Description Usage Arguments Details Value See Also Examples

The function finds the posterior probabilities ofeach vector of association status for each feature, given the feature's vector of binned z-scores.

1 |

`pdf.binned.z` |
Same input as in |

`binned.z.mat` |
Same input as in |

`Pi` |
The estimated prior probabilities for each association status vector. Can be extracted from the output of |

`h.vecs` |
The row indices in |

A subset of features (e.g most significant) can be specified as the rows in `binned.z.mat`

, so the posterior probabilities of the vectors of association status are computed for this subset of features. See Example section.

Matrix with rows that contain for each of the vectors of association status the posterior probabilities. The columns are the different feature.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
## 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")
data(Pi)
# Fdr calculation:
output3 <- repfdr(pbz, bz, "replication",Pi.previous.result = Pi)
BayesFdr <- output3$mat[,"Fdr"]
sum(BayesFdr <= 0.05)
# The posterior probabilities for the the first five features with Bayes FDR at most 0.05:
post <- ldr(pbz,bz[which(BayesFdr <= 0.05)[1:5],],Pi)
round(post,4)
# posteriors for a subset of the association status vectors can also be reported,
# here the subset is the four first association status vectors:
post <- ldr(pbz,bz[which(BayesFdr <= 0.05)[1:5],],Pi,h.vecs= 1:4)
round(post,4)
## End(Not run)
``` |

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