bootmds.stops {stops}

MDS Bootstrap for stops objects

Description

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 (note we cannot as of yet have any dissimilarity matrix).

Usage

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

Arguments

object

Object of class stops or pcops.

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.

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.

Value

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

Examples

dats <- na.omit(PVQ40[,1:5])
diss <- dist(t(dats))   ## Euclidean distances 
fit <- stops(diss,loss="rstress",itmax=5,lower=0.2,upper=3)       
set.seed(123)
resboot <- bootmds(fit, dats, method.dat = "euclidean", nrep = 2)
resboot