pattern.GDM2 {clusterSim}

An application of GDM2 distance for ordinal data to compute the distances of objects from the pattern object (upper or lower)

Description

An application of GDM2 distance for ordinal data to compute the distances of objects from the upper (ideal point co-ordinates) or lower (anti-ideal point co-ordinates) pattern object

Usage

pattern.GDM2(data, performanceVariable, nomOptValues=NULL,
weightsType="equal", weights=NULL, patternType="upper",
patternCoordinates="dataBounds", patternManual=NULL,
nominalTransfMethod=NULL)

Arguments

data

matrix or dataset

performanceVariable

vector containing three types of performance variables:

s for stimulants where higher value means better performance

d for destimulants where low values indicate better performance

n for nominants where the best value is implied. Object performance is positively assessed if the measure has implied value

nomOptValues

vector containing optimal values for nominant variables and NA values for stimulants and destimulants. If performanceVariable do not contain nominant variables this nomOptValues may be set to NULL

weightsType

equal or different1 or different2

"equal" - equal weights

"different1" - vector of different weights should satisfy conditions: each weight takes value from interval [0; 1] and sum of weights equals one

"different2" - vector of different weights should satisfy conditions: each weight takes value from interval [0; m] and sum of weights equals m (m - the number of variables)

weights

vector of weights

patternType

"upper" - ideal point co-ordinates consists of the best variables' values

"lower" - anti-ideal point co-ordinates consists of the worst variables' values

patternCoordinates

"dataBounds" - pattern should be calculated as following: "upper" pattern (maximum for stimulants, minimum for destimulants, nominal value for nominants), "lower" pattern (minimum for stimulants, maximum for destimulants)

"manual" - pattern should be given in patternManual variable

patternManual

Pattern co-ordinates contain:

real numbers

"min" - for minimal value of variable

"max" - for maximal value of variable

"nom" - for nominal value of variable (for upper pattern only - given in nomOptValues vector)

nominalTransfMethod

method of transformation of nominant to destimulant variable for patternType="lower":

"database" - for each nominant separately GDM2 distance is calculated between each nominant observation (with repetitions - all variable values are used in calculation) and nominal value. Next the variable observations are replaced by those distances

"symmetrical" - for each nominant separately GDM2 distance is calculated between each nominant observation (without repetition - each observation is used once) and nominal value. Next the variable observations are replaced by those distances

Details

See file ../doc/patternGDM2_details.pdf for further details

Value

pdata

raw (primary) data matrix

data

data matrix after transformation of nominant variables (with pattern in last row)

distances

GDM2 distances from pattern object

sortedDistances

sorted GDM2 distances from pattern object

Author(s)

Marek Walesiak [e-mail], Andrzej Dudek [e-mail]

epartment of Econometrics and Computer Science, University of Economics, Wroclaw, Poland http://keii.ue.wroc.pl/clusterSim/

References

Jajuga, K., Walesiak, M., Bak, A. (2003), On the general distance measure, In: M. Schwaiger, O. Opitz (Eds.), Exploratory data analysis in empirical research, Springer-Verlag, Berlin, Heidelberg, 104-109. Available at: doi:10.1007/978-3-642-55721-7_12.

Walesiak, M. (1993), Statystyczna analiza wielowymiarowa w badaniach marketingowych [Multivariate statistical analysis in marketing research]. Wroclaw University of Economics, Research Papers no. 654. Available at: http://keii.ue.wroc.pl/pracownicy/mw/1993_Walesiak_SAW_w_badaniach_marketingowych_OCR.pdf.

Walesiak, M. (1999), Distance Measure for Ordinal Data, "Argumenta Oeconomica", No. 2 (8), 167-173. Available at: http://keii.ue.wroc.pl/pracownicy/mw/1999_Walesiak_Argumenta_Oeconomica_no_2.pdf.

Walesiak, M. (2006), Uogolniona miara odleglosci w statystycznej analizie wielowymiarowej [The Generalized Distance Measure in multivariate statistical analysis], Wydawnictwo AE, Wroclaw. Available at: http://keii.ue.wroc.pl/pracownicy/mw/2006_Walesiak_Uogolniona_miara_odleglosci.pdf.

Walesiak, M. (2011), Uogólniona miara odległości GDM w statystycznej analizie wielowymiarowej z wykorzystaniem programu R [The Generalized Distance Measure GDM in multivariate statistical analysis with R], Wydawnictwo Uniwersytetu Ekonomicznego, Wroclaw. Available at: http://keii.ue.wroc.pl/pracownicy/mw/2011_Walesiak_Uogolniona_miara_odleglosci_GDM_w_SAW_z_wykorzystaniem_programu_R_errata.pdf.

Walesiak, M. (2016), Uogólniona miara odległości GDM w statystycznej analizie wielowymiarowej z wykorzystaniem programu R. Wydanie 2 poprawione i rozszerzone [The Generalized Distance Measure GDM in multivariate statistical analysis with R], Wydawnictwo Uniwersytetu Ekonomicznego, Wroclaw. Available at: http://keii.ue.wroc.pl/pracownicy/mw/2016_Walesiak_Uogolniona_miara_odleglosci_GDM.pdf.

See Also

dist.GDM

Examples

# Example 1
library(clusterSim)
data(data_patternGDM2)
res<-pattern.GDM2(data_patternGDM2,
performanceVariable=c("s","s","s","d","d","n"),
nomOptValues=c(NA,NA,NA,NA,NA,3), weightsType<-"equal", weights=NULL,
patternType="lower", patternCoordinates="manual",
patternManual=c("min","min",0,5,"max","max"),
nominalTransfMethod="symmetrical")
print(res)
gdm_p<-res$distances
plot(cbind(gdm_p,gdm_p),xlim=c(max(gdm_p),min(gdm_p)),
ylim=c(min(gdm_p),max(gdm_p)), 
xaxt="n",xlab="Order of objects from the best to the worst",
ylab="GDM distances from pattern object", lwd=1.6)
axis(1, at=gdm_p,labels=names(gdm_p), cex.axis=0.5)

# Example 2
library(clusterSim)
data(data_patternGDM2)
res<-pattern.GDM2(data_patternGDM2,
performanceVariable=c("s","s","s","d","d","n"),
nomOptValues=c(NA,NA,NA,NA,NA,3), weightsType<-"equal", weights=NULL,
patternType="upper", patternCoordinates="dataBounds",
patternManual=NULL, nominalTransfMethod="database")
print(res)
gdm_p<-res$distances
plot(cbind(gdm_p,gdm_p), xlim=c(min(gdm_p),max(gdm_p)),
ylim=c(min(gdm_p),max(gdm_p)), 
xaxt="n",xlab="Order of objects from the best to the worst",
ylab="GDM distances from pattern object", lwd=1.6)
axis(1, at=gdm_p,labels=names(gdm_p), cex.axis=0.5)