Compute relationship between all traits and all axes of the functional space. For continuous trait a linear model is computed and r2 and p-value are returned. For other types of traits, a Kruskal-Wallis test is computed and eta2 statistics is returned. Option allows to plot trait-axis relationships with scatterplot and boxplot for continuous and non-continuous traits, respectively.
traits.faxes.cor(
sp_tr,
sp_faxes_coord,
tr_nm = NULL,
faxes_nm = NULL,
plot = FALSE,
name_file = NULL,
color_signif = "darkblue",
color_non_signif = "gray80",
stop_if_NA = TRUE
)a data frame containing species as rows and traits as columns.
a matrix of species coordinates in a multidimensional
functional space. Species coordinates have been retrieved
thanks to tr.cont.fspace or quality.fspaces.
a vector gathering the names of traits (as in sp_tr) to
consider. If NULL all traits are considered.
a vector gathering the names of PCoA axes (as in
sp_faxes_coord) to consider.
a logical value indicating whether plot illustrating relations between trait and axes should be drawn. You can only plot relationships for up to 10 traits and/or 10 axes.
the file name (without extension) to save the plot as a 300
dpi JPEG file. Default is NULL which means plot is only displayed. If
plot = FALSE this argument is ignored.
an R color name or an hexadecimal code referring to
the color of points when relationships between the trait and the axis is
significant. Default is darkblue.
an R color name or an hexadecimal code referring to
the
color of points when relationships between the trait and the axis are not
significant. Default is gray80.
a logical value to stop or not the process if the
sp_tr data frame contains NA. Functional measures are sensitive to
missing traits. For further explanations, see the Note section.
Default is TRUE.
1 data frame with for each combination of trait and axis (rows), the
name of the test performed, and the corresponding statistics and p-value.
If plot = TRUE a multi-panel figure with traits as columns and axes as
rows is also plotted. When relationships between trait and axis is
significant the points are colored, else they remain grayish.
# Load Species x Traits Data
data("fruits_traits", 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)
#> [1] "Running w.type=equal on groups=c(Size)"
#> [1] "Running w.type=equal on groups=c(Plant)"
#> [1] "Running w.type=equal on groups=c(Climate)"
#> [1] "Running w.type=equal on groups=c(Seed)"
#> [1] "Running w.type=equal on groups=c(Sugar)"
#> [1] "Running w.type=equal on groups=c(Use,Use,Use)"
# Compute Functional Spaces Quality (to retrieve species coordinates)
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
sp_faxes_coord_fruits <- fspaces_quality_fruits$details_fspaces$sp_pc_coord
# Compute Correlation between Traits and Functional Axes
mFD::traits.faxes.cor(
sp_tr = fruits_traits,
sp_faxes_coord = sp_faxes_coord_fruits,
tr_nm = NULL,
faxes_nm = NULL,
name_file = NULL,
color_signif = "darkblue",
color_non_signif = "gray80")
#> trait axis test stat value p.value
#> 1 Size PC1 Kruskal-Wallis eta2 0.284 0.0461
#> 2 Size PC2 Kruskal-Wallis eta2 0.316 0.0352
#> 3 Size PC3 Kruskal-Wallis eta2 0.008 0.3839
#> 4 Size PC4 Kruskal-Wallis eta2 0.285 0.0459
#> 5 Size PC5 Kruskal-Wallis eta2 0.151 0.1349
#> 6 Size PC6 Kruskal-Wallis eta2 0.312 0.0366
#> 7 Size PC7 Kruskal-Wallis eta2 0.100 0.1990
#> 8 Size PC8 Kruskal-Wallis eta2 0.164 0.1216
#> 9 Size PC9 Kruskal-Wallis eta2 0.299 0.0408
