bootmds.smacofP function

MDS Bootstrap for smacofP objects

Performs a bootstrap on an MDS solution. It works for derived dissimilarities only, i.e. generated by the call dist(data). The original data matrix needs to be provided, as well as the type of dissimilarity measure used to compute the input dissimilarities.

## S3 method for class 'smacofP'
bootmds(
  object,
  data,
  method.dat = "pearson",
  nrep = 100,
  alpha = 0.05,
  verbose = FALSE,
  ...
)

Arguments

• object: Object of class smacofP if used as method or another object inheriting from smacofB (needs to be called directly as bootmds.smacofP then).
• data: Initial data (before dissimilarity computation).
• method.dat: Dissimilarity computation used as MDS input. This must be one of "pearson", "spearman", "kendall", "euclidean", "maximum", "manhattan", "canberra", "binary".
• nrep: Number of bootstrap replications.
• alpha: Alpha level for condfidence ellipsoids.
• verbose: If 'TRUE', bootstrap index is printed out.
• ...: Additional arguments needed for dissimilarity computation as specified in sim2diss.

Returns

An object of class 'smacofboot', see bootmds. With values

• cov: Covariances for ellipse computation
• bootconf: Configurations bootstrap samples
• stressvec: Bootstrap stress values
• bootci: Stress bootstrap percentile confidence interval
• spp: Stress per point (based on stress.en)
• stab: Stability coefficient

Details

In order to examine the stability solution of an MDS, a bootstrap on the raw data can be performed. This results in confidence ellipses in the configuration plot. The ellipses are returned as list which allows users to produce (and further customize) the plot by hand. See bootmds for more.

Examples

##see ?smacof::bootmds for more 
data <- na.omit(smacof::PVQ40[,1:5])
diss <- dist(t(data))   ## Euclidean distances
fit <- rStressMin(diss,r=0.5,itmax=1000) ## 2D ratio MDS
set.seed(123)
resboot <- bootmds(fit, data, method.dat = "euclidean", nrep = 10) #run for more nrep
resboot
plot(resboot) #see ?smacof::bootmds for more on the plot method