plot.nparncp: plot an object of class nparncpt, i.e., nonparametric...
In pi0: Estimating the proportion of true null hypotheses for FDR

Description Usage Arguments Details Value Author(s) References See Also Examples

Plot the Network information criterion (NIC), effective number of parameters (ENP), and estimated proportion (pi0) of true null hypotheses for different choices of tuning parameters; also plot the estimated density of noncentrality parameters

1 2	## S3 method for class 'nparncpt' plot(x, ...)

`x`	an object of class `nparncpt`
`...`	currently not used.

For NIC, only values within 2 s.e.'s of the minimum are shown. The solid line on NIC, ENP and pi0 shows the final tuning parameter, i.e., the one that minimizes NIC.

Invisible par.

Long Qu

Qu L, Nettleton D, Dekkers JCM. (2012) Improved Estimation of the Noncentrality Parameter Distribution from a Large Number of $t$-statistics, with Applications to False Discovery Rate Estimation in Microarray Data Analysis. Biometrics, 68, 1178–1187.

nparncpt, sparncpt,parncpt

## Not run: 
data(simulatedTstat)
(npfit=nparncpt(tstat=simulatedTstat, df=8, plotit=FALSE)); plot(npfit)
(pfit=parncpt(tstat=simulatedTstat, df=8, zeromean=FALSE)); plot(pfit)
(pfit0=parncpt(tstat=simulatedTstat, df=8, zeromean=TRUE)); plot(pfit0)
(spfit=sparncpt(npfit,pfit)); plot(spfit)

## End(Not run)