Discretizes two angular variables into sectors and estimates joint or conditional mass flows between sectors.
Usage
toroidal_flow_data(
theta,
phi,
n_sectors = 32,
min_mass = 0.002,
mass_type = c("joint", "conditional")
)See also
Other toroidal dependence helpers:
estimate_toroidal_density(),
toroidal_ridge_data(),
toroidal_topography_data()
Examples
dat <- simulate_toroidal(n = 80, seed = 1)
toroidal_flow_data(dat$theta, dat$phi, n_sectors = 12)
#> theta_sector phi_sector count joint_mass conditional_mass mass
#> 1 5 6 10 0.1250 0.83333333 0.1250
#> 2 8 8 7 0.0875 1.00000000 0.0875
#> 3 6 7 5 0.0625 0.55555556 0.0625
#> 4 10 7 4 0.0500 0.44444444 0.0500
#> 5 10 8 4 0.0500 0.44444444 0.0500
#> 6 11 9 4 0.0500 0.44444444 0.0500
#> 7 1 2 3 0.0375 0.75000000 0.0375
#> 8 2 3 3 0.0375 0.75000000 0.0375
#> 9 3 4 3 0.0375 0.50000000 0.0375
#> 10 6 8 3 0.0375 0.33333333 0.0375
#> 11 11 8 3 0.0375 0.33333333 0.0375
#> 12 4 4 2 0.0250 0.33333333 0.0250
#> 13 3 5 2 0.0250 0.33333333 0.0250
#> 14 4 5 2 0.0250 0.33333333 0.0250
#> 15 4 6 2 0.0250 0.33333333 0.0250
#> 16 7 7 2 0.0250 0.50000000 0.0250
#> 17 9 7 2 0.0250 0.33333333 0.0250
#> 18 7 8 2 0.0250 0.50000000 0.0250
#> 19 9 8 2 0.0250 0.33333333 0.0250
#> 20 12 11 2 0.0250 0.50000000 0.0250
#> 21 2 2 1 0.0125 0.25000000 0.0125
#> 22 3 3 1 0.0125 0.16666667 0.0125
#> 23 5 5 1 0.0125 0.08333333 0.0125
#> 24 9 6 1 0.0125 0.16666667 0.0125
#> 25 10 6 1 0.0125 0.11111111 0.0125
#> 26 5 7 1 0.0125 0.08333333 0.0125
#> 27 11 7 1 0.0125 0.11111111 0.0125
#> 28 6 9 1 0.0125 0.11111111 0.0125
#> 29 9 9 1 0.0125 0.16666667 0.0125
#> 30 11 10 1 0.0125 0.11111111 0.0125
#> 31 12 10 1 0.0125 0.25000000 0.0125
#> 32 1 12 1 0.0125 0.25000000 0.0125
#> 33 12 12 1 0.0125 0.25000000 0.0125
#> theta_center phi_center theta_plot phi_plot source_y target_y
#> 1 2.3561945 2.8797933 2.3561945 2.8797933 -3.141593 2.8797933
#> 2 3.9269908 3.9269908 -2.3561945 -2.3561945 -3.141593 -2.3561945
#> 3 2.8797933 3.4033920 2.8797933 -2.8797933 -3.141593 -2.8797933
#> 4 4.9741884 3.4033920 -1.3089969 -2.8797933 -3.141593 -2.8797933
#> 5 4.9741884 3.9269908 -1.3089969 -2.3561945 -3.141593 -2.3561945
#> 6 5.4977871 4.4505896 -0.7853982 -1.8325957 -3.141593 -1.8325957
#> 7 0.2617994 0.7853982 0.2617994 0.7853982 -3.141593 0.7853982
#> 8 0.7853982 1.3089969 0.7853982 1.3089969 -3.141593 1.3089969
#> 9 1.3089969 1.8325957 1.3089969 1.8325957 -3.141593 1.8325957
#> 10 2.8797933 3.9269908 2.8797933 -2.3561945 -3.141593 -2.3561945
#> 11 5.4977871 3.9269908 -0.7853982 -2.3561945 -3.141593 -2.3561945
#> 12 1.8325957 1.8325957 1.8325957 1.8325957 -3.141593 1.8325957
#> 13 1.3089969 2.3561945 1.3089969 2.3561945 -3.141593 2.3561945
#> 14 1.8325957 2.3561945 1.8325957 2.3561945 -3.141593 2.3561945
#> 15 1.8325957 2.8797933 1.8325957 2.8797933 -3.141593 2.8797933
