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

View source: R/GroupBootPlot.R

This function plots output from bootstrapped networks computed with GroupNetworkBoot.

1 2 3 | ```
GroupBootPlot(BootOut, GroupNames, edges.x, edges.y,
labels = TRUE, transparency = 0.15, point.size = 1.5, line.size = 1, scales = "fixed",
legend.position = "none", GroupNamesCheck = FALSE)
``` |

`BootOut` |
The output from GroupNetworkBoot. |

`GroupNames` |
A vector of optional group names that will be printed as facet labels in plot. By default, names of the networks are taken. If specified, GroupNames should match the alphabetical order of names of network groups. If unsure, you can check the matching of names by setting |

`edges.x` |
If only a subset of edge combinations is of interest for the plot, this subset can be specified by setting |

`edges.y` |
See |

`labels` |
Logical, should edge labels be included in plots. Default is |

`transparency` |
Set ggplot2 alpha channel (transparency) for confidence interval ribbon in plot. |

`point.size` |
Set point size. |

`line.size` |
Set line size. |

`scales` |
Set ggplot2 facet scales. Default is |

`legend.position` |
Define legend position to indicate colour for sample and bootstrap means. See ?theme in ggplot2. |

`GroupNamesCheck` |
Option to print match of indicated GroupNames to console. Only prints if GroupNames is specified. See |

The code for the Joint Graphical Lasso procedure was adapted from the R package JGL. Some of the code for the cross-validation procedure was adapted from package parcor. Some of the code was inspired by package qgraph. GroupBootPlot automatically calls BootTable to format GroupNetworkBoot output, so see BootTable for completely independent plotting.

The output of GroupBootPlot returns a plot based on ggplot2 with the bootstrapped confidence intervals of edges across groups.

Nils Kappelmann <n.kappelmann@gmail.com>, Giulio Costantini

Danaher, P., Wang, P., and Witten, D. M. (2014). The joint graphical lasso for inverse covariance estimation across multiple classes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(2), 373-397. http://doi.org/10.1111/rssb.12033

JGL, qgraph, parcor

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