Comments (1)
I've tested the robustness of geometry::convhulln()
to the number of traits using the following code:
tr_df = matrix(rnorm(50*25), ncol = 25)
rownames(tr_df) = paste0("sp", 1:50)
colnames(tr_df) = paste0("tr", 1:25)
comp_chull = lapply(1:ncol(tr_df), function(x) {
sub_tr = tr_df[, 1:x, drop = FALSE]
tryCatch({
geometry::convhulln(sub_tr, options = "FA")
data.frame(n_tr = x, computable = TRUE)
},
error = function(cond) {
data.frame(n_tr = x, computable = FALSE)
})
})
Which gives the following results:
n_tr computable
1 1 FALSE
2 2 TRUE
3 3 TRUE
4 4 TRUE
5 5 TRUE
6 6 TRUE
7 7 TRUE
8 8 TRUE
9 9 TRUE
10 10 TRUE
11 11 TRUE
12 12 TRUE
13 13 TRUE
14 14 TRUE
15 15 TRUE
16 16 TRUE
17 17 FALSE
18 18 FALSE
19 19 FALSE
20 20 FALSE
21 21 FALSE
22 22 FALSE
23 23 FALSE
24 24 FALSE
25 25 FALSE
As other empirical test found no clear limits in the number of species (i.e. rows) it seems there is a clear limit between 16 traits and 17 traits.
from fundiversity.
Related Issues (20)
- Hex logo
- Managing error from qhull transferred to geometry::convhulln() in fd_chull()
- Error when site contains no species in fd_fdiv() and fd_feve() HOT 2
- Errors when using non-quantitative traits in fd_fric()
- Release fundiversity 0.2.1
- Specific error obtained with Qhull HOT 3
- Investigate RCGAL as a replacement of geometry HOT 4
- Simplify CI matrix by using `_R_CHECK_DEPENDS_ONLY_TESTS_` HOT 2
- fd_fdiv() returns `NaN` on datasets without rownames
- FRic returns NA without warnings nor messages HOT 1
- Add tip in error message because of non-continuous traits HOT 1
- Renaming the vignettes to order them logically? HOT 2
- Change title of package in DESCRIPTION as it doesn't only computes "Alpha functional diversity indices"
- fd_fdis() bugs when site-species object is a data.frame() expects a matrix. HOT 2
- Using future and memoise together HOT 2
- Add link to published paper
- Should we write a vignette for non-continuous trait data? HOT 1
- fd_dis gives error if site x species matrix does not have row names HOT 4
- Be more specific in the help with memoization HOT 2
- Have a worked through example with individual level dataset
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from fundiversity.