186, 192, 198, 204, 210, 216, 222, 228, 234, 240, 246, 252, 258, 264, 270, 276, 282, 288, 294, 300, 306, 312, 318, 324, 330, 336, 342, 348, 354, 360)
r2 = c(1, 0.997, 0.989, 0.976, 0.957, 0.933, 0.905, 0.872, 0.835, 0.794, 0.75, 0.703, 0.655, 0.604, 0.552, 0.5, 0.448, 0.396, 0.345, 0.297, 0.25, 0.206, 0.165, 0.128, 0.095, 0.067, 0.043, 0.024, 0.011, 0.003, 0, 0.003, 0.011, 0.024, 0.043, 0.067, 0.095, 0.128, 0.165, 0.206, 0.25, 0.297, 0.345, 0.396, 0.448, 0.5, 0.552, 0.604, 0.655, 0.703, 0.75, 0.794, 0.835, 0.872, 0.905, 0.933, 0.957, 0.976, 0.989, 0.997, 1)
t2 = c(0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96, 102, 108, 114, 120, 126, 132, 138, 144, 150, 156, 162, 168, 174, 180, 186, 192, 198, 204, 210, 216, 222, 228, 234, 240, 246, 252, 258, 264, 270, 276, 282, 288, 294, 300, 306, 312, 318, 324, 330, 336, 342, 348, 354, 360)
r3 = c(1, 0.996, 0.984, 0.963, 0.935, 0.9, 0.857, 0.807, 0.752, 0.691, 0.625, 0.555, 0.482, 0.406, 0.328, 0.25, 0.172, 0.094, 0.018, 0.055, 0.125, 0.191, 0.252, 0.307, 0.357, 0.4, 0.435, 0.463, 0.484, 0.496, 0.5, 0.496, 0.484, 0.463, 0.435, 0.4, 0.357, 0.307, 0.252, 0.191, 0.125, 0.055, 0.018, 0.094, 0.172, 0.25, 0.328, 0.406, 0.482, 0.555, 0.625, 0.691, 0.752, 0.807, 0.857, 0.9, 0.935, 0.963, 0.984, 0.996, 1)
t3 = c(0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96, 102, 108, 114, 120, 126, 132, 138, 144, 150, 156, 162, 168, 174, 180, 186, 192, 198, 204, 210, 216, 222, 228, 234, 240, 246, 252, 258, 264, 270, 276, 282, 288, 294, 300, 306, 312, 318, 324, 330, 336, 342, 348, 354, 360)
r4 = c(1, 0.998, 0.993, 0.985, 0.974, 0.96, 0.943, 0.923, 0.901, 0.876, 0.85, 0.822, 0.793, 0.762, 0.731, 0.7, 0.669, 0.638, 0.607, 0.578, 0.55, 0.524, 0.499, 0.477, 0.457, 0.44, 0.426, 0.415, 0.407, 0.402, 0.4, 0.402, 0.407, 0.415, 0.426, 0.44, 0.457, 0.477, 0.499, 0.524, 0.55, 0.578, 0.607, 0.638, 0.669, 0.7, 0.731, 0.762, 0.793, 0.822, 0.85, 0.876, 0.901, 0.923, 0.943, 0.96, 0.974, 0.985, 0.993, 0.998, 1)
t4 = c(0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96, 102, 108, 114, 120, 126, 132, 138, 144, 150, 156, 162, 168, 174, 180, 186, 192, 198, 204, 210, 216, 222, 228, 234, 240, 246, 252, 258, 264, 270, 276, 282, 288, 294, 300, 306, 312, 318, 324, 330, 336, 342, 348, 354, 360)
r5 = c(1, 0.997, 0.986, 0.969, 0.946, 0.916, 0.88, 0.838, 0.792, 0.74, 0.685, 0.626, 0.565, 0.501, 0.436, 0.37, 0.304, 0.239, 0.175, 0.114, 0.055, 0, 0.052, 0.098, 0.14, 0.176, 0.206, 0.229, 0.246, 0.257, 0.26, 0.257, 0.246, 0.229, 0.206, 0.176, 0.14, 0.098, 0.052, 0, 0.055, 0.114, 0.175, 0.239, 0.304, 0.37, 0.436, 0.501, 0.565, 0.626, 0.685, 0.74, 0.792, 0.838, 0.88, 0.916, 0.946, 0.969, 0.986, 0.997, 1)
t5 = c(0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96, 102, 108, 114, 120, 126, 132, 138, 144, 150, 156, 162, 168, 174, 180, 186, 192, 198, 204, 210, 216, 222, 228, 234, 240, 246, 252, 258, 264, 270, 276, 282, 288, 294, 300, 306, 312, 318, 324, 330, 336, 342, 348, 354, 360)
nms <- c(
  rep("Figure8", length(r)), 
  rep("Cardioid", length(r2)), 
  rep("Hypercardioid", length(r3)), 
  rep("Subcardioid", length(r4)), 
  rep("Supercardioid", length(r5))
)
mic <- data.frame(
  r = c(r, r2, r3, r4, r5),
  t = c(t, t2, t3, t4, t5),
  nms
)
save(mic, file = "data/mic.rda")