This vignette contains a number of examples on how to use
clugenr
in 3D. Examples require the following setup
code:
library(clugenr) # The clugenr library
options(rgl.useNULL = TRUE) # Create RGL plots in systems without displays (CI)
library(rgl)
setupKnitr(autoprint = TRUE) # Render RGL plots directly on generated page
# Load helper functions for plotting examples
source("plot_examples_3d.R", local = knitr::knit_global())
# Keep examples reproducible in newer R versions
RNGversion("3.6.0")
The 3D examples were plotted with the plot_examples_3d()
function available here.
direction
parameter<- 123 seed
<- clugen(3, 4, 500, c(1, 0, 0), 0, c(10, 10, 10), 15, 1.5, 0.5, seed = seed)
e40 <- clugen(3, 4, 500, c(1, 1, 1), 0, c(10, 10, 10), 15, 1.5, 0.5, seed = seed)
e41 <- clugen(3, 4, 500, c(0, 0, 1), 0, c(10, 10, 10), 15, 1.5, 0.5, seed = seed) e42
plot_examples_3d(list(e = e40, t = "e40: direction = [1, 0, 0]"),
list(e = e41, t = "e41: direction = [1, 1, 1]"),
list(e = e42, t = "e42: direction = [0, 0, 1]"))
angle_disp
parameter and using a custom
angle_deltas_fn
function<- 123 seed
# Custom angle_deltas function: arbitrarily rotate some clusters by 90 degrees
<- function(nclu, astd) sample(c(0, pi / 2), nclu, replace = TRUE) angdel_90
<- clugen(3, 6, 1000, c(1, 0, 0), 0, c(10, 10, 10), 15, 1.5, 0.5, seed = seed)
e43 <- clugen(3, 6, 1000, c(1, 0, 0), pi / 8, c(10, 10, 10), 15, 1.5, 0.5, seed = seed)
e44 <- clugen(3, 6, 1000, c(1, 0, 0), 0, c(10, 10, 10), 15, 1.5, 0.5, seed = seed,
e45 angle_deltas_fn = angdel_90)
plot_examples_3d(list(e = e43, t = "e43: angle_disp = 0"),
list(e = e44, t = "e44: angle_disp = π / 8"),
list(e = e45, t = "e45: custom angle_deltas function"))
<- 123 seed
llength
parameter<- clugen(3, 5, 800, c(1, 0, 0), pi / 10, c(10, 10, 10), 0, 0, 0.5,
e46 seed = seed, point_dist_fn = "n")
<- clugen(3, 5, 800, c(1, 0, 0), pi / 10, c(10, 10, 10), 10, 0, 0.5,
e47 seed = seed, point_dist_fn = "n")
<- clugen(3, 5, 800, c(1, 0, 0), pi / 10, c(10, 10, 10), 30, 0, 0.5,
e48 seed = seed, point_dist_fn = "n")
plot_examples_3d(list(e = e46, t = "e46: llength = 0"),
list(e = e47, t = "e47: llength = 10"),
list(e = e48, t = "e48: llength = 30"))
llength_disp
parameter and using a custom
llengths_fn
function# Custom llengths function: line lengths tend to grow for each new cluster
<- function(nclu, llen, llenstd) {
llen_grow * (0:(nclu - 1) + rnorm(nclu, sd = llenstd))
llen }
<- clugen(3, 5, 800, c(1, 0, 0), pi / 10, c(10, 10, 10), 15, 0.0, 0.5,
e49 seed = seed, point_dist_fn = "n")
<- clugen(3, 5, 800, c(1, 0, 0), pi / 10, c(10, 10, 10), 15, 10.0, 0.5,
e50 seed = seed, point_dist_fn = "n")
<- clugen(3, 5, 800, c(1, 0, 0), pi / 10, c(10, 10, 10), 10, 0.1, 0.5,
e51 seed = seed, point_dist_fn = "n", llengths_fn = llen_grow)
plot_examples_3d(list(e = e49, t = "e49: llength_disp = 0.0"),
list(e = e50, t = "e50: llength_disp = 10.0"),
list(e = e51, t = "e51: custom llengths function"))
cluster_sep
parameter<- 321 seed
<- clugen(3, 8, 1000, c(1, 1, 1), pi / 4, c(30, 10, 10), 25, 4, 3, seed = seed)
e52 <- clugen(3, 8, 1000, c(1, 1, 1), pi / 4, c(10, 30, 10), 25, 4, 3, seed = seed)
e53 <- clugen(3, 8, 1000, c(1, 1, 1), pi / 4, c(10, 10, 30), 25, 4, 3, seed = seed) e54
