envfit
objectsfortify.envfit.Rd
Produces a tidy data frame from the results of an envfit
object.
# S3 method for class 'envfit'
fortify(model, data, ...)
an object of class envfit
, the result of a call to envfit
.
additional data to augment the envfit
results. Currently ignored.
arguments passed to scores.envfit
.
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 scores.envfit
.
data(varespec, varechem)
ord <- metaMDS(varespec)
#> Square root transformation
#> Wisconsin double standardization
#> Run 0 stress 0.1843196
#> Run 1 stress 0.18458
#> ... Procrustes: rmse 0.04934384 max resid 0.1574557
#> Run 2 stress 0.1948413
#> Run 3 stress 0.2109614
#> Run 4 stress 0.1974421
#> Run 5 stress 0.18584
#> Run 6 stress 0.2212139
#> Run 7 stress 0.2104991
#> Run 8 stress 0.2085949
#> Run 9 stress 0.1843196
#> ... Procrustes: rmse 4.017075e-05 max resid 0.0001549597
#> ... Similar to previous best
#> Run 10 stress 0.2102718
#> Run 11 stress 0.1967393
#> Run 12 stress 0.1955836
#> Run 13 stress 0.22964
#> Run 14 stress 0.1825658
#> ... New best solution
#> ... Procrustes: rmse 0.04162943 max resid 0.1518165
#> Run 15 stress 0.1852397
#> Run 16 stress 0.1843196
#> Run 17 stress 0.2447739
#> Run 18 stress 0.1948414
#> Run 19 stress 0.1967393
#> Run 20 stress 0.2199834
#> *** Best solution was not repeated -- monoMDS stopping criteria:
#> 19: stress ratio > sratmax
#> 1: scale factor of the gradient < sfgrmin
fit <- envfit(ord, varechem, perm = 199)
fortify(fit)
#> label type NMDS1 NMDS2
#> 1 N Vector -0.02885460 -0.5028066
#> 2 P Vector 0.27282423 0.3455426
#> 3 K Vector 0.32600362 0.2732355
#> 4 Ca Vector 0.43971910 0.4674548
#> 5 Mg Vector 0.41332647 0.5061631
#> 6 S Vector 0.08009496 0.4108345
#> 7 Al Vector -0.63272378 0.3558210
#> 8 Fe Vector -0.62446795 0.2347619
#> 9 Mn Vector 0.57767472 -0.4351499
#> 10 Zn Vector 0.26769928 0.3409494
#> 11 Mo Vector -0.22294152 0.1060196
#> 12 Baresoil Vector 0.46318828 -0.1903747
#> 13 Humdepth Vector 0.67273086 -0.2597792
#> 14 pH Vector -0.31132379 0.3658781
data(dune, dune.env)
ord <- cca(dune)
fit <- envfit(ord ~ Moisture + A1, dune.env, perm = 199)
fortify(fit)
#> label type CA1 CA2
#> 1 A1 Vector -0.5560989 -0.03376924
#> 2 Moisture1 Centroid 0.7484337 0.14227818
#> 3 Moisture2 Centroid 0.4651928 0.21556841
#> 4 Moisture4 Centroid -0.1826781 0.73151540
#> 5 Moisture5 Centroid -1.1142548 -0.57080238