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.
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an object of class |
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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
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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.
1 2 3 4 5 6 7 8 | ## 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)
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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
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