#> 10 Size PC10 Kruskal-Wallis eta2 0.132 0.1564
#> 11 Plant PC1 Kruskal-Wallis eta2 0.315 0.0222
#> 12 Plant PC2 Kruskal-Wallis eta2 0.413 0.0086
#> 13 Plant PC3 Kruskal-Wallis eta2 0.197 0.0675
#> 14 Plant PC4 Kruskal-Wallis eta2 -0.057 0.6157
#> 15 Plant PC5 Kruskal-Wallis eta2 0.266 0.0354
#> 16 Plant PC6 Kruskal-Wallis eta2 0.089 0.1818
#> 17 Plant PC7 Kruskal-Wallis eta2 -0.087 0.7598
#> 18 Plant PC8 Kruskal-Wallis eta2 0.187 0.0745
#> 19 Plant PC9 Kruskal-Wallis eta2 -0.014 0.4378
#> 20 Plant PC10 Kruskal-Wallis eta2 -0.138 0.9927
#> 21 Climate PC1 Kruskal-Wallis eta2 0.746 0.0001
#> 22 Climate PC2 Kruskal-Wallis eta2 -0.048 0.6216
#> 23 Climate PC3 Kruskal-Wallis eta2 0.030 0.2636
#> 24 Climate PC4 Kruskal-Wallis eta2 0.194 0.0433
#> 25 Climate PC5 Kruskal-Wallis eta2 -0.015 0.4334
#> 26 Climate PC6 Kruskal-Wallis eta2 -0.032 0.5232
#> 27 Climate PC7 Kruskal-Wallis eta2 -0.012 0.4199
#> 28 Climate PC8 Kruskal-Wallis eta2 -0.022 0.4706
#> 29 Climate PC9 Kruskal-Wallis eta2 0.116 0.1026
#> 30 Climate PC10 Kruskal-Wallis eta2 0.024 0.2821
#> 31 Seed PC1 Kruskal-Wallis eta2 0.111 0.1082
#> 32 Seed PC2 Kruskal-Wallis eta2 0.475 0.0020
#> 33 Seed PC3 Kruskal-Wallis eta2 0.435 0.0031
#> 34 Seed PC4 Kruskal-Wallis eta2 0.057 0.1957
#> 35 Seed PC5 Kruskal-Wallis eta2 0.034 0.2524
#> 36 Seed PC6 Kruskal-Wallis eta2 -0.074 0.8344
#> 37 Seed PC7 Kruskal-Wallis eta2 -0.081 0.8981
#> 38 Seed PC8 Kruskal-Wallis eta2 -0.040 0.5700
#> 39 Seed PC9 Kruskal-Wallis eta2 -0.040 0.5700
#> 40 Seed PC10 Kruskal-Wallis eta2 -0.063 0.7397
#> 41 Sugar PC1 Linear Model r2 0.089 0.1486
#> 42 Sugar PC2 Linear Model r2 0.195 0.0273
#> 43 Sugar PC3 Linear Model r2 0.218 0.0187
#> 44 Sugar PC4 Linear Model r2 0.022 0.4814
#> 45 Sugar PC5 Linear Model r2 0.094 0.1357
#> 46 Sugar PC6 Linear Model r2 0.305 0.0042
#> 47 Sugar PC7 Linear Model r2 0.032 0.3948
#> 48 Sugar PC8 Linear Model r2 0.004 0.7668
#> 49 Sugar PC9 Linear Model r2 0.000 0.9473
#> 50 Sugar PC10 Linear Model r2 0.000 0.9252
#> 51 Use.raw PC1 Linear Model r2 0.497 0.0001
#> 52 Use.raw PC2 Linear Model r2 0.004 0.7551
#> 53 Use.raw PC3 Linear Model r2 0.103 0.1168
#> 54 Use.raw PC4 Linear Model r2 0.218 0.0187
#> 55 Use.raw PC5 Linear Model r2 0.002 0.8154
#> 56 Use.raw PC6 Linear Model r2 0.117 0.0947
#> 57 Use.raw PC7 Linear Model r2 0.023 0.4740
#> 58 Use.raw PC8 Linear Model r2 0.000 0.9295
#> 59 Use.raw PC9 Linear Model r2 0.011 0.6154
#> 60 Use.raw PC10 Linear Model r2 0.005 0.7474
#> 61 Use.pastry PC1 Linear Model r2 0.031 0.4036
#> 62 Use.pastry PC2 Linear Model r2 0.000 0.9630
#> 63 Use.pastry PC3 Linear Model r2 0.061 0.2349
#> 64 Use.pastry PC4 Linear Model r2 0.360 0.0015
#> 65 Use.pastry PC5 Linear Model r2 0.283 0.0062
#> 66 Use.pastry PC6 Linear Model r2 0.091 0.1432
#> 67 Use.pastry PC7 Linear Model r2 0.100 0.1228
#> 68 Use.pastry PC8 Linear Model r2 0.002 0.8436
#> 69 Use.pastry PC9 Linear Model r2 0.004 0.7750
#> 70 Use.pastry PC10 Linear Model r2 0.010 0.6325
#> 71 Use.jam PC1 Linear Model r2 0.567 0.0000
#> 72 Use.jam PC2 Linear Model r2 0.006 0.7239
#> 73 Use.jam PC3 Linear Model r2 0.055 0.2588
#> 74 Use.jam PC4 Linear Model r2 0.033 0.3835
#> 75 Use.jam PC5 Linear Model r2 0.082 0.1655
#> 76 Use.jam PC6 Linear Model r2 0.050 0.2831
#> 77 Use.jam PC7 Linear Model r2 0.153 0.0533
#> 78 Use.jam PC8 Linear Model r2 0.003 0.8123
#> 79 Use.jam PC9 Linear Model r2 0.029 0.4193
#> 80 Use.jam PC10 Linear Model r2 0.000 0.9327