View source: R/stepArchetypesRawData.R
stepArchetypesRawData | R Documentation |
This is a slight modification of the original stepArchetypes
function of the archetypes R package to apply the archetype algorithm to raw data. The stepArchetypes
function standardizes the data by default and this option is not always desired.
stepArchetypesRawData(data,numArch,numRep=3,verbose=TRUE)
data |
Data to obtain archetypes. |
numArch |
Number of archetypes to compute, from 1 to |
numRep |
For each |
verbose |
If TRUE, the progress during execution is shown. |
A list with numArch
elements. Each element is a list of class attribute stepArchetypes
with numRep
elements.
Guillermo Vinue based on the the original stepArchetypes
function of archetypes.
Eugster, M. J., and Leisch, F., (2009). From Spider-Man to Hero - Archetypal Analysis in R, Journal of Statistical Software 30, 1–23, doi: 10.18637/jss.v030.i08.
Vinue, G., Epifanio, I., and Alemany, S., (2015). Archetypoids: a new approach to define representative archetypal data, Computational Statistics and Data Analysis 87, 102–115.
stepArchetypes
#COCKPIT DESIGN PROBLEM: #As a toy example, only the first 25 individuals are used. USAFSurvey_First25 <- USAFSurvey[1:25, ] #Variable selection: variabl_sel <- c(48, 40, 39, 33, 34, 36) #Changing to inches: USAFSurvey_First25_inch <- USAFSurvey_First25[,variabl_sel] / (10 * 2.54) #Data preprocessing: USAFSurvey_preproc <- preprocessing(USAFSurvey_First25_inch, TRUE, 0.95, TRUE) #For reproducing results, seed for randomness: #suppressWarnings(RNGversion("3.5.0")) #set.seed(2010) #Run archetype algorithm repeatedly from 1 to numArch archetypes: #This is a toy example. In other situation, choose numArch=10 and numRep=20. numArch <- 5 ; numRep <- 2 lass <- stepArchetypesRawData(data = USAFSurvey_preproc$data, numArch = 1:numArch, numRep = numRep, verbose = FALSE) #To understand the warning messages, see the vignette of the #archetypes package.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.