plspm (version 0.2-2)

resclus.plot: Colored dendrogram for communality and structural residuals according to the REBUS algorithm

Description

Plot function for objects of class "hclust" returned by res.clus

Usage

resclus.plot(x, k=2, col.up="black", col.down=rainbow(k), lty.up=2, lty.down=1, lwd.up=1, lwd.down=1,
               type="rectangle", knot.pos="mean", show.labels=FALSE, only.tree=FALSE, members, ...)

Arguments

x

An object of class "hclust" obtained from res.clus.

k

The number of classes.

col.up

The color to be used for the lines above the cut of the dendrogram. Default "black".

col.down

The colors to be used for the classes below the cut of the dendrogram.

lty.up

The line type above the cut of the dendrogram. Line types are specified as an integer (0=blank, 1=solid (default), 2=dashed, 3=dotted, 4=dotdash, 5=longdash, 6=twodash).

lty.down

The line type for the classes below the cut of the dendrogram.

lwd.up

The line width above the cut of the dendrogram. Line width must be a positive number, defaulting to 1.

lwd.down

The line width for the classes (below the cut of the dendrogram).

type

The type of dendrogram. Can be either "rectangle" or "triangle".

knot.pos

Possition of the knots. Can be "mean", "bary", "left", "right" or "random".

show.labels

Logical value indicating whether the labels of the objects should be printed. Default FALSE.

only.tree

Logical value indicating whether only the dendrogram should be printed. Default FALSE.

members

NULL or a vector with length size of a dissimilarity structure as produced by dist.

...

Further arguments are ignored.

Details

The function resclus.plot displays a dendrogram that helps to visualize the partitions in a dendrogram from res.clus results.

References

Modified version of the functions created by Romain Fracois. Source code available at: http://addictedtor.free.fr/graphiques/RGraphGallery.php?graph=79

See Also

res.clus, it.reb

Examples

## example of rebus analysis with simulated data
  data(sim.data)
  ## First compute GLOBAL model
  sim.mat <- matrix(c(0,0,0,0,0,0,1,1,0),3,3,byrow=TRUE)
  dimnames(sim.mat) <- list(c("Price","Quality","Satisfaction"),
                            c("Price","Quality","Satisfaction"))
  sim.sets <- list(c(1,2,3,4,5),c(6,7,8,9,10),c(11,12,13)) 
  sim.mod <- c("A","A","A")  ## reflective indicators
  sim.global <- plspm(sim.data, sim.mat, sim.sets, sim.mod)
  sim.global
  ## Then compute cluster analysis on the residuals of global model
  sim.res.clus <- res.clus(sim.global)
  ## Plot dendrogram with 4 classes specifying colors
  dev.new()
  resclus.plot(sim.res.clus, k=4, col.up="gray", col.down=c("orange","royalblue","green3","red"))
  ## Plot triangular dendrogram with 4 classes, labels shown, and widther lines
  dev.new()
  resclus.plot(sim.res.clus, k=4, lwd.down=2, col.up="gray", show.labels=TRUE, type="triangle")