plot.anthropmmd_result {AnthropMMD}  R Documentation 
Display a multidimensional scaling (MDS) plot with the MMD dissimilarities as input
Description
This function plots a 2D or 3D MDS to represent the MMD
dissimilarities among the groups compared. Various MDS methods are
proposed, and most of them are based on the R package smacof
.
Usage
## S3 method for class 'anthropmmd_result'
plot(x, method = c("classical", "interval", "ratio", "ordinal"),
axes = FALSE, gof = FALSE, dim = 2, asp = TRUE, xlim = NULL, ...)
Arguments
x 
An object of class 
.
method 
Specification of MDS type. 
axes 
Boolean: should the axes be displayed on the plot? 
gof 
Boolean: should goodness of fit statistics be displayed on the topleft corner of the plot? More details below. 
dim 
Numeric value, 2 or 3. Indicates the maximal dimension
desired for the MDS plot. It should be noted that, even with

asp 
Boolean. If 
xlim 
Parameter passed to 
... 
Other arguments possibly passed to 
Details

Axes and scale. Making all axes use the same scale is strongly recommended in all cases (Borg et al., 2013). For a 3Dplot, since the third axis carries generally only a very small percentage of the total variability, you might want to uncheck this option to better visualize the distances along the third axis. In this case, the axes scales must be displayed on the plot, otherwise the plot would be misleading.

Goodness of fit values. (i) For classical metric MDS, a common statistic is given: the sum of the eigenvalues of the first two axes, divided by the sum of all eigenvalues. It indicates the fraction of the total variance of the data represented in the MDS plot. This statistic comes from the
$GOF
value returned by the functionstats::cmdscale
. (ii) For SMACOF methods, the statistic given is the$stress
value returned by the functionsmacof::smacofSym
It indicates the final stress1 value. A value very close to 0 corresponds to a perfect fit. (iii) For both approaches, a 'rho' value is also given, which is the Spearman's correlation coefficient between real dissimilarities (i.e., MMD values) and distances observed on the MDS plot (Dzemyda et al.,2013). A value very close to 1 indicates a perfect fit.
Value
This function returns no value by itself, and only plots a MDS in a new device.
Author(s)
Frédéric Santos, frederic.santos@ubordeaux.fr
References
G. Dzemyda, O. Kurasova and J. Zilinskas (2013) Multidimensional Data Visualization, Springer, chap. 2, p. 39–40.
I. Borg, P. Groenen and P. Mair (2013) Applied Multidimensional Scaling, Springer, chap. 7, p. 79.
See Also
start_mmd
, stats::cmdscale
, smacof::smacofSym
Examples
## Load and visualize a binary dataset:
data(toyMMD)
head(toyMMD)
## Convert this dataframe into a table of sample sizes and relative
## frequencies:
tab < binary_to_table(toyMMD, relative = TRUE)
tab
## Compute and display a symmetrical matrix of MMD values:
mmd_out < mmd(tab, angular = "Freeman")
## Plot a classical metric MDS in two dimensions:
plot(x = mmd_out, method = "classical",
axes = TRUE, gof = TRUE, dim = 2)