plot_examples_3d(list(e = e52, t = "e52: cluster_sep = [30, 10, 10]"),
list(e = e53, t = "e53: cluster_sep = [10, 30, 10]"),
list(e = e54, t = "e54: cluster_sep = [10, 10, 30]"))
cluster_offset
parameter and using a
custom clucenters_fn
function<- 321 seed
# Custom clucenters function: places clusters in a diagonal
<- function(nclu, csep, coff) {
centers_diag matrix(1, nrow = nclu, ncol = length(csep)) * (1:nclu * max(csep)) +
rep(coff, each = nclu)
}
<- clugen(3, 8, 1000, c(1, 1, 1), pi / 4, c(10, 10, 10), 12, 3, 2.5, seed = seed)
e55 <- clugen(3, 8, 1000, c(1, 1, 1), pi / 4, c(10, 10, 10), 12, 3, 2.5, seed = seed,
e56 cluster_offset = c(20, -20, 20))
<- clugen(3, 8, 1000, c(1, 1, 1), pi / 4, c(10, 10, 10), 12, 3, 2.5, seed = seed,
e57 cluster_offset = c(-50, -50, -50), clucenters_fn = centers_diag)
plot_examples_3d(list(e = e55, t = "e55: default"),
list(e = e56, t = "e56: cluster_offset = [20, -20, 20]"),
list(e = e57, t = "e57: custom clucenters function"))
<- 456 seed
proj_dist_fn = "norm"
<- clugen(3, 4, 1000, c(1, 0, 0), pi / 2, c(20, 20, 20), 13, 2, 0.0, seed = seed)
e58 <- clugen(3, 4, 1000, c(1, 0, 0), pi / 2, c(20, 20, 20), 13, 2, 1.0, seed = seed)
e59 <- clugen(3, 4, 1000, c(1, 0, 0), pi / 2, c(20, 20, 20), 13, 2, 3.0, seed = seed) e60
plot_examples_3d(list(e = e58, t = "e58: lateral_disp = 0"),
list(e = e59, t = "e59: lateral_disp = 1"),
list(e = e60, t = "e60: lateral_disp = 3"))
proj_dist_fn = "unif"
<- clugen(3, 4, 1000, c(1, 0, 0), pi / 2, c(20, 20, 20), 13, 2, 0.0, seed = seed,
e61 proj_dist_fn = "unif")
<- clugen(3, 4, 1000, c(1, 0, 0), pi / 2, c(20, 20, 20), 13, 2, 1.0, seed = seed,
e62 proj_dist_fn = "unif")
<- clugen(3, 4, 1000, c(1, 0, 0), pi / 2, c(20, 20, 20), 13, 2, 3.0, seed = seed,
e63 proj_dist_fn = "unif")
plot_examples_3d(list(e = e61, t = "e61: lateral_disp = 0"),
list(e = e62, t = "e62: lateral_disp = 1"),
list(e = e63, t = "e63: lateral_disp = 3"))
# Custom proj_dist_fn: point projections placed using the Beta distribution
<- function(len, n) len * rbeta(n, 0.1, 0.1) - len / 2 proj_beta
<- clugen(3, 4, 1000, c(1, 0, 0), pi / 2, c(20, 20, 20), 13, 2, 0.0, seed = seed,
e64 proj_dist_fn = proj_beta)
<- clugen(3, 4, 1000, c(1, 0, 0), pi / 2, c(20, 20, 20), 13, 2, 1.0, seed = seed,
e65 proj_dist_fn = proj_beta)
<- clugen(3, 4, 1000, c(1, 0, 0), pi / 2, c(20, 20, 20), 13, 2, 3.0, seed = seed,
e66 proj_dist_fn = proj_beta)
plot_examples_3d(list(e = e64, t = "e64: lateral_disp = 0"),
list(e = e65, t = "e65: lateral_disp = 1"),
list(e = e66, t = "e66: lateral_disp = 3"))
<- 12321 seed
# Custom proj_dist_fn: point projections placed using the Beta distribution
<- function(len, n) len * rbeta(n, 0.1, 0.1) - len / 2 proj_beta
point_dist_fn = "n-1"
<- clugen(3, 5, 1500, c(1, 0, 0), pi / 3, c(20, 20, 20), 22, 3, 2, seed = seed)
e67 <- clugen(3, 5, 1500, c(1, 0, 0), pi / 3, c(20, 20, 20), 22, 3, 2, seed = seed,
e68 proj_dist_fn = "unif")
<- clugen(3, 5, 1500, c(1, 0, 0), pi / 3, c(20, 20, 20), 22, 3, 2, seed = seed,
e69 proj_dist_fn = proj_beta)
plot_examples_3d(list(e = e67, t = "e67: proj_dist_fn = 'norm' (default)"),
list(e = e68, t = "e68: proj_dist_fn = 'unif'"),
list(e = e69, t = "e69: custom proj_dist_fn (Beta dist.)"))
