Plot a `logit.spike`

object. The default plot is a
barplot of the marginal inclusion probabilities for each variable,
as obtained by `PlotMarginalInclusionProbabilities`

.
See below for other types of plots.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
## S3 method for class 'logit.spike'
plot(x,
y = c("inclusion", "coefficients", "scaled.coefficients", "fit",
"residuals", "size", "help"),
burn = SuggestBurnLogLikelihood(x$log.likelihood),
...)
## S3 method for class 'probit.spike'
plot(x,
y = c("inclusion", "coefficients", "scaled.coefficients", "fit",
"residuals", "size", "help"),
burn = SuggestBurnLogLikelihood(x$log.likelihood),
...)
``` |

`x` |
An object of class |

`y` |
The type of plot desired. |

`burn` |
The number of MCMC iterations to discard as burn-in. |

`...` |
Additional arguments passed to the specific functions that do the plotting. |

The default plot is a barplot showing the marginal inclusion
probabilities of the coefficients, constructed using
`PlotMarginalInclusionProbabilities`

.

The plot of the fit summary is handled by
`PlotLogitSpikeFitSummary`

.

The plot of the residuals is handled by
`PlotLogitSpikeResiduals`

.

The plot of model size is handled by `PlotModelSize`

.

Steven L. Scott

`PlotMarginalInclusionProbabilities`

`PlotModelSize`

`PlotLogitSpikeFitSummary`

`PlotLogitSpikeResiduals`

1 | ```
## See the examples in ?logit.spike
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.