Description Usage Arguments Details Value Author(s) See Also Examples
Function goodness.metaMDS
find goodness of fit measure for
points in nonmetric multidimensional scaling, and function
stressplot
makes a Shepard
diagram.
1 2 3 4 5  ## S3 method for class 'metaMDS'
goodness(object, dis, ...)
## Default S3 method:
stressplot(object, dis, pch, p.col = "blue", l.col = "red",
lwd = 2, ...)

object 
A result object from 
dis 
Dissimilarities. This should not be used with

pch 
Plotting character for points. Default is dependent on the number of points. 
p.col, l.col 
Point and line colours. 
lwd 
Line width. For 
... 
Other parameters to functions, e.g. graphical parameters. 
Function goodness.metaMDS
finds a goodness of fit statistic
for observations (points). This is defined so that sum of squared
values is equal to squared stress. Large values indicate poor fit.
The absolute values of the goodness statistic depend on the
definition of the stress: isoMDS
expresses
stress in percents, and therefore its goodness values are 100 times
higher than those of monoMDS
which expresses the
stress as a proportion.
Function stressplot
draws a Shepard diagram which is a plot
of ordination distances and monotone or linear fit line against
original dissimilarities. In addition, it displays two
correlationlike statistics on the goodness of fit in the graph.
The nonmetric fit is based on stress S and defined as R2 = 1S*S. The “linear fit” is the squared
correlation between fitted values and ordination distances. For
monoMDS
, the “linear fit” and R2
from “stress type 2” are equal.
Both functions can be used with metaMDS
,
monoMDS
and isoMDS
. The original
dissimilarities should not be given for monoMDS
or
metaMDS
results (the latter tries to reconstruct the
dissimilarities using metaMDSredist
if
isoMDS
was used as its engine). With
isoMDS
the dissimilarities must be given. In
either case, the functions inspect that dissimilarities are
consistent with current ordination, and refuse to analyse
inconsistent dissimilarities. Function goodness.metaMDS
is
generic in vegan, but you must spell its name completely with
isoMDS
which has no class.
Function goodness
returns a vector of values. Function
stressplot
returns invisibly an object with items for
original dissimilarities, ordination distances and fitted values.
Jari Oksanen.
metaMDS
, monoMDS
,
isoMDS
, Shepard
. Similar
diagrams for eigenvector ordinations can be drawn with
stressplot.wcmdscale
, stressplot.cca
,
stressplot.rda
and stressplot.capscale
.
1 2 3 4 5 6 7 
Loading required package: permute
Loading required package: lattice
This is vegan 2.44
Square root transformation
Wisconsin double standardization
Run 0 stress 0.1843196
Run 1 stress 0.3811651
Run 2 stress 0.2126571
Run 3 stress 0.1962453
Run 4 stress 0.2439795
Run 5 stress 0.1976152
Run 6 stress 0.2173476
Run 7 stress 0.1852399
Run 8 stress 0.2005511
Run 9 stress 0.1948417
Run 10 stress 0.2124996
Run 11 stress 0.2028828
Run 12 stress 0.2530506
Run 13 stress 0.1974419
Run 14 stress 0.2204287
Run 15 stress 0.2104573
Run 16 stress 0.1982376
Run 17 stress 0.1948415
Run 18 stress 0.2045022
Run 19 stress 0.2134694
Run 20 stress 0.1962451
*** No convergence  monoMDS stopping criteria:
20: stress ratio > sratmax
[1] 0.02956349 0.03430663 0.04593897 0.04039584 0.04187239 0.03235999
[7] 0.03348133 0.03063787 0.03138894 0.02968296 0.03368395 0.02593423
[13] 0.03839280 0.03420943 0.02954730 0.02675647 0.04274826 0.03775407
[19] 0.06790026 0.03251787 0.02989796 0.03040904 0.05486768 0.04079135
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