pi0 (version 1.4-1)

plot.nparncpt: plot an object of class nparncpt, i.e., nonparametric estiamte of noncentrality parameters

Description

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

Usage

# S3 method for nparncpt
plot(x, ...)

Arguments

x

an object of class nparncpt

…

currently not used.

Value

Invisible par.

Details

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.

References

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.

See Also

nparncpt, sparncpt,parncpt

Examples

# 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)
# }