point_dist_fn = "n"
<- clugen(3, 5, 1500, c(1, 0, 0), pi / 3, c(20, 20, 20), 22, 3, 2, seed = seed,
e70 point_dist_fn = "n")
<- clugen(3, 5, 1500, c(1, 0, 0), pi / 3, c(20, 20, 20), 22, 3, 2, seed = seed,
e71 point_dist_fn = "n", proj_dist_fn = "unif")
<- clugen(3, 5, 1500, c(1, 0, 0), pi / 3, c(20, 20, 20), 22, 3, 2, seed = seed,
e72 point_dist_fn = "n", proj_dist_fn = proj_beta)
plot_examples_3d(list(e = e70, t = "e70: proj_dist_fn = 'norm' (default)"),
list(e = e71, t = "e71: proj_dist_fn = 'unif'"),
list(e = e72, t = "e72: custom proj_dist_fn (Beta dist.)"))
# Custom point_dist_fn: final points placed using the Exponential distribution
<- function(projs, lat_std, len, clu_dir, clu_ctr) {
clupoints_n_1_exp <- function(npts, lstd) lstd * rexp(npts, rate = 2 / lstd)
dist_exp clupoints_n_1_template(projs, lat_std, clu_dir, dist_exp)
}
<- clugen(3, 5, 1500, c(1, 0, 0), pi / 3, c(20, 20, 20), 22, 3, 2, seed = seed,
e73 point_dist_fn = clupoints_n_1_exp)
<- clugen(3, 5, 1500, c(1, 0, 0), pi / 3, c(20, 20, 20), 22, 3, 2, seed = seed,
e74 point_dist_fn = clupoints_n_1_exp, proj_dist_fn = "unif")
<- clugen(3, 5, 1500, c(1, 0, 0), pi / 3, c(20, 20, 20), 22, 3, 2, seed = seed,
e75 point_dist_fn = clupoints_n_1_exp, proj_dist_fn = proj_beta)
plot_examples_3d(list(e = e73, t = "e73: proj_dist_fn = 'norm' (default)"),
list(e = e74, t = "e74: proj_dist_fn = 'unif'"),
list(e = e75, t = "e75: custom proj_dist_fn (Beta dist.)"))
<- 87 seed
# Custom clucenters_fn (all): yields fixed positions for the clusters
<- function(nclu, csep, coff) {
centers_fixed matrix(c(-csep[1], -csep[2], -csep[3], csep[1], -csep[2], -csep[3],
-csep[1], csep[2], csep[3], csep[1], csep[2], csep[3]),
nrow = nclu, byrow = TRUE)
}
# Custom clusizes_fn (e77): cluster sizes determined via the uniform distribution,
# no correction for total points
<- function(nclu, npts, ae) sample(2 * npts / nclu, nclu, replace = TRUE)
clusizes_unif
# Custom clusizes_fn (e78): clusters all have the same size, no correction for
# total points
<- function(nclu, npts, ae) npts %/% nclu * rep.int(1, nclu) clusizes_equal
<- clugen(3, 4, 1500, c(1, 1, 1), pi, c(20, 20, 20), 0, 0, 5, seed = seed,
e76 point_dist_fn = "n",
clucenters_fn = centers_fixed)
<- clugen(3, 4, 1500, c(1, 1, 1), pi, c(20, 20, 20), 0, 0, 5, seed = seed,
e77 clusizes_fn = clusizes_unif, point_dist_fn = "n",
clucenters_fn = centers_fixed)
<- clugen(3, 4, 1500, c(1, 1, 1), pi, c(20, 20, 20), 0, 0, 5, seed = seed,
e78 clusizes_fn = clusizes_equal, point_dist_fn = "n",
clucenters_fn = centers_fixed)
plot_examples_3d(list(e = e76, t = "e76: normal dist. (default)"),
list(e = e77, t = "e77: unif. dist. (custom)"),
list(e = e78, t = "e78: equal size (custom)"))