# Load Species*Traits dataframe:
data('fruits_traits', package = 'mFD')
# Load Assemblages*Species dataframe:
data('baskets_fruits_weights', package = 'mFD')
# Load Traits categories dataframe:
data('fruits_traits_cat', package = 'mFD')
# Compute functional distance
sp_dist_fruits <- mFD::funct.dist(sp_tr = fruits_traits,
tr_cat = fruits_traits_cat,
metric = "gower",
scale_euclid = "scale_center",
ordinal_var = "classic",
weight_type = "equal",
stop_if_NA = TRUE)
# Compute functional spaces quality to retrieve species coordinates matrix:
fspaces_quality_fruits <- mFD::quality.fspaces(
sp_dist = sp_dist_fruits,
maxdim_pcoa = 10,
deviation_weighting = 'absolute',
fdist_scaling = FALSE,
fdendro = 'average')
# Retrieve species coordinates matrix:
sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord
# Compute alpha diversity indices
alpha_fd_indices_fruits <- mFD::alpha.fd.multidim(
sp_faxes_coord = sp_faxes_coord_fruits[, c('PC1', 'PC2', 'PC3', 'PC4')],
asb_sp_w = baskets_fruits_weights,
ind_vect = c('fdis', 'fmpd', 'fnnd', 'feve', 'fric', 'fdiv',
'fori', 'fspe'),
scaling = TRUE,
check_input = TRUE,
details_returned = TRUE)
# Retrieve alpha diversity indices table
fd_ind_values_fruits <- alpha_fd_indices_fruits$functional_diversity_indices
fd_ind_values_fruits
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