R: SigClust plot

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 `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.

### 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

`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)

[Package sigclust version 1.1.0 Index]