sjc.elbow

data frame containing all variables that should be used for 
determining the elbow criteria

data

maximum group-count for the k-means cluster analysis for
which the elbow-criterion should be displayed. Default is <code>15</code>.

steps

logical, if <code>TRUE</code>, an additional plot with the differences between 
each fusion step of the Elbow criterion calculation is shown. This plot
may help identifying the "elbow". Default for this argument is <code>FALSE</code>.

show.diff


Plot elbow values of a k-means cluster analysis. This function
               computes a k-means cluster analysis on the provided data frame
               and produces two plots: one with the different elbow values
               and a second plot that maps the differences between each
               "step" (i.e. between elbow values) on the y-axis. An
               increase in the second plot may indicate the elbow criterion.


Collection of plotting and table output functions for data
visualization. Results of various statistical analyses (that are commonly used
in social sciences) can be visualized using this package, including simple and
cross tabulated frequencies, histograms, box plots, (generalized) linear models,
mixed effects models, PCA and correlation matrices, cluster analyses, scatter
plots, Likert scales, effects plots of regression models (including interaction
terms) and much more. This package supports labelled data.

sjc.elbow: Compute elbow values of a k-means cluster analysis

Description

Usage

Arguments

Examples