Function implements Kruskal's (1964a,b) non-metric multidimensional scaling (NMDS) using monotone regression and primary (“weak”) treatment of ties. In addition to traditional global NMDS, the function implements local NMDS, linear and hybrid multidimensional scaling.
monoMDS(dist, y, k = 2, model = c("global", "local", "linear", "hybrid"),
threshold = 0.8, maxit = 200, weakties = TRUE, stress = 1,
scaling = TRUE, pc = TRUE, smin = 1e-4, sfgrmin = 1e-7,
sratmax=0.999999, ...)
# S3 method for monoMDS
scores(x, display = "sites", shrink = FALSE, choices,
tidy = FALSE, ...)
# S3 method for monoMDS
plot(x, display = "sites", choices = c(1,2), type = "t", ...)
# S3 method for monoMDS
points(x, display = "sites", choices = c(1,2), select, ...)
# S3 method for monoMDS
text(x, display = "sites", labels, choices = c(1,2),
select, ...)Returns an object of class "monoMDS". The final scores
are returned in item points (function scores extracts
these results), and the stress in item stress. In addition,
there is a large number of other items (but these may change without
notice in the future releases). There are no species scores, but these
can be added with sppscores function.
Input dissimilarities.
Starting configuration. A random configuration will be generated if this is missing.
Number of dimensions. NB., the number of points \(n\) should be \(n > 2k + 1\), and preferably higher in non-metric MDS.
MDS model: "global" is normal non-metric MDS
with a monotone regression, "local" is non-metric MDS with
separate regressions for each point, "linear" uses linear
regression, and "hybrid" uses linear regression for
dissimilarities below a threshold in addition to monotone
regression. See Details.
Dissimilarity below which linear regression is used alternately with monotone regression.
Maximum number of iterations.
Use primary or weak tie treatment, where equal
observed dissimilarities are allowed to have different fitted
values. if FALSE, then secondary (strong) tie treatment is
used, and tied values are not broken.
Use stress type 1 or 2 (see Details).
Scale final scores to unit root mean squares.
Rotate final scores to principal components.
Convergence criteria: iterations stop
when stress drops below smin, scale factor of the gradient
drops below sfgrmin, or stress ratio between two iterations
goes over sratmax (but is still \(< 1\)).
A monoMDS result.
Kind of scores. Normally there are only scores for
"sites", but "species" scores can be added with
sppscores.
Shrink back species scores if they were expanded in
wascores.
Return scores that are compatible with ggplot2:
all scores are in a single data.frame, score type is
identified by factor variable code ("sites" or
"species"), the names by variable label. These scores
are incompatible with conventional plot functions, but they can
be used in ggplot2.
Dimensions returned or plotted. The default NA
returns all dimensions.
The type of the plot: "t" for text, "p"
for points, and "n" for none.
Items to be displayed. This can either be a logical
vector which is TRUE for displayed items or a vector of
indices of displayed items.
Labels to be use used instead of row names. If
select is used, labels are given only the selected items in
the order they occur in the scores.
Other parameters to the functions (ignored in
monoMDS, passed to graphical functions in plot.).
NMDS is iterative, and the function stops when any of its
convergence criteria is met. There is actually no criterion of
assured convergence, and any solution can be a local optimum. You
should compare several random starts (or use monoMDS via
metaMDS) to assess if the solutions is likely a global
optimum.
The stopping criteria are:
maxit:Maximum number of iterations. Reaching this
criterion means that solutions was almost certainly not found,
and maxit should be increased.
smin:Minimum stress. If stress is nearly zero,
the fit is almost perfect. Usually this means that data set is
too small for the requested analysis, and there may be several
different solutions that are almost as perfect. You should reduce
the number of dimensions (k), get more data (more
observations) or use some other method, such as metric scaling
(cmdscale, wcmdscale).
sratmax:Change in stress. Values close to one mean almost unchanged stress. This may mean a solution, but it can also signal stranding on suboptimal solution with flat stress surface.
sfgrmin:Minimum scale factor. Values close to zero mean almost unchanged configuration. This may mean a solution, but will also happen in local optima.
Peter R. Michin (Fortran core) and Jari Oksanen (R interface).
There are several versions of non-metric multidimensional
scaling in R, but monoMDS offers the following unique
combination of features:
“Weak” treatment of ties (Kruskal 1964a,b), where tied dissimilarities can be broken in monotone regression. This is especially important for cases where compared sites share no species and dissimilarities are tied to their maximum value of one. Breaking ties allows these points to be at different distances and can help in recovering very long coenoclines (gradients). Functions in the smacof package also hav adequate tie treatment.
Handles missing values in a meaningful way.
Offers “local” and “hybrid” scaling in addition to usual “global” NMDS (see below).
Uses fast compiled code (isoMDS of the
MASS package also uses compiled code).
Function monoMDS uses Kruskal's (1964b) original monotone
regression to minimize the stress. There are two alternatives of
stress: Kruskal's (1964a,b) original or “stress 1” and an
alternative version or “stress 2” (Sibson 1972). Both of
these stresses can be expressed with a general formula
$$s^2 = \frac{\sum (d - \hat d)^2}{\sum(d - d_0)^2}$$
where \(d\) are distances among points in ordination configuration,
\(\hat d\) are the fitted ordination distances, and
\(d_0\) are the ordination distances under null model. For
“stress 1” \(d_0 = 0\), and for “stress 2”
\(d_0 = \bar{d}\) or mean distances. “Stress 2”
can be expressed as \(s^2 = 1 - R^2\),
where\(R^2\) is squared correlation between fitted values and
ordination distances, and so related to the “linear fit” of
stressplot.
Function monoMDS can fit several alternative NMDS variants that
can be selected with argument model. The default model =
"global" fits global NMDS, or Kruskal's (1964a,b) original NMDS
similar to isoMDS (MASS). Alternative
model = "local" fits local NMDS where independent monotone
regression is used for each point (Sibson 1972). Alternative
model = "linear" fits a linear MDS. This fits a linear
regression instead of monotone, and is not identical to metric scaling
or principal coordinates analysis (cmdscale) that
performs an eigenvector decomposition of dissimilarities (Gower
1966). Alternative model = "hybrid" implements hybrid MDS that
uses monotone regression for all points and linear regression for
dissimilarities below or at a threshold dissimilarity in
alternating steps (Faith et al. 1987). Function
stressplot can be used to display the kind of regression
in each model.
Scaling, orientation and direction of the axes is arbitrary.
However, the function always centres the axes, and the default
scaling is to scale the configuration of unit root mean
square and to rotate the axes (argument pc) to principal
components so that the first dimension shows the major variation.
It is possible to rotate the solution so that the first axis is
parallel to a given environmental variable using function
MDSrotate.
Faith, D.P., Minchin, P.R and Belbin, L. 1987. Compositional dissimilarity as a robust measure of ecological distance. Vegetatio 69, 57--68.
Gower, J.C. (1966). Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53, 325--328.
Kruskal, J.B. 1964a. Multidimensional scaling by optimizing goodness-of-fit to a nonmetric hypothesis. Psychometrika 29, 1--28.
Kruskal, J.B. 1964b. Nonmetric multidimensional scaling: a numerical method. Psychometrika 29, 115--129.
Minchin, P.R. 1987. An evaluation of relative robustness of techniques for ecological ordinations. Vegetatio 69, 89--107.
Sibson, R. 1972. Order invariant methods for data analysis. Journal of the Royal Statistical Society B 34, 311--349.
data(dune)
dis <- vegdist(dune)
m <- monoMDS(dis, model = "loc")
m
plot(m)
Run the code above in your browser using DataLab