lfdr.mdl: Minimum description length (MDL) method for estimation of LFDR.

Description

Estimate the local false discovery rate using the minimum description length (MDL) method.

Usage

lfdr.mdl(x, dFUN = dabsTd, lower.ncp = 0.001, upper.ncp = 20, lower.p0 = 0,
        upper.p0 = 1, fixed.p0 = NULL, fixed.ncp = NULL, d0 = 0, ...)
lfdr.mdlo(x, v = 0, dFUN = dabsTd, lower.ncp = 0.001, upper.ncp = 20, 
        lower.p0 = 0, upper.p0 = 1, fixed.p0 = NULL, fixed.ncp = NULL,
        d0 = 0, ...)

Arguments

Input numeric vector of statistics.

dFUN

Density function; default dabsTd (from absolute value of the Student t distribution)

v in [0,1], portion of one feature that is considered in turn to estimate the LFDR; default value is v=0 (for lfdr.l1o (L1O)) and v=1/2 (for lfdr.lho (LHO)). For other values of v, use lfdr.lo.

lower.ncp

The lowerbound of the location parameter of dFUN (noncentrality parameter for default dFUN=dabsTd); default value is 0.001

upper.ncp

The upperbound of the location parameter of dFUN (noncentrality parameter for default dFUN=dabsTd); default value is 20

lower.p0

The lowerbound of p0 (proportion of unaffected features (null hypothesis)); default value is 0

upper.p0

The upperbound of p0 (proportion of unaffected features (null hypothesis)); default value is 1

fixed.p0

A fixed value of p0 (proportion of unaffected features (null hypothesis)); default value is NULL

fixed.ncp

A fixed value of the location parameter of dFUN (noncentrality parameter for default dFUN=dabsTd); default value is NULL

the numeric value of the null hypothesis for dFUN, default value is 0.

…

Other parameters to pass to dFUN (see notes and examples).

Value

A list with:

LFDR.hat

estimates of the LFDR

p0.hat

estimate of the proportion of unaffected features p0 (true null hypothesis).

ncp.hat

estimate of the location parameter of the distribution dFUN (ncp: noncentrality parameter of dFUN=dabsTd by default).

info

method name and information about computation failure.

References

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

Run this code

# NOT RUN {
dfx <- 4;n.alt <- 1;n.null <- 4;true.ncp <- 7
#numeric imput data: statistics of the data with missing values (removed internally)
#(result of a absolute t.test statistics on the data)
W<-abs(c(rt(n=n.alt,ncp=true.ncp,df=dfx),rt(n=n.null,ncp=0,df=dfx)))
W[3]<-NA

z1<-lfdr.mdl(x=W,df=dfx)
z2<-lfdr.mdl(x=W,df=dfx,fixed.ncp=10)
z3<-lfdr.mdlo(x=W,df=dfx,v=1/3,fixed.p0=0.3)
# }