plot.parncp: plot an object of class parncpt, i.e., parametric estiamte of...
In pi0: Estimating the Proportion of True Null Hypotheses for FDR

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

Plot the histogram of observed t-statistics together with its fitted density estimate; also plotted is the estimated density of noncentrality parameters.

## S3 method for class 'parncpt'
plot(x,...)
## S3 method for class 'parncpt2'
plot(x,...)

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

Left panel shows the density estimate of observed t-statistics, overlapped with a histogram; right panel shows the estimated density of noncentrality parameters. Solid line is the actual mean of the estimate; dashed line is located at zero.

the invisible x itself

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.

parncpt, nparncpt, sparncpt

## Not run: 
data(simulatedTstat)
(npfit=nparncpt(tstat=simulatedTstat, df=8)); 
(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)

pi0= 0.7483634
mu.ncp= -0.02254265
sd.ncp= 1.523897
enp= 2.408478
lambda= 100
Warning message:
In nparncpt.sqp(tstat, df, ...) :
  Less than half of the estimated coefficients (betas) are less than 0.01. Your might want to try enlarging the `bounds` argument.
pi0 (proportion of null hypotheses) = 0.7483103
mu.ncp (mean of noncentrality parameters) = -0.03791745
sd.ncp (SD of noncentrality parameters) = 1.624555
pi0 (proportion of null hypotheses) = 0.7486391
mu.ncp (mean of noncentrality parameters) = 0
sd.ncp (SD of noncentrality parameters) = 1.626181
pi0= 0.7483134
mu.ncp= -0.03704109
sd.ncp= 1.534416
rho= 0.943
enp= 3.966283