R: Metric data with 17 objects and 10 variables (8 stimulant...

data_patternGDM1 {clusterSim} | R Documentation

## Metric data with 17 objects and 10 variables (8 stimulant variables, 2 destimulant variables)

### Description

Metric data with 17 objects and 10 variables (8 stimulant variables, 2 destimulant variables)

Data on the Polish voivodships, owing to the conditions of the population living in cities in 2007. The analysis includes the following variables:

x1 - dwellings in per cent fitted with water-line system,

x2 - dwellings in per cent fitted with lavatory,

x3 - dwellings in per cent fitted with bathroom,

x4 - dwellings in per cent fitted with gas-line system,

x5 - dwellings in per cent fitted with central heating,

x6 - average number of rooms per dwelling,

x7 - average number of persons per dwelling,

x8 - average number of persons per room,

x9 - usable floor space in square meter per dwelling,

x10 - usable floor space in square meter per person.

Types of performance variables:

x1 - x6, x9, x10 - stimulants,

x7, x8 - destimulants.

### Format

data.frame: 17 objects, 10 variables

### Source

Voivodships Statistical Yearbook, Poland 2008.

### Examples

    # Example 1
    library(clusterSim)
    data(data_patternGDM1)
    res<-pattern.GDM1(data_patternGDM1,
    performanceVariable=c("s","s","s","s","s","s","d","d","s","s"),
    scaleType="r",nomOptValues=NULL,weightsType<-"equal",weights=NULL,
    normalization<-"n4",patternType<-"lower",patternCoordinates<-"manual",
    patternManual<-c(0,0,0,0,0,"min","max","max","min","min"),
    nominalTransfMethod <-NULL)
    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_patternGDM1)
    res<-pattern.GDM1(data_patternGDM1,
    performanceVariable=c("s","s","s","s","s","s","d","d","s","s"),
    scaleType="r",nomOptValues=NULL,weightsType<-"equal",weights=NULL,
    normalization<-"n2",patternType<-"upper",
    patternCoordinates<-"dataBounds",patternManual<-NULL,
    nominalTransfMethod <-NULL)
    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]