Cluster: Cluster Analysis.

Description

Performs hierarchical and non-hierarchical cluster analysis in a data set.

Usage

Cluster(data, titles = NA, hierarquic = TRUE, analysis = "Obs",  
        cor.abs = FALSE, normalize = FALSE, distance = "euclidean",  
        method = "complete", horizontal = FALSE, num.groups = 0,
        lambda = 2, savptc = FALSE, width = 3236, height = 2000, 
        res = 300, casc = TRUE)

Value

Several graphics.

tab.res: Table with similarities and distances of the groups formed.
groups: Original data with groups formed.
res.groups: Results of the groups formed.
R.sqt: Result of the R squared.
sum.sqt: Total sum of squares.
mtx.dist: Matrix of the distances.

Arguments

data: Data to be analyzed.
titles: Titles of the graphics, if not set, assumes the default text.
hierarquic: Hierarchical groupings (default = TRUE), for non-hierarchical groupings (method K-Means), only for case 'analysis' = "Obs".
analysis: "Obs" for analysis on observations (default), "Var" for analysis on variables.
cor.abs: Matrix of absolute correlation case 'analysis' = "Var" (default = FALSE).
normalize: Normalize the data only for case 'analysis' = "Obs" (default = FALSE).
distance: Metric of the distances in case of hierarchical groupings: "euclidean" (default), "maximum", "manhattan", "canberra", "binary" or "minkowski". Case Analysis = "Var" the metric will be the correlation matrix, according to cor.abs.
method: Method for analyzing hierarchical groupings: "complete" (default), "ward.D", "ward.D2", "single", "average", "mcquitty", "median" or "centroid".
horizontal: Horizontal dendrogram (default = FALSE).
num.groups: Number of groups to be formed.
lambda: Value used in the minkowski distance.
savptc: Saves graphics images to files (default = FALSE).
width: Graphics images width when savptc = TRUE (defaul = 3236).
height: Graphics images height when savptc = TRUE (default = 2000).
res: Nominal resolution in ppi of the graphics images when savptc = TRUE (default = 300).
casc: Cascade effect in the presentation of the graphics (default = TRUE).

Author

Paulo Cesar Ossani

References

Rencher, A. C. Methods of multivariate analysis. 2th. ed. New York: J.Wiley, 2002. 708 p.

Mingoti, S. A. analysis de dados atraves de metodos de estatistica multivariada: uma abordagem aplicada. Belo Horizonte: UFMG, 2005. 297 p.

Ferreira, D. F. Estatistica Multivariada. 2a ed. revisada e ampliada. Lavras: Editora UFLA, 2011. 676 p.

Examples

Run this code

data(DataQuan) # set of quantitative data

data <- DataQuan[,2:8]

rownames(data) <- DataQuan[1:nrow(DataQuan),1]

res <- Cluster(data, titles = NA, hierarquic = TRUE, analysis = "Obs",
               cor.abs = FALSE, normalize = FALSE, distance = "euclidean", 
               method = "ward.D", horizontal = FALSE, num.groups = 2,
               savptc = FALSE, width = 3236, height = 2000, res = 300, 
               casc = FALSE)

print("R squared:"); res$R.sqt
# print("Total sum of squares:"); res$sum.sqt
print("Groups formed:"); res$groups
# print("Table with similarities and distances:"); res$tab.res
# print("Table with the results of the groups:"); res$res.groups
# print("Distance Matrix:"); res$mtx.dist 
 
write.table(file=file.path(tempdir(),"SimilarityTable.csv"), res$tab.res, sep=";",
            dec=",",row.names = FALSE) 
write.table(file=file.path(tempdir(),"GroupData.csv"), res$groups, sep=";",
            dec=",",row.names = TRUE) 
write.table(file=file.path(tempdir(),"GroupResults.csv"), res$res.groups, sep=";",
            dec=",",row.names = TRUE)

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