#> 16 3.4033920 3.4033920 -2.8797933 -2.8797933 -3.141593 -2.8797933
#> 17 4.4505896 3.4033920 -1.8325957 -2.8797933 -3.141593 -2.8797933
#> 18 3.4033920 3.9269908 -2.8797933 -2.3561945 -3.141593 -2.3561945
#> 19 4.4505896 3.9269908 -1.8325957 -2.3561945 -3.141593 -2.3561945
#> 20 6.0213859 5.4977871 -0.2617994 -0.7853982 -3.141593 -0.7853982
#> 21 0.7853982 0.7853982 0.7853982 0.7853982 -3.141593 0.7853982
#> 22 1.3089969 1.3089969 1.3089969 1.3089969 -3.141593 1.3089969
#> 23 2.3561945 2.3561945 2.3561945 2.3561945 -3.141593 2.3561945
#> 24 4.4505896 2.8797933 -1.8325957 2.8797933 -3.141593 2.8797933
#> 25 4.9741884 2.8797933 -1.3089969 2.8797933 -3.141593 2.8797933
#> 26 2.3561945 3.4033920 2.3561945 -2.8797933 -3.141593 -2.8797933
#> 27 5.4977871 3.4033920 -0.7853982 -2.8797933 -3.141593 -2.8797933
#> 28 2.8797933 4.4505896 2.8797933 -1.8325957 -3.141593 -1.8325957
#> 29 4.4505896 4.4505896 -1.8325957 -1.8325957 -3.141593 -1.8325957
#> 30 5.4977871 4.9741884 -0.7853982 -1.3089969 -3.141593 -1.3089969
#> 31 6.0213859 4.9741884 -0.2617994 -1.3089969 -3.141593 -1.3089969
#> 32 0.2617994 6.0213859 0.2617994 -0.2617994 -3.141593 -0.2617994
#> 33 6.0213859 6.0213859 -0.2617994 -0.2617994 -3.141593 -0.2617994
#> x0 y0 x1 y1
#> 1 -0.7071068 0.7071068 -0.5602370 0.1501150
#> 2 -0.7071068 -0.7071068 -0.4101219 -0.4101219
#> 3 -0.9659258 0.2588190 -0.5602370 -0.1501150
#> 4 0.2588190 -0.9659258 -0.5602370 -0.1501150
#> 5 0.2588190 -0.9659258 -0.4101219 -0.4101219
#> 6 0.7071068 -0.7071068 -0.1501150 -0.5602370
#> 7 0.9659258 0.2588190 0.4101219 0.4101219
#> 8 0.7071068 0.7071068 0.1501150 0.5602370
#> 9 0.2588190 0.9659258 -0.1501150 0.5602370
#> 10 -0.9659258 0.2588190 -0.4101219 -0.4101219
#> 11 0.7071068 -0.7071068 -0.4101219 -0.4101219
#> 12 -0.2588190 0.9659258 -0.1501150 0.5602370
#> 13 0.2588190 0.9659258 -0.4101219 0.4101219
#> 14 -0.2588190 0.9659258 -0.4101219 0.4101219
#> 15 -0.2588190 0.9659258 -0.5602370 0.1501150
#> 16 -0.9659258 -0.2588190 -0.5602370 -0.1501150
#> 17 -0.2588190 -0.9659258 -0.5602370 -0.1501150
#> 18 -0.9659258 -0.2588190 -0.4101219 -0.4101219
#> 19 -0.2588190 -0.9659258 -0.4101219 -0.4101219
#> 20 0.9659258 -0.2588190 0.4101219 -0.4101219
#> 21 0.7071068 0.7071068 0.4101219 0.4101219
#> 22 0.2588190 0.9659258 0.1501150 0.5602370
#> 23 -0.7071068 0.7071068 -0.4101219 0.4101219
#> 24 -0.2588190 -0.9659258 -0.5602370 0.1501150
#> 25 0.2588190 -0.9659258 -0.5602370 0.1501150
#> 26 -0.7071068 0.7071068 -0.5602370 -0.1501150
#> 27 0.7071068 -0.7071068 -0.5602370 -0.1501150
#> 28 -0.9659258 0.2588190 -0.1501150 -0.5602370
#> 29 -0.2588190 -0.9659258 -0.1501150 -0.5602370
#> 30 0.7071068 -0.7071068 0.1501150 -0.5602370
#> 31 0.9659258 -0.2588190 0.1501150 -0.5602370
#> 32 0.9659258 0.2588190 0.5602370 -0.1501150
#> 33 0.9659258 -0.2588190 0.5602370 -0.1501150