plot.pvclust: Draws Dendrogram with P-values for Pvclust Object

In pvclust: Hierarchical Clustering with P-Values via Multiscale Bootstrap Resampling

Description

plot dendrogram for a pvclust object and add p-values for clusters.

Usage

## S3 method for class 'pvclust'
plot(x, print.pv=TRUE, print.num=TRUE, float=0.01,
  col.pv=c(si=4, au=2, bp=3, edge=8), cex.pv=0.8, font.pv=NULL,
  col=NULL, cex=NULL, font=NULL, lty=NULL, lwd=NULL, main=NULL,
  sub=NULL, xlab=NULL, ...)

## S3 method for class 'pvclust'
text(x, col=c(au=2, bp=3, edge=8), print.num=TRUE,
  float=0.01, cex=NULL, font=NULL, ...)

Arguments

x	object of class pvclust, which is generated by function pvclust. See pvclust for details.
print.pv	logical flag to specify whether print p-values around the edges (clusters), or character vector of length 0 to 3 which specifies the names of p-values to print (c("si", "au", "bp") for example).
print.num	logical flag to specify whether print edge numbers below clusters.
float	numeric value to adjust the height of p-values from edges.
col.pv	named numeric vector to specify the colors for p-values and edge numbers. For back compatibility it can also be unnamed numeric vector of length 3, which corresponds to the color of AU, BP values and edge numbers.
cex.pv	numeric value which specifies the size of characters for p-values and edge numbers. See cex argument for par.
font.pv	numeric value which specifies the font of characters for p-values and edge numbers. See font argument for par.
col, cex, font	in text function, they correspond to col.pv, cex.pv and font.pv in plot function, respectively. In plot function they are used as generic graphic parameters.
lty, lwd, main, sub, xlab, ...	generic graphic parameters. See par for details.

Details

This function plots a dendrogram with p-values for given object of class pvclust. SI p-value (printed in blue color in default) is the approximately unbiased p-value for selective inference, and AU p-value (printed in red color in default) is also the approximately unbiased p-value but for non-selective inference. They ared calculated by multiscale bootstrap resampling. BP value (printed in green color in default) is "bootstrap probability" value, which is less accurate than AU value as p-value. One can consider that clusters (edges) with high SI or AU values (e.g. 95%) are strongly supported by data. SI value is newly introduced in Terada and Shimodaira (2017) for selective inference, which is more appropriate for testing clusters identified by looking at the tree. AU value has been used since Shimodaira (2002), which is not designed for selective inference. AU is valid when you know the clusters before looking at the data. See also documatation (Multiscale Bootstrap using Scaleboot Package, verison 0.4-0 or higher) in scaleboot package.

Author(s)

Ryota Suzuki [e-mail]

References

Terada, Y. and Shimodaira, H. (2007) "Selective inference for the problem of regions via multiscale bootstrap", arXiv:1711.00949.

Shimodaira, H. (2004) "Approximately unbiased tests of regions using multistep-multiscale bootstrap resampling", Annals of Statistics, 32, 2616-2641.

Shimodaira, H. (2002) "An approximately unbiased test of phylogenetic tree selection", Systematic Biology, 51, 492-508.

See Also

text.pvclust

pvclust documentation built on Nov. 19, 2019, 5:07 p.m.