select_pcoa_axes: Select optimal number of pcoa axes

In alexology/biomonitoR: biomonitoring indices and metrics

Select optimal number of pcoa axes

Description

\Sexpr[results=rd, stage=render]{ lifecycle::badge("experimental") }

Select the number of the axis for the calculation of functional richness.

Usage

select_pcoa_axes(x, method = "legendre", tresh = NULL, nbdim = 15)

Arguments

x	A distance matrix.
method	Quality measure of the functional space. Can be cor, legendre and maire.
tresh	the treshold for selecting the optimal number of axes. By default is 0.7 for cor and legendre and 0.01 for maire.
nbdim	The maximum number of dimension when the option is set to maire. Default to 15.

Details

This function provides some information to select the number of axes for the calculation of functional richness. As implemented in biomonitoR, functional richness requires a pcoa to be computed from the trait distances. For calculating functional richness a subset of the number of axes originating from the pcoa is kept. The choiche of the number of axis can affect the calculation and some information about how much the subset represents the original distance matrix is desirable. To this purpose, 3 measures are provide. The cor option represents the correlation between the distance of the points in the reduced space and the original distance matrix. The distance of points in the reduced space is calculated with the dist() function from the stats package on the coordinates of the selected number of axis. A p-value based on a mantel statistic is also provided. The legendre option calculates the R^2 like ratio as described by Legendre and Legendre (2008) and implemented in the FD package. The maire option calculate the quality of the functional space according to Maire et al. (2015). The function selectPcoaAxis also provides information about the euclidean property of the trait distance. The lack of this property can lead to biases and some solutions are proposed here and described in Legendre and Legendre (2008). Please take a look also to the help of the FD function dbFD for further information.

References

Maire, E., Grenouillet, G., Brosse, S., & Villeger, S. (2015). How many dimensions are needed to accurately assess functional diversity? A pragmatic approach for assessing the quality of functional spaces. Global Ecology and Biogeography, 24(6), 728-740.

Legendre, P. and L. Legendre (1998) Numerical Ecology. 2nd English edition. Amsterdam: Elsevier.

See Also

aggregate_taxa

Examples

library(ade4)

data_bio <- as_biomonitor(macro_ex)
data_agr <- aggregate_taxa(data_bio)
data_ts <- assign_traits(data_agr)

# averaging
data_ts_av <- average_traits(data_ts)

col_blocks <- c(8, 7, 3, 9, 4, 3, 6, 2, 5, 3, 9, 8, 8, 5, 7, 5, 4, 4, 2, 3, 8)

rownames(data_ts_av) <- data_ts_av$Taxa
traits_prep <- prep.fuzzy(data_ts_av[, -1], col.blocks = col_blocks)

traits_dist <- ktab.list.df(list(traits_prep))
traits_dist <- dist.ktab(traits_dist, type = "F")

select_pcoa_axes(traits_dist, method = "cor", tresh = 0.7)
select_pcoa_axes(traits_dist, method = "legendre", tresh = 0.7)
select_pcoa_axes(traits_dist, method = "maire", tresh = 0.01)

alexology/biomonitoR documentation built on Oct. 10, 2024, 12:02 a.m.