Fortify method for envfit objects — fortify.envfit • ggvegan

Produces a tidy data frame from the results of an vegan::envfit() object.

Usage

# S3 method for class 'envfit'
fortify(model, data, ...)

# S3 method for class 'envfit'
tidy(x, data, ...)

Arguments

model, x: an object of class envfit, the result of a call to vegan::envfit().
data: additional data to augment the envfit results. Currently ignored.
...: arguments passed to vegan::scores.envfit().

Value

A data frame with columns label, type, containing the label for, and whether each row refers to, the fitted vector or factor. Remaining variables are coordinates on the respective ordination axes returned by vegan::scores.envfit().

Author

Gavin L. Simpson

Examples


library("vegan")

data(varespec, varechem)
ord <- metaMDS(varespec)
#> Square root transformation
#> Wisconsin double standardization
#> Run 0 stress 0.1843196 
#> Run 1 stress 0.1843196 
#> ... Procrustes: rmse 0.0001319991  max resid 0.0005352859 
#> ... Similar to previous best
#> Run 2 stress 0.2265716 
#> Run 3 stress 0.2244976 
#> Run 4 stress 0.2357179 
#> Run 5 stress 0.2173475 
#> Run 6 stress 0.2370315 
#> Run 7 stress 0.2088293 
#> Run 8 stress 0.1948413 
#> Run 9 stress 0.1982376 
#> Run 10 stress 0.2110414 
#> Run 11 stress 0.1825658 
#> ... New best solution
#> ... Procrustes: rmse 0.04162616  max resid 0.1518042 
#> Run 12 stress 0.1843196 
#> Run 13 stress 0.1825658 
#> ... Procrustes: rmse 1.175466e-05  max resid 3.169968e-05 
#> ... Similar to previous best
#> Run 14 stress 0.2178549 
#> Run 15 stress 0.2109612 
#> Run 16 stress 0.215148 
#> Run 17 stress 0.2327977 
#> Run 18 stress 0.2225663 
#> Run 19 stress 0.18458 
#> Run 20 stress 0.214431 
#> *** Best solution repeated 1 times
fit <- envfit(ord, varechem, perm = 199)

fortify(fit)
#> # A tibble: 14 × 4
#>    label    type     NMDS1  NMDS2
#>    <chr>    <chr>    <dbl>  <dbl>
#>  1 N        Vector -0.0289 -0.503
#>  2 P        Vector  0.273   0.346
#>  3 K        Vector  0.326   0.273
#>  4 Ca       Vector  0.440   0.467
#>  5 Mg       Vector  0.413   0.506
#>  6 S        Vector  0.0801  0.411
#>  7 Al       Vector -0.633   0.356
#>  8 Fe       Vector -0.624   0.235
#>  9 Mn       Vector  0.578  -0.435
#> 10 Zn       Vector  0.268   0.341
#> 11 Mo       Vector -0.223   0.106
#> 12 Baresoil Vector  0.463  -0.190
#> 13 Humdepth Vector  0.673  -0.260
#> 14 pH       Vector -0.311   0.366

data(dune, dune.env)
ord <- ca(dune)
fit <- envfit(ord ~ Moisture + A1, dune.env, perm = 199)

fortify(fit)
#> # A tibble: 5 × 4
#>   label     type        CA1     CA2
#>   <chr>     <chr>     <dbl>   <dbl>
#> 1 A1        Vector   -0.556 -0.0338
#> 2 Moisture1 Centroid  0.748  0.142 
#> 3 Moisture2 Centroid  0.465  0.216 
#> 4 Moisture4 Centroid -0.183  0.732 
#> 5 Moisture5 Centroid -1.11  -0.571