pi0 (version 1.4-1)

sparncpt: Semiparametric density estimation for noncentrality parameters

Description

Semiparametric density estimation for noncentrality parameters using the combination method of Olkin and Spiegelman (1987), based on fits from both parncpt and nparncpt.

Usage

sparncpt(obj1, obj2, ...)
# S3 method for parncpt
sparncpt(obj1, obj2, ...)
# S3 method for nparncpt
sparncpt(obj1, obj2, ...)
# S3 method for numeric
sparncpt(obj1, obj2, ...)

Arguments

obj1, obj2

Case 1: obj1 and obj2 are of class parncpt and nparncpt respectively; or vice versa; Case 2: obj1 is a numeric vector of t-statistics and obj2 is a vector degrees of freedom

…

other arguments passed to dtn.mix, most notably the approximation argument.

Value

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 logLik. The associated df is the estimated effective number of parameters (enp). The log likelihood is also penalized likelihood. See also logLik.ncpest and AIC.

enp

estimated ENP

par

estimated mixing proportion rho

gradiant

analytic gradiant at the estimate (not implemented)

hessian

analytic hessian at the estimate (not implemented)

parfit

the fitted parncpt object

nparfit

the fitted nparncpt object

nobs

the number of test statistics

Details

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.

References

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.

See Also

parncpt, nparncpt, fitted.sparncpt, plot.sparncpt, summary.sparncpt, coef.ncpest, logLik.ncpest, vcov.ncpest, AIC, dncp

Examples

# 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)
# }