hierarchical {Kira} | R Documentation |
Hierarchical unsupervised classification.
Description
Performs hierarchical unsupervised classification analysis in a data set.
Usage
hierarchical(data, titles = NA, 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)
Arguments
data |
Data to be analyzed. |
titles |
Titles of the graphics, if not set, assumes the default text. |
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). |
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. |
Author(s)
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(iris) # data set
data <- iris
res <- hierarchical(data[,1:4], titles = NA, analysis = "Obs", cor.abs = FALSE,
normalize = FALSE, distance = "euclidean", method = "ward.D",
horizontal = FALSE, num.groups = 3, savptc = FALSE, width = 3236,
height = 2000, res = 300, casc = FALSE)
message("R squared: ", res$R.sqt)
# message("Total sum of squares: ", res$sum.sqt)
message("Groups formed: "); res$groups
# message("Table with similarities and distances:"); res$tab.res
# message("Table with the results of the groups:"); res$res.groups
# message("Distance Matrix:"); res$mtx.dist
#write.table(file=file.path(tempdir(),"GroupData.csv"), res$groups, sep=";",
# dec=",",row.names = TRUE)