makeMDSTask: Creates a Multi Dimensional Scaling Task Object

In ptl93/AEDA: Automated Exploratory Data Analysis

Description

A Task encapsulates the Data with some additional information.
As of now MDS performs classical (weighted) MDS and non-metric methods

Usage

makeMDSTask(id, data, dist.norm = "euclidean", method = "cmdscale",
  par.vals = list(), show.NA.msg = FALSE, ...)

Arguments

id	[character(1)] ID of the Task Object
data	[data.frame] A Dataframe with different variables
dist.norm	[character(1)] Character indicating how the distance matrix will be computed. Possible values are:"euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski".
method	[character(1)] Defines the MDS method Possible choices are: For Metric Scaling: cmdscale - for more information @seealso cmdscale wcmdscale - for more information @seealso wcmdscale smacofSym - for more information @seealso smacofSym For Non-Metric Scaling: isoMDS - for more information @seealso isoMDS sammon - for more information @seealso sammon Default is method = "cmdscale"
par.vals	[list] Additional arguments handled over to MDS algorithm method. Default is empty list par.vals = list()
show.NA.msg	[logical(1)] Logical whether to show missing values message Default is FALSE.
...	For now has no use

Value

MDSTask Object

Examples

data(swiss)
mds.task = makeMDSTask(id = "swiss", data = swiss,
 dist.norm = "euclidean", method = "cmdscale", show.NA.msg = TRUE)

ptl93/AEDA documentation built on May 7, 2019, 3:20 p.m.