MVar (version 2.2.7)

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

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)