Description Usage Arguments Details Value Author(s) References See Also Examples
Semiparametric density estimation for noncentrality parameters using the combination method of Olkin and Spiegelman (1987),
based on fits from both parncpt
and nparncpt
.
1 2 3 4 5 6 7 |
obj1, obj2 |
Case 1: |
... |
other arguments passed to |
This is a two-component mixture of a parametric fit from parncpt
and a nonparametric fit from nparncpt
,
with mixing proportion rho. If obj1
and obj2
are t-statistics and degrees of freedom respectively, calls to each of parncpt
and nparncpt
are made and their results are used in combination.
a list with class c('sparncpt','ncpest')
:
pi0 |
estimated proportion of true nulls |
mu.ncp |
mean of ncp |
sd.ncp |
SD of ncp |
logLik |
an object of class |
enp |
estimated ENP |
par |
estimated mixing proportion |
gradiant |
analytic gradiant at the estimate (not implemented) |
hessian |
analytic hessian at the estimate (not implemented) |
parfit |
the fitted |
nparfit |
the fitted |
nobs |
the number of test statistics |
Long Qu
I. Olkin and C. H. Spiegelman. (1987) A Semiparametric Approach to Density Estimation. Journal of the American Statistical Association. 82,399,858–865
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
,
fitted.sparncpt
, plot.sparncpt
, summary.sparncpt
,
coef.ncpest
, logLik.ncpest
, vcov.ncpest
,
AIC
, dncp
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)
|
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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