LFDR.MLE (version 1.0.1)

lfdr.mle: Type II Maximum likelihood estimate of LFDR (LFDR-MLE).

Description

Estimates the local false discovery rate by the Type II maximum likelihood estimates (MLE).

Usage

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

Arguments

x

Input numeric vector of statistics.

dFUN

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

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

d0

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.

%% \item{comp2 }{Description of 'comp2'} %% ...

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.

See Also

lfdr.mdl, lfdr.l1o, lfdr.lho.

Examples

# NOT RUN {
#numeric imput data: statistics of the data with missing values (removed internally)
#(result of a absolute t.test statistics on the data)
dfx <- 4;n.alt <- 1;n.null <- 4;true.ncp <- 7
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.mle(x=W,dFUN=dabsTd, df=dfx)
z2<-lfdr.mle(x=W,dFUN=dabsTd, df=dfx, fixed.p0=0.4, fixed.ncp=4)

#other dFUN -------
#NOTE: arguments for dFUN are x, df, ncp. If dFUN has other arguments, 
#please adapt them. For example:

new.df<-function(x,df,ncp,...){df(x=x,ncp=ncp,df1=df,...)}
z3<-lfdr.mle(x=W,df=dfx,dFUN=new.df,df2=5)
# }
# NOT RUN {
# }