Description Usage Arguments Value Note Author(s) References See Also Examples

The Input Data, Local Importance Scores, and Classic Multidimensional Scaling Plots

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`rfprep` |
A list of prepared Random Forests input data to be used in visualization, created using the function rf_prep. |

`input` |
Should the Input Data Parallel Coordinate Plot be included in the visualization? |

`imp` |
Should the Local Importance Scores Parallel Coordinate Plot be included in the visualization? |

`cmd` |
Should the Classic Multidimensional Scaling Proximites 2-D XYZ Scatter Plot be included in the visualization? |

`hl_color` |
The highlight color when you select points on the plot(s). |

Any combination of the parallel coordinate plots of the input data, the local importance scores, and the 2-D XYZ classic multidimensional scaling proximities from the output of the random forest algorithm.

For instructions on how to use randomForests, use ?randomForest. For more information on loon, use ?loon.

For detailed instructions in the use of these plots in this package, visit https://github.com/chrisbeckett8/rfviz/blob/master/Rfviz.md

Chris Beckett chrisbeckett8@gmail.com, based on original Java graphics by Leo Breiman and Adele Cutler.

Liaw A, Wiener M (2002). “Classification and Regression by randomForest.” _R News_, *2*(3), 18-22. https://CRAN.R-project.org/doc/Rnews/

Waddell A, Oldford R. Wayne (2018). "loon: Interactive Statistical Data Visualization" https://github.com/waddella/loon

Breiman, L. (2001), Random Forests, Machine Learning 45(1), 5-32.

Breiman, L (2002), “Manual On Setting Up, Using, And Understanding Random Forests V3.1”, https://www.stat.berkeley.edu/~breiman/Using_random_forests_V3.1.pdf

Breiman, L., Cutler, A., Random Forests Graphics. https://www.stat.berkeley.edu/~breiman/RandomForests/cc_graphics.htm

`randomForest`

, `rf_prep`

, `l_plot`

, `l_serialaxes`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
#Classification with Iris data set
rfprep <- rf_prep(x = iris[,1:4], y = iris$Species)
#View all three plots
Myrfplots <- rf_viz(rfprep, input = TRUE, imp = TRUE, cmd = TRUE, hl_color = 'orange')
#View only the Input Data and CMD Scaling Proximities Plots
Myrfplots <- rf_viz(rfprep, input = TRUE, imp = FALSE, cmd = TRUE, hl_color = 'orange')
#Regression with mtcars data set
rfprep2 <- rf_prep(x = mtcars[,-1], y = mtcars$mpg)
#View all three plots
Myrfplots <- rf_viz(rfprep2, input = TRUE, imp = TRUE, cmd = TRUE, hl_color = 'orange')
``` |

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