1d:T7e8,# Estimation of the Local False Discovery Rate using Type II Maximum Likelihood Estimation. Suite of R functions for the estimation of the local false discovery rate (LFDR) using Type II maximum likelihood estimation (MLE). package ## 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 ## 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 ```r 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) ```

LFDR.MLE-package