# 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
# Plot functional spaces:
mFD::funct.space.plot(
sp_faxes_coord = sp_faxes_coord_fruits[, c("PC1", "PC2", "PC3", "PC4")],
faxes = NULL,
name_file = NULL,
faxes_nm = NULL,
range_faxes = c(NA, NA),
color_bg = "grey95",
color_pool = "darkturquoise",
fill_pool = "white",
shape_pool = 21,
size_pool = 1,
plot_ch = TRUE,
color_ch = "darkblue",
fill_ch = "white",
alpha_ch = 1,
plot_vertices = TRUE,
color_vert = "darkturquoise",
fill_vert = "darkturquoise",
shape_vert = 22,
size_vert = 1,
plot_sp_nm = NULL,
nm_size = 3,
nm_color = "black",
nm_fontface = "plain",
check_input = TRUE)
Run the code above in your browser using DataLab