R: Ordinal data with 27 objects and 6 variables (3 stimulant...

data_patternGDM2 {clusterSim} | R Documentation

## 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)

[Package clusterSim version 0.49-2 Index]