sigclust (version 1.1.0)

plot-methods: SigClust plot

Description

Diagnostics and p-value plots from a sigclust object.

Usage

"plot"(x,y,arg="all",...)

Arguments

x

An object of class sigclust.

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 plot.

Details

SigClust diagnostic plots are suggested to monitor the performance of the SigClust method for a given dataset.

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

sigclust.

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)