PRE_FATE.speciesDistanceCombine: Combine several dissimilarity distance matrices

In leca-dev/RFate: FATE with R

Combine several dissimilarity distance matrices

Description

This script is designed to create a distance matrix between species, combining several dissimilarity distance matrices.

Usage

PRE_FATE.speciesDistanceCombine(
  list.mat.dist,
  opt.min.noMat = length(list.mat.dist),
  opt.normal = TRUE,
  opt.weights = NULL
)

Arguments

list.mat.dist	a list of matrices containing dissimilarity distance values between each pair of species.
opt.min.noMat	(optional) default length(list.mat.dist). An integer corresponding to the minimal number of distance matrices for which each species should have values
opt.normal	(optional) default TRUE. If TRUE, all given distance matrices will be normalized (see Details)
opt.weights	(optional) default NULL. A vector of double (between 0 and 1) corresponding to the weights for each distance matrix provided in list.mat.dist. They must sum up to 1.

Details

This function allows to obtain a distance matrix between species, based on several dissimilarity distance matrices combined according to the following formula :

\text{mat.DIST} = \Sigma (\text{wei.i} * \text{mat.DIST}_{i})

If opt.normal = TRUE, two normalization steps are applied to each distance matrix before combining them :

1. a non-paranormal (npn) transformation (huge.npn function) to obtain Gaussian distributions for all dissimilarity matrices used

2. a range normalization to bring the values back between 0 and 1 :

   \text{mat.DIST}_{i} = \frac{\text{mat.DIST}_{i} - min(\text{mat.DIST}_{i})}{max(\text{mat.DIST}_{i}) - min(\text{mat.DIST}_{i})}

Value

A matrix containing the weighted (or not) combination of the different transformed (or not) distance matrices given.

The information for the combination of all distances is written in ‘PRE_FATE_DOMINANT_speciesDistance.csv’ file.

Author(s)

Maya Guéguen

See Also

huge.npn

Examples

## Load example data
Champsaur_PFG = .loadData('Champsaur_PFG', 'RData')

## Species traits
tab.traits = Champsaur_PFG$sp.traits
tab.traits = tab.traits[, c('species', 'GROUP', 'MATURITY', 'LONGEVITY'
                            , 'HEIGHT', 'DISPERSAL', 'LIGHT', 'NITROGEN')]
str(tab.traits)

## Species niche overlap (dissimilarity distances)
DIST.overlap = Champsaur_PFG$mat.overlap
DIST.overlap[1:5, 1:5]

## Species functional distances (dissimilarity)
DIST.traits = PRE_FATE.speciesDistanceTraits(mat.traits = tab.traits
                                             , opt.maxPercent.NA = 0.05
                                             , opt.maxPercent.similarSpecies = 0.3
                                             , opt.min.sd = 0.3)
DIST.traits$Chamaephyte[1:5, 1:5]

## Combine distances ---------------------------------------------------------
list.DIST = list(DIST.overlap, DIST.traits$Chamaephyte)
sp.DIST.n = PRE_FATE.speciesDistanceCombine(list.mat.dist = list.DIST
                                            , opt.weights = c(0.2, 0.8))
sp.DIST.un = PRE_FATE.speciesDistanceCombine(list.mat.dist = list.DIST
                                             , opt.norm = FALSE
                                             , opt.weights = c(0.2, 0.8))
str(sp.DIST.n)



## Not run: 
require(corrplot)
list.DIST = list(DIST.overlap, DIST.traits$Chamaephyte
                 , sp.DIST.un, sp.DIST.n)
names(list.DIST) = c('overlap', 'traits', 'un-normed', 'normed')

par(mfrow = c(2, 2))
for (li in 1:length(list.DIST))
{
  tmp = list.DIST[[li]]
  tmp = tmp[colnames(sp.DIST.n), colnames(sp.DIST.n)]
  corrplot(tmp, method = 'shade'
           , type = 'lower', cl.lim = c(0, 1)
           , is.corr = FALSE, title = names(list.DIST)[li])
}

require(foreach); require(ggplot2); require(ggdendro)
hc = hclust(as.dist(sp.DIST.n))
pp = ggdendrogram(hc, rotate = TRUE) +
  labs(title = 'Hierarchical clustering based on species distances')
plot(pp)

## End(Not run)

leca-dev/RFate documentation built on Sept. 19, 2024, 6:09 a.m.