| plot-methods {sigclust} | R Documentation |
SigClust plot
Description
Diagnostics and p-value plots from a sigclust object.
Usage
## S4 method for signature 'sigclust,missing'
plot(x,y,arg="all",...)
Arguments
x |
An object of class |
y |
not used |
arg |
Type of the individual plot: "background": make background standard deviation diagnostic plots. These plots contain the raw data points as well as the corresponding density plots using kernel and robust Gaussian fits; "qq": the QQ plot assessing the quality of robust fit of a Gaussian distribution; "diag": make a null distribution covariance estimation diagnostic plot; "pvalue": make a clustering significance pvalue plot; "all": make all above plots (default). |
... |
further arguments for |
Details
SigClust diagnostic plots are suggested to monitor the performance of the SigClust method for a given dataset.
Author(s)
Hanwen Huang: hanwenh@email.unc.edu; Yufeng Liu: yfliu@email.unc.edu; J. S. Marron: marron@email.unc.edu
References
Liu, Yufeng, Hayes, David Neil, Nobel, Andrew and Marron, J. S, 2008, Statistical Significance of Clustering for High-Dimension, Low-Sample Size Data, Journal of the American Statistical Association 103(483) 1281–1293. See also the vignette included with this package.
See Also
Examples
## Simulate a dataset from a collection of mixtures of two
## multivariate Gaussian distributions with different means.
mu <- 5
n <- 30
p <- 500
dat <- matrix(rnorm(p*2*n),2*n,p)
dat[1:n,1] <- dat[1:n,1]+mu
dat[(n+1):(2*n),1] <- dat[(n+1):(2*n),1]-mu
nsim <- 1000
nrep <- 1
icovest <- 3
pvalue <- sigclust(dat,nsim=nsim,nrep=nrep,labflag=0,icovest=icovest)
#sigclust plot
plot(pvalue)