data_patternGDM2 {clusterSim}

Ordinal data with 27 objects and 6 variables (3 stimulant variables, 2 destimulant variables and 1 nominant variable)

Description

Ordinal data with 27 objects and 6 variables (3 stimulant variables, 2 destimulant variables and 1 nominant variable)

Residential housing properties were described by the following variables:

x1 - Location of environmental land, which is linked to a dwelling (1 - poor, 2 - inadequate, 3 - satisfactory, 4 - good, 5 - very good),

x2 - Standard utility of a dwelling (1 - bad, 2 - low, 3 - average, 4 - high),

x3 - Living conditions occurring on the land, which is linked to a dwelling (1 - bad, 2 - average, 3 - good),

x4 - Location of land, which is related to dwelling in the area of the city (1 - central, 2 - downtown, 3 - intermediate, 4 - peripheral),

x5 - Type of condominium (1 - low, 2 - large),

x6 - Area of land, which is related to dwelling (1 - below the contour of the building, 2 - outline of the building, 3 - the outline of the building with the environment acceptable, such as parking, playground, 4 - the outline of the building with the environment too much).

Types of performance variables:

x1, x2, x3 - stimulants,

x4, x5 - destimulants,

x6 - nominant (the nominal category: 3).

Format

data.frame: 27 objects, 6 variables

Source

data from real estate market

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)