countries: Country Dissimilarities

In ElemStatLearn: Data Sets, Functions and Examples from the Book: "The Elements of Statistical Learning, Data Mining, Inference, and Prediction" by Trevor Hastie, Robert Tibshirani and Jerome Friedman

Description

Political science students were asked to give pairwise dissimilarities for 12 countries.

Usage

data(countries)

Format

The format is: num [1:12, 1:12] 0 5.58 7 7.08 4.83 2.17 6.42 3.42 2.5 6.08 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:12] "BEL" "BRA" "CHI" "CUB" ... ..$ : chr [1:12] "BEL" "BRA" "CHI" "CUB" ...

Details

These are average dissimilarities over students.

Source

Kaufman, L. and Rousseeuw, P. (1990) Finding Groups in data: An Introduction to Cluster Analysis, Wiley, New York.

Examples

str(countries)
colnames(countries)
rownames(countries)
if(require(MASS)){
# We use multidimensional scaling:
   if(interactive())par(ask=TRUE)
   countries.cmdscale <- cmdscale(countries, k=2, eig=TRUE)
   eqscplot(countries.cmdscale$points)
   countries.sam <- sammon(countries)
   eqscplot(countries.sam$points)
}

Example output

 num [1:12, 1:12] 0 5.58 7 7.08 4.83 2.17 6.42 3.42 2.5 6.08 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:12] "BEL" "BRA" "CHI" "CUB" ...
  ..$ : chr [1:12] "BEL" "BRA" "CHI" "CUB" ...
 [1] "BEL" "BRA" "CHI" "CUB" "EGY" "FRA" "IND" "ISR" "USA" "USS" "YUG" "ZAI"
 [1] "BEL" "BRA" "CHI" "CUB" "EGY" "FRA" "IND" "ISR" "USA" "USS" "YUG" "ZAI"
Loading required package: MASS
Initial stress        : 0.11767
stress after  10 iters: 0.05445, magic = 0.461
stress after  20 iters: 0.04929, magic = 0.500
stress after  30 iters: 0.04903, magic = 0.500
stress after  40 iters: 0.04897, magic = 0.500

ElemStatLearn documentation built on Aug. 12, 2019, 9:04 a.m.