tonedata

<p>The tone perception data stem
  from an experiment of Cohen (1980) and have been analyzed in de Veaux
  (1989).
  A pure fundamental tone was played to a
  trained musician. Electronically generated overtones were added, determined 
  by a stretching ratio of <code>stretchratio</code>. <code>stretchratio=2.0</code>
  corresponds to the harmonic pattern
  usually heard in traditional definite pitched instruments. The musician was
  asked to tune an adjustable tone to the octave above the fundamental tone.
  <code>tuned</code> gives the ratio of the adjusted tone to the fundamental,
  i.e. <code>tuned=2.0</code> would be the correct tuning for all
  <code>stretchratio</code>-values.
  The data analyzed here belong to 150 trials 
  with the same musician. In the original study, there were four further
  musicians.</p>

datasets

Various methods for clustering and cluster validation.
Fixed point clustering. Linear regression clustering. Clustering by
merging Gaussian mixture components. Symmetric
and asymmetric discriminant projections for visualisation of the
separation of groupings. Cluster validation statistics
for distance based clustering including corrected Rand index.
Cluster-wise cluster stability assessment. Methods for estimation of
the number of clusters: Calinski-Harabasz, Tibshirani and Walther's
prediction strength, Fang and Wang's bootstrap stability.
Gaussian/multinomial mixture fitting for mixed
continuous/categorical variables. Variable-wise statistics for cluster
interpretation. DBSCAN clustering. Interface functions for many
clustering methods implemented in R, including estimating the number of
clusters with kmeans, pam and clara. Modality diagnosis for Gaussian
mixtures. For an overview see package?fpc.

ORPHANED

Flexible Procedures for Clustering

tonedata function

<p>A data frame with 2 variables <code>stretchratio</code> and
  <code>tuned</code> and 150 cases.</p>

tonedata: Tone perception data

Description

Usage

Arguments

Format

References