partial.dependence.plot {BoomSpikeSlab} | R Documentation |

## Plot a Bayesian Neural Network

### Description

Plot the relationship between Y and a single X variable, averaging over the values of the other X's.

### Usage

```
PartialDependencePlot(model,
which.variable,
burn = SuggestBurn(model),
data.fraction = .2,
gridsize = 50,
mean.only = FALSE,
show.points = TRUE,
xlab = NULL,
ylab = NULL,
ylim = NULL,
report.time = FALSE,
...)
```

### Arguments

`model` |
An object of class |

`which.variable` |
Either an integer denoting the position of the X variable in the data frame used to fit the model, or a character string naming that variable. |

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

`data.fraction` |
The fraction of observations in the predictor matrix to use when constructing the partial dependence plot. A random sub-sample of this fraction will be taken (without replacement) for the purposes of marginalizing over the remaining predictors. |

`gridsize` |
The number of grid points to use on the X axis. |

`mean.only` |
Logical. If |

`show.points` |
If |

`xlab` |
Label for the X axis. NULL produces a default label. Use "" for no label. |

`ylab` |
Label for the Y axis. NULL produces a default label. Use "" for no label. |

`ylim` |
Limits on the vertical axis. If NULL then the plot will default to its natural vertical limits. |

`report.time` |
Print the time required to produce the plot. |

`...` |
Extra arguments are passed either to 'plot' (if mean.only
is |

### Details

A partial dependence plot shows the relationship between Y and a single X variable, averaging over the values of the other X's in a possibly nonlinear regression model. Partial dependence plots are a generalization of the "added variable plot" idea from linear regression models.

A partial dependence plot is more expensive to produce than most other plots, because a set of predictions must be generated at each point on the X axis. This is done by taking a random subset of the training data, and evaluating the posterior predictive distribution with each observation's target X value set to each value of X on the grid.

### Author(s)

Steven L. Scott

### See Also

### Examples

```
# Please see the code in ?BayesNnet
```

*BoomSpikeSlab*version 1.2.6 Index]