| LFDR.MLE-package {LFDR.MLE} | R Documentation |
Estimation of the Local False Discovery Rate using Type II Maximum Likelihood Estimation.
Description
Suite of R functions for the estimation of the local false discovery rate (LFDR) using Type II maximum likelihood estimation (MLE).
Details
| Package: | LFDR.MLE |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2015-07-30 |
| License: | GPL-3 |
| Depends: | R (>= 2.14.0), stats, methods |
| URL: | http://www.cran.r-project.org, http://www.statomics.com |
Author(s)
Code: Ye Yang, Marta Padilla, Zhenyu Yang, Zuojing Li, Corey M. Yanofsky
Documentation: Alaa Ali, Kyle Leckett, Marta Padilla
Maintainer: D. R. Bickel <dbickel@uOttawa.ca>
References
Yang, Y., & Bickel, D. R. (2010). Minimum description length and empirical Bayes methods of identifying SNPs associated with disease. Technical Report, Ottawa Institute of Systems Biology, COBRA Preprint Series, Article 74, available at biostats.bepress.com/cobra/ps/art74.
Bickel, D. R. (2010). Minimum description length methods of medium-scale simultaneous inference. arXiv preprint arXiv:1009.5981.
Padilla, M., & Bickel, D. R. (2012). Estimators of the local false discovery rate designed for small numbers of tests. Statistical Applications in Genetics and Molecular Biology, 11(5), art. 4.
Examples
dFUN<-dabsTd; dfx <- 4
n.alternative <- 3; n.null <- 4; true.ncp <- 7
#NOTE: arguments for dFUN are x, df, ncp. If dFUN has other arguments,
#please adapt them. For example:
#dFUN<-function(x,df,ncp){dnorm(x=x,mean=ncp,sd=df)};attr(dFUN,'name')<-'dnorm'
#In the examples, W is the result of the absolute value of the t.test statistics
#on the data
W<-abs(c(rt(n=n.alternative,ncp=true.ncp,df=dfx),rt(n=n.null,ncp=0,df=dfx)))
W[c(1,3,5)]<-NA
z1<-lfdr.mle(x=W,dFUN=dFUN, df=dfx)
z2<-lfdr.mle(x=W,dFUN=dFUN, df=dfx,fixed.p0=0.4,fixed.ncp=4)
z3<-lfdr.mdl(x=W,df=dfx,dFUN=dFUN)
z4<-lfdr.l1o(x=W,df=dfx,dFUN=dFUN)
z5<-lfdr.lho(x=W,df=dfx,dFUN=dFUN,fixed.ncp=5)
z6<-lfdr.lo(x=W,v=1/3,df=dfx,dFUN=dFUN)
z7<-lfdr.mdlo(x=W,v=1/3,df=dfx,dFUN=dFUN)