Description Usage Arguments Details Value See Also Examples

For each study, the function discretizes the z-scores into bins and estimates the probabilities in each bin for the null and non-null states.

The function can plot diagnostic plots (disabled by default) for model fit. These should be monitored for misfit of model to data, before using function output in `repfdr`

. See description of diagnostic plots below.

1 2 3 4 5 6 |

`zmat` |
Matrix of z-scores of the features (in rows) in each study (columns). |

`n.association.status` |
either 2 for no-association\association or 3 for no-associtation\negative-association\positive-association. |

`n.bins` |
Number of bins in the discretization of the z-score axis (the number of bins is |

`type` |
Type of fitting used for f; 0 is a natural spline, 1 is a polynomial, in either case with degrees of freedom |

`df` |
Degrees of freedom for fitting the estimated density f(z). |

`central.prop` |
Central proportion of the z-scores used like the area of zero-assumption to estimate pi0. |

`pi0` |
Sets argument for estimation of proportion of null hypotheses. Default value is NULL (automatic estimation of pi0) for every study. Second option is to supply vector of values between 0 and 1 (with length of the number of studies/ columns of |

`plot.diagnostics` |
If set to A second plot is the Normal Q-Q plot of Zscores, converted using Misfit in these two plots should be investigated by the user, before using output in Default value is |

`trim.z` |
If set to |

`trim.z.upper` |
Upper bound for trimming Z scores. Default value is 8 |

`trim.z.lower` |
Lower bound for trimming Z scores. Default value is -8 |

`force.bin.number` |
Set to |

`pi.using.plugin` |
Logical flag indicating whether estimation of the number of null hypotheses should be done using the plugin estimator.(Default is |

`pi.plugin.lambda` |
Parameter used for estimation of proportion of null hypotheses, for one sided tests. Default value is 0.05. This should be set to the type 1 error used for hypothesis testing. |

This utility function outputs the first two arguments to be input in the main function `repfdr`

.

A list with:

`pdf.binned.z` |
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. |

`binned.z.mat` |
A matrix of the bin numbers for each the z-scores (rows) in each study (columns). |

`breaks.matrix` |
A matrix with |

`df` |
Number of degrees of freedom, used for spline fitting of density. |

`proportions` |
Matrix with |

`PlotWarnings` |
Vector of size |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | ```
# Simulated example using both the central proportion estimator
# and the plug in estimator for the proportion of null hypotheses:
set.seed(1)
p = 10000
p1 = 300
z1 = (rnorm(p))
z2 = (rnorm(p))
temp = rnorm(p1, 3.5,0.5)
z1[1:p1] = temp + rnorm(p1,0,0.2)
z2[1:p1] = temp + rnorm(p1,0,0.2)
z1.abs = abs(z1)
z2.abs = abs(z2)
plot(z1,z2)
hist(z1)
hist(z2)
zmat.example = cbind(z1,z2)
ztobins.res = ztobins(zmat.example,
plot.diagnostics = TRUE)
ztobins.res$proportions
ztobins.res.plugin.estimator = ztobins(zmat.example,
pi.using.plugin = TRUE,
plot.diagnostics = TRUE)
ztobins.res.plugin.estimator$proportions
## Not run:
# three association states case (H in {-1,0,1}):
download.file('http://www.math.tau.ac.il/~ruheller/repfdr_RData/zmat.RData',destfile = "zmat.RData")
load(file = "zmat.RData")
input.to.repfdr3 <- ztobins(zmat, 3, df = 15)
pbz <- input.to.repfdr3$pdf.binned.z
bz <- input.to.repfdr3$binned.z.mat
# two association states case (H in {0,1}):
data(zmat_sim)
input.to.repfdr <- ztobins(zmat_sim, 2, n.bins = 100 ,plot.diagnostics = T)
pbz_sim <- input.to.repfdr$pdf.binned.z
bz_sim <- input.to.repfdr$binned.z.mat
## End(Not run)
``` |

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