splitDensity {bartMan} | R Documentation |

## splitDensity

### Description

Density plots of the split value for each variable.

### Usage

```
splitDensity(
trees,
data,
bandWidth = NULL,
panelScale = NULL,
scaleFactor = NULL,
display = "histogram"
)
```

### Arguments

`trees` |
A list of trees created using the trees function. |

`data` |
Data frame containing variables from the model. |

`bandWidth` |
Bandwidth used for density calculation. If not provided, is estimated from the data. |

`panelScale` |
If TRUE, the default, relative scaling is calculated separately for each panel. If FALSE, relative scaling is calculated globally. @param scaleFactor A scaling factor to scale the height of the ridgelines relative to the spacing between them. A value of 1 indicates that the maximum point of any ridgeline touches the baseline right above, assuming even spacing between baselines. |

`scaleFactor` |
A numerical value to scale the plot. |

`display` |
Choose how to display the plot. Either histogram, facet wrap, ridges or display both the split value and density of the predictor by using dataSplit. |

### Value

A faceted group of density plots

### Examples

```
if(requireNamespace("dbarts", quietly = TRUE)){
# Load the dbarts package to access the bart function
library(dbarts)
# Get Data
df <- na.omit(airquality)
# Create Simple dbarts Model For Regression:
set.seed(1701)
dbartModel <- bart(df[2:6], df[, 1], ntree = 5, keeptrees = TRUE, nskip = 10, ndpost = 10)
# Tree Data
trees_data <- extractTreeData(model = dbartModel, data = df)
splitDensity(trees = trees_data, data = df, display = 'ridge')
}
```

*bartMan*version 0.1.1 Index]