goodness.metaMDS: Goodness of Fit and Shepard Plot for Nonmetric...

Description Usage Arguments Details Value Author(s) See Also Examples

Description

Function goodness.metaMDS find goodness of fit measure for points in nonmetric multidimensional scaling, and function stressplot makes a Shepard diagram.

Usage

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## S3 method for class 'metaMDS'
goodness(object, dis, ...)
## Default S3 method:
stressplot(object, dis, pch, p.col = "blue", l.col = "red", 
    lwd = 2, ...) 

Arguments

object

A result object from metaMDS, monoMDS or isoMDS.

dis

Dissimilarities. This should not be used with metaMDS or monoMDS, but must be used with isoMDS.

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 monoMDS the default is lwd = 1 if more than two lines are drawn, and lwd = 2 otherwise.

...

Other parameters to functions, e.g. graphical parameters.

Details

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 correlation-like statistics on the goodness of fit in the graph. The nonmetric fit is based on stress S and defined as R2 = 1-S*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.

Value

Function goodness returns a vector of values. Function stressplot returns invisibly an object with items for original dissimilarities, ordination distances and fitted values.

Author(s)

Jari Oksanen.

See Also

metaMDS, monoMDS, isoMDS, Shepard. Similar diagrams for eigenvector ordinations can be drawn with stressplot.wcmdscale, stressplot.cca, stressplot.rda and stressplot.capscale.

Examples

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data(varespec)
mod <- metaMDS(varespec)
stressplot(mod)
gof <- goodness(mod)
gof
plot(mod, display = "sites", type = "n")
points(mod, display = "sites", cex = 2*gof/mean(gof))

Example output

Loading required package: permute
Loading required package: lattice
This is vegan 2.4-4
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

vegan documentation built on May 31, 2017, 4:08 a.m.