mmd_boot {AnthropMMD}  R Documentation 
Implementation of Fidalgo et al.'s (2022) method of bootstrap for the Mean Measure of Divergence
Description
Compute a matrix of MMD dissimilarities among bootstrapped samples of the original groups. The input data must be a “raw binary dataset”.
Usage
mmd_boot(data, angular = c("Anscombe", "Freeman"), B = 100, ...)
Arguments
data 
A “raw binary dataset”, as defined in the man page
of 
angular 
Choice of a formula for angular transformation: either Anscombe or FreemanTukey transformation. 
B 
Numeric value: number of bootstrap samples. 
... 
Arguments for traits selection, passed to

Details
This function sticks very close to Fidalgo et al's (2022) implementation. In particular, no correction for small sample sizes is applied in the MMD formula; see Fidalgo et al's (2021) for the rationale.
Note that only a “raw binary dataset” is allowed as input, since the resampling cannot be performed properly from a table of counts and frequencies.
To get a MDS plot of the dissimilarity matrix obtained with this
function, see plot.anthropmmd_boot
.
Value
A symmetrical dissimilarity matrix of MMD values among original groups
and bootstrapped samples. This matrix is an R object of class
anthropmmd_boot
.
Author(s)
Frédéric Santos, frederic.santos@ubordeaux.fr
References
D. Fidalgo, M. Hubbe and V. Vesolowski (2021). Population history of Brazilian south and southeast shellmound builders inferred through dental morphology. American Journal of Physical Anthropology 176(2), 192207.
D. Fidalgo, V. Vesolowski and M. Hubbe (2022). Biological affinities of Brazilian precolonial coastal communities explored through boostrapped biodistances of dental nonmetric traits. Journal of Archaeological Science 138, 105545.
See Also
Examples
## Not run:
## Load and visualize a raw binary dataset:
data(toyMMD)
head(toyMMD)
## Compute MMD among bootstrapped samples:
resboot < mmd_boot(
data = toyMMD,
B = 50, # number of bootstrap samples
angular = "Anscombe",
strategy = "excludeQNPT", # strategy for trait selection
k = 10 # minimal number of observations required per trait
)
## View part of MMD matrix among bootstrapped samples:
dim(resboot)
print(resboot[1:15, 1:15])
## End(Not run)