plot.anthropmmd_boot {AnthropMMD} | R Documentation |
Display a multidimensional scaling (MDS) plot using Fidalgo et al's (2022) bootstrap method for MMD
Description
This function plots a 2D MDS to represent the MMD dissimilarities
among the groups compared, after a bootstrap resampling performed with
mmd_boot
.
Usage
## S3 method for class 'anthropmmd_boot'
plot(x, method = c("classical", "interval", "ratio", "ordinal"),
level = 0.95, pch = 16, gof = FALSE, xlab = NA, ylab = NA,
main = "MDS plot of original and bootstrapped samples", ...)
Arguments
x |
An object of class |
.
method |
Algorithm used for MDS computation; see |
level |
Numeric value between 0 and 1, confidence level for the contour lines displayed after the kernel density estimate. |
pch |
Passed to |
gof |
Boolean; whether to display goodness of fit statistic on the plot. |
xlab |
Passed to |
ylab |
Passed to |
main |
Passed to |
... |
Other arguments possibly passed to |
Details
In the current implementation, to stick to Fidalgo et al.'s (2022)
protocol, this function does not provide as much freedom as
plot.anthropmmd_result
as concenrs MDS parameters and
other analysis options.
Value
This function returns no value by itself, and only plots a MDS in a new device.
Author(s)
Frédéric Santos, frederic.santos@u-bordeaux.fr
References
D. Fidalgo, V. Vesolowski and M. Hubbe (2022). Biological affinities of Brazilian pre-colonial coastal communities explored through boostrapped biodistances of dental non-metric traits. Journal of Archaeological Science 138, 105545.
See Also
start_mmd
, stats::cmdscale
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
)
## MDS plot for bootstrapped samples:
plot(
x = resboot,
method = "interval", # algorithm used for MDS computation
level = 0.95 # confidence level for the contour lines
)
## End(Not run)