mdsBart {bartMan} | R Documentation |

## mdsBart

### Description

Multi-dimensional Scaling Plot of proximity matrix from a BART model.

### Usage

```
mdsBart(
trees,
data,
target,
response,
plotType = "rows",
showGroup = TRUE,
level = 0.95
)
```

### Arguments

`trees` |
A data frame created by 'extractTreeData' function. |

`data` |
a dataframe used in building the model. |

`target` |
A target proximity matrix to |

`response` |
The name of the response for the fit. |

`plotType` |
Type of plot to show. Either 'interactive' - showing interactive confidence ellipses. 'point' - a point plot showing the average position of a observation. 'rows' - displaying the average position of a observation number instead of points. 'all' - show all observations (not averaged). |

`showGroup` |
Logical. Show confidence ellipses. |

`level` |
The confidence level to show. Default is 95% confidence level. |

### Value

For this function, the MDS coordinates are calculated for each iteration. Procrustes method is then applied to align each of the coordinates to a target set of coordinates. The returning result is then a clustered average of each point.

### 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)
# Cretae Porximity Matrix
bmProx <- proximityMatrix(
trees = trees_data,
reorder = TRUE,
normalize = TRUE,
iter = 1
)
# MDS plot
mdsBart(
trees = trees_data, data = df, target = bmProx,
plotType = "interactive", level = 0.25, response = "Ozone"
)
}
```

*bartMan*version 0.1.1 Index]