sparncp: Semiparametric density estimation for noncentrality...
In gitlongor/pi0: Estimating the Proportion of True Null Hypotheses for FDR

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.

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

`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.

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 `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

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

## 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)