plot dendrogram for a pvclust object and add \(p\)-values for
clusters.
# S3 method for 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 pvclust
text(x, col=c(au=2, bp=3, edge=8), print.num=TRUE,
float=0.01, cex=NULL, font=NULL, ...)
object of class pvclust, which is generated by
function pvclust. See pvclust for details.
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).
logical flag to specify whether print edge numbers below clusters.
numeric value to adjust the height of \(p\)-values from edges.
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.
numeric value which specifies the size of characters for
\(p\)-values and edge numbers. See cex argument for
par.
numeric value which specifies the font of characters
for \(p\)-values and edge numbers. See font argument for
par.
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.
generic graphic parameters. See par for
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.
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.