| plot.hclustering {BasketballAnalyzeR} | R Documentation | 
Plots hierarchical clustering from a 'hclustering' object
Description
Plots hierarchical clustering from a 'hclustering' object
Usage
## S3 method for class 'hclustering'
plot(
  x,
  title = NULL,
  profiles = FALSE,
  ncol.arrange = NULL,
  circlize = FALSE,
  horiz = TRUE,
  cex.labels = 0.7,
  colored.labels = TRUE,
  colored.branches = FALSE,
  rect = FALSE,
  lower.rect = NULL,
  min.mid.max = NULL,
  ...
)
Arguments
| x | an object of class  | 
| title | character or vector of characters (when plotting radial plots of cluster profiles; see Value), plot title(s). | 
| profiles | logical; if  | 
| ncol.arrange | integer, number of columns when arranging multiple grobs on a page (active when plotting radial plots of cluster profiles; see Value). | 
| circlize | logical; if  | 
| horiz | logical; if  | 
| cex.labels | numeric, the magnification to be used for labels (active when plotting a dendrogram; see Value). | 
| colored.labels | logical; if  | 
| colored.branches | logical; if  | 
| rect | logical; if  | 
| lower.rect | numeric, a value of how low should the lower part of the rect be (active when plotting a dendrogram; see option  | 
| min.mid.max | numeric vector with 3 elements: lower bound, middle dashed line, upper bound for radial axis (active when plotting radial plots of cluster profiles; see Value). | 
| ... | other graphical parameters. | 
Value
If x$k is NULL, plot.hclustering returns a single ggplot2 object, displaying the pattern of the explained variance vs the number of clusters.
If x$k is not NULL and profiles=FALSE, plot.hclustering returns a single ggplot2 object, displaying the dendrogram.
If x$k is not NULL and profiles=TRUE, plot.hclustering returns a list of ggplot2 objects, displaying the radial plots of the cluster profiles.
Author(s)
Marco Sandri, Paola Zuccolotto, Marica Manisera (basketballanalyzer.help@unibs.it)
References
P. Zuccolotto and M. Manisera (2020) Basketball Data Science: With Applications in R. CRC Press.
See Also
Examples
data <- with(Pbox, data.frame(PTS, P3M, REB=OREB+DREB, AST, TOV, STL, BLK, PF))
data <- subset(data, Pbox$MIN >= 1500)
ID <- Pbox$Player[Pbox$MIN >= 1500]
hclu1 <- hclustering(data)
plot(hclu1)
hclu2 <- hclustering(data, labels=ID, k=7)
plot(hclu2)