Description Usage Arguments Details Author(s) References See Also
plot dendrogram for a pvclust
object and add pvalues for
clusters.
1 2 3 4 5 6 7 8 9  ## 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, ...)

x 
object of class 
print.pv 
logical flag to specify whether print pvalues
around the edges (clusters), or character vector of length 0 to 3
which specifies the names of pvalues to print
( 
print.num 
logical flag to specify whether print edge numbers below clusters. 
float 
numeric value to adjust the height of pvalues from edges. 
col.pv 
named numeric vector to specify the colors for pvalues 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
pvalues and edge numbers. See 
font.pv 
numeric value which specifies the font of characters
for pvalues and edge numbers. See 
col, cex, font 
in 
lty, lwd, main, sub, xlab, ... 
generic graphic parameters. See 
This function plots a dendrogram with pvalues for given object
of class pvclust
.
SI pvalue (printed in blue color in default) is the approximately unbiased
pvalue for selective inference, and
AU pvalue (printed in red color in default) is also the approximately unbiased
pvalue but for nonselective 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
pvalue. 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 0.40 or higher) in scaleboot
package.
Ryota Suzuki suzuki@efprime.com
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 multistepmultiscale bootstrap resampling", Annals of Statistics, 32, 26162641.
Shimodaira, H. (2002) "An approximately unbiased test of phylogenetic tree selection", Systematic Biology, 51, 492508.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.