cor.fit.mixture {GeneTS}R Documentation

Fit Mixture Distribution to Estimated Correlation Coefficients

Description

cor.fit.mixture fits a mixture model

f(r) = eta0 dcor0(r, kappa) + (1-eta0) etaA fA

to a vector of empirical partial correlation coefficients. This allows to estimate both the degree of freedom kappa in the null-distribution and the proportion eta0 of null r-values. The alternative distribution may be any arbitrary nonparametric distribution which vanishes for values of r near the center r=0.

cor.fit.mixture also computes

etaA fA/f(r),

i.e. the (empirical) posterior probability that the true correlation is non-zero given the empirical correlation r, the degree of freedom of the null-distribution kappa, and the prior eta0 for the null-distribution.

Usage

cor.fit.mixture(r, df=7, plot.locfdr=0)

Arguments

r vector of estimated correlations
df degrees of freedom for the spline fitting the density
plot.locfdr controls plot option in locfdr

Details

The above functions are useful to determine the null-distribution of edges in a sparse graphical Gaussian model, see Schaefer and Strimmer (2005a,b) for more details and an application to infer genetic networks from microarray data.

For details on how to fit the empirical null distribution while at the same time non-parametrically estimating the alternative hypothesis see Efron (2004) and the associated R package locfdr.

Value

A list object with the following components:

kappa the degree of freedom of the null distribution (see dcor0)
eta0 the prior for the null distribution, i.e. the proportion of null r-values
prob.nonzero empirical posterior probability that the observed correlations are non-zero.

Author(s)

Juliane Schaefer (http://www.stat.math.ethz.ch/~schaefer/) and Korbinian Strimmer (http://www.statistik.lmu.de/~strimmer/).

References

Efron, B. (2004). Large-scale simulataneous hypothesis testing: the choice of a null hypothesis. JASA 99:96-104.

Schaefer, J., and Strimmer, K. (2005a). An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21:754-764.

Schaefer, J., and Strimmer, K. (2005). A shrinkage approach to large-scale covariance estimation and implications for functional genomics. Statist. Appl. Genet. Mol. Biol. 4:32. (http://www.bepress.com/sagmb/vol4/iss1/art32/)

See Also

dcor0, kappa2n, fdr.estimate.eta0.

Examples

# load GeneTS library
library("GeneTS")

# simulate mixture distribution
r <- rcor0(700, kappa=10)
u1 <- runif(200, min=-1, max=0.5)
u2 <- runif(200, min=0.5, max=1)
rc <- c(r, u1, u2)

# estimate kappa and eta0 (=7/9)
c1 <- cor.fit.mixture(r)
c1$eta0
c1$kappa
c2 <- cor.fit.mixture(rc) 
c2$eta0
c2$kappa


[Package GeneTS version 2.10.1 Index]