plot-methods | R Documentation |
Diagnostics and p-value plots from a sigclust object.
## S4 method for signature 'sigclust,missing' plot(x,y,arg="all",...)
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 |
SigClust diagnostic plots are suggested to monitor the performance of the SigClust method for a given dataset.
Hanwen Huang: hanwenh@email.unc.edu; Yufeng Liu: yfliu@email.unc.edu; J. S. Marron: marron@email.unc.edu
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.
sigclust
.
## 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)
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