piem: Estimation of the prior probabilities for each association...
In repfdr: Replicability Analysis for Multiple Studies of High Dimension

Description Usage Arguments Details Value Author(s) References See Also Examples

The function calls an expectation-maximization (EM) algorithm to estimate the prior probabilities of each association status vector. It is also used internally in repfdr.

1	piem(pdf.binned.z, binned.z.mat, control = em.control())

`pdf.binned.z`	Same input as in `repfdr`. A 3-dimensional array which contains for each study (first dimension), the probabilities of a z-score to fall in the bin (second dimension), under each hypothesis status (third dimension). The third dimension can be of size 2 or 3, depending on the number of association states: if the association can be either null or only in one direction, the dimension is 2; if the association can be either null, or positive, or negative, the dimension is 3. Element `[[1]]` in the output of `ztobins`.
`binned.z.mat`	Same input as in `repfdr`. A matrix of the bin numbers for each the z-scores (rows) in each study (columns). Element `[[2]]` in the output of `ztobins`.
`control`	List of control parameters to pass to the EM algorithm. See `em.control`.

The implementation of the EM algorithm is in C, and allows paralel processing. By default, the software automatically detects the number of available processing threads. See em.control for the option of providing the number of threads to use, as well as for the additional control parameters.

`all.iterations`	Matrix with number of columns equal to the number of EM iterations, and each column is the estimated probability distribution of the vector of association status.
`last.iteration`	Matrix of the vectors of association status along with the column vector of the last EM iteration, which contains the estimated probabilities of the vectors of association status.

C implementation by Shachar Kaufman.

Heller, Ruth, and Daniel Yekutieli. "Replicability analysis for Genome-wide Association studies." arXiv preprint arXiv:1209.2829 (2012).

repfdr

## Not run: 

download.file('http://www.math.tau.ac.il/~ruheller/repfdr_RData/binned_zmat.RData',
  destfile = "binned_zmat.RData")
load(file = "binned_zmat.RData")
#binned_zmat can also be generated via 
output_piem <- piem(pbz, bz)

# extract the last iteration to use it in repfdr (see help(repfdr)):
Pi1 <- output_piem$last.iteration
data(Pi)
stopifnot(all.equal(Pi,Pi1))

# simulation data:
data(binned_zmat_sim)
output_piem_sim <- piem(pbz_sim, bz_sim)
Pi_sim <- output_piem_sim$last.iteration

# following are the true proportions in the data: (see help(hmat_sim) for data generation details.)
f <- c(0.895,0.005,0.005,0.02,0.005,0.02,0.02,0.03) 

# the estimation vs the true proportions:
cbind(round(Pi_sim,6),f)

## End(Not run)