MultBiplotR (version 23.11.0)

AddCluster2Biplot: Add clusters to a biplot object

Description

The function add clusters to a biplot object to be represented on the biplot. The clusters can be defined by a nominal variable provided by the user, obtained from the hclust function of the base package or from the kmeans function

Usage

AddCluster2Biplot(Bip, NGroups=3, ClusterType="hi", Groups=NULL, 
                  Original=FALSE, ClusterColors=NULL, ...)

Value

The function returns the biplot object with the information about the clusters added in new fields

ClusterType

The method of clustering as defined in the argument ClusterType.

Clusters

A factor containing the solution or the user defined clusters

ClusterNames

The names of the clusters

ClusterColors

The colors of the clusters

Dendrogram

The Dendrogram if we have used hirarchical clustering

ClusterObject

The object obtained from hclust, kmeans or MGC

Arguments

Bip

A Biplot object obtained from any biplot procedure. It has to be a list containing a field called Bip$RowCoordinates in order to calculate the clusters when necessary.

NGroups

Number of groups or clusters. Only necessary when hierarchical or k-means procedures are used.

ClusterType

The type of cluster to add. There are three possibilities "us" (User Defined), "hi" (hierarchical clusters), "km" (kmeans clustering) or "gm" (gaussian mixture).

Groups

A factor defining the groups provided by the user.

Original

Should the clusters be calculated using the original data rather than the reduced dimensions?.

ClusterColors

Colors for the clusters.

...

Any other parameter for the hclust and kmeans procedures.

Author

Jose Luis Vicente Villardon

Details

One of the main shortcomings of cluster analysis is that it is not easy to search for the variables associated to the obtained classification; representing the clusters on the biplot can help to perform that interpretation. If you consider the technique for dimension reduction as a way to separate the signal from the noise, clusters should be constructed using the dimensions retained in the biplot, otherwise the complete original data matrix can be used. The colors used by each cluster should match the color used in the Dendrogram. User defined clusters can also be plotted, for example, to investigate the relation of the biplot solution to an external nominal variable.

References

Demey, J. R., Vicente-Villardon, J. L., Galindo-Villardon, M. P., & Zambrano, A. Y. (2008). Identifying molecular markers associated with classification of genotypes by External Logistic Biplots. Bioinformatics, 24(24), 2832-2838.

Gallego-Alvarez, I., & Vicente-Villardon, J. L. (2012). Analysis of environmental indicators in international companies by applying the logistic biplot. Ecological Indicators, 23, 250-261.

Galindo, P. V., Vaz, T. D. N., & Nijkamp, P. (2011). Institutional capacity to dynamically innovate: an application to the Portuguese case. Technological Forecasting and Social Change, 78(1), 3-12.

Vazquez-de-Aldana, B. R., Garcia-Criado, B., Vicente-Tavera, S., & Zabalgogeazcoa, I. (2013). Fungal Endophyte (Epichloë festucae) Alters the Nutrient Content of Festuca rubra Regardless of Water Availability. PloS one, 8(12), e84539.

See Also

For clusters not provided by the user the function uses the standard procedures in hclust and kmeans.

Examples

# \donttest{
data(Protein)
bip=PCA.Biplot(Protein[,3:11])
plot(bip)
# Add user defined clusters containing the region (North, South, Center)
bip=AddCluster2Biplot(bip, ClusterType="us", Groups=Protein$Region)
plot(bip, mode="a", margin=0.1, PlotClus=TRUE)

# Hierarchical clustering on the biplot coordinates using the Ward method
bip=AddCluster2Biplot(bip, ClusterType="hi", method="ward.D")
op <- par(mfrow=c(1,2))
plot(bip, mode="s", margin=0.1, PlotClus=TRUE)
plot(bip$Dendrogram)
par(op)
# K-means cluster on the biplot coordinates using the Ward method
bip=AddCluster2Biplot(bip, ClusterType="hi", method="ward.D")
op <- par(mfrow=c(1,2))
plot(bip, mode="s", margin=0.1, PlotClus=TRUE)
plot(bip$Dendrogram)
par(op)
# }