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

View source: R/hrunbiasedDiagnostic.r

Plot function for hrunbiasedDiagnostic objects

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`x` |
Object of class |

`diagnostic.plot` |
At least one of |

`id.size` |
For class |

`show.stat` |
For |

`legend.out` |
If TRUE, an automatic legend is shown in the plot |

`perms.to.show` |
For |

`seq.alpha` |
For |

`seq.alpha.pow` |
For |

`power.whplots` |
For class |

`col1` |
For |

`col2` |
For |

`pointsq` |
If |

`...` |
Arguments passed to or from other methods to the low level. |

Following details section in `hrunbiasedDiagnostic`

,
module (i) contains `"density.rs"`

: hazard ratio
t-statistic density plot obtained from random signatures;
`"density.rs.size"`

: density plot for hazard ratio t-statistic as
function of signature size; `"geneMean.geneSign"`

: average log HR
genewise in a signature vs log HR in the signature. Module (ii) contains
`"perm.violin"`

: hazard ratio violin plots obtained from random
signatures for several instances with a shuffle in the time-to-even
outcome; `"perm.GSvsEvents"`

: global signature boxplots that
distinguish between event and not event; `"perms.corr.GS"`

:
relationship between GS event and not event average difference and
observed average lHR. Module (iii) contains `"positive.cont.power"`

:
power curves using positive control random signatures;
`"bias.power"`

: two y-axis plot showing average values for the hazard
ratio t-statistic and power given several charactarizations of negative
controls; `"corNCsig.power"`

: two y-axis plot showing correlation to
positive controls and power of several charactarizations of negative
controls; Module (iv) contains `"simulations"`

: random signatures
hazard ratio distributions for simulated time-to-event data.

plot of object of class `hrunbiasedDiagnostic`

.

Adria Caballe Mestres

Caballe Mestres A, Berenguer Llergo A and Stephan-Otto Attolini C. Adjusting for systematic technical biases in risk assessment of gene signatures in transcriptomic cancer cohorts. bioRxiv (2018).

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