Recursive Partitioning and Regression Trees

Fit and plot a rpart model for exploratory purposes using rpart and rpart.plot libraries.

Usage

tree_var(
  df,
  y,
  type = 2,
  max = 3,
  min = 20,
  cp = 0,
  ohse = TRUE,
  plot = TRUE,
  explain = TRUE,
  title = NA,
  subtitle = NULL,
  ...
)

Arguments

df

Data frame

y

Variable or Character. Name of the dependent variable or response.

type

Type of plot. Possible values:

0 Draw a split label at each split and a node label at each leaf.

1 Label all nodes, not just leaves. Similar to text.rpart's all=TRUE.

2 Default. Like 1 but draw the split labels below the node labels. Similar to the plots in the CART book.

3 Draw separate split labels for the left and right directions.

4 Like 3 but label all nodes, not just leaves. Similar to text.rpart's fancy=TRUE. See also clip.right.labs.

5 Show the split variable name in the interior nodes.

max

Integer. Maximal depth of the tree.

min

Integer. The minimum number of observations that must exist in a node in order for a split to be attempted.

cp

complexity parameter. Any split that does not decrease the overall lack of fit by a factor of cp is not attempted. For instance, with anova splitting, this means that the overall R-squared must increase by cp at each step. The main role of this parameter is to save computing time by pruning off splits that are obviously not worthwhile. Essentially,the user informs the program that any split which does not improve the fit by cp will likely be pruned off by cross-validation, and that hence the program need not pursue it.

ohse

Boolean. Auto generate One Hot Smart Encoding?

plot

Boolean. Return a plot? If not, rpart object.

explain

Boolean. Include a brief explanation on the bottom part of the plot.

title, subtitle

Character. Title and subtitle to include in plot. Set to NULL to ignore.

...

Additional parameters passed to rpart.plot().

Value

(Invisible) list type 'tree_var' with plot (function), model, predictions, performance metrics, and interpret auxiliary text.

Details

This differs from the tree function in S mainly in its handling of surrogate variables. In most details it follows Breiman et. al (1984) quite closely. R package tree provides a re-implementation of tree.

References

Breiman L., Friedman J. H., Olshen R. A., and Stone, C. J. (1984) Classification and Regression Trees. Wadsworth.

See also

Other Exploratory: corr_cross(), corr_var(), crosstab(), df_str(), distr(), freqs_df(), freqs_list(), freqs_plot(), freqs(), lasso_vars(), missingness(), plot_cats(), plot_df(), plot_nums()

Other Visualization: distr(), freqs_df(), freqs_list(), freqs_plot(), freqs(), noPlot(), plot_chord(), plot_survey(), plot_timeline()

Author

Stephen Milborrow, borrowing heavily from the rpart package by Terry M. Therneau and Beth Atkinson, and the R port of that package by Brian Ripley.

Examples

data(dft)
# Regression Tree
tree <- tree_var(dft, Fare, subtitle = "Titanic dataset")
#> >>> One Hot Encoding applied to 6 variables: 'Pclass', 'Ticket', 'Cabin', 'Embarked', 'Survived', 'Sex'
tree$plot() # tree plot

tree$model # rpart model object
#> n= 891 
#> 
#> node), split, n, deviance, yval
#>       * denotes terminal node
#> 
#>  1) root 891 2197799.00  32.20421  
#>    2) Pclass_1< 0.5 675  107453.20  15.58005  
#>      4) Ticket_OTHER>=0.5 602   36951.84  12.77241  
#>        8) SibSp< 0.5 454   11332.08  10.27211 *
#>        9) SibSp>=0.5 148   14075.24  20.44226 *
#>      5) Ticket_OTHER< 0.5 73   26621.89  38.73350  
#>       10) SibSp< 6.5 66   19269.25  35.46508 *
#>       11) SibSp>=6.5 7       0.00  69.55000 *
#>    3) Pclass_1>=0.5 216 1320849.00  84.15469  
#>      6) Ticket_OTHER>=0.5 193 1067421.00  74.74652  
#>       12) Parch< 0.5 154  626392.20  63.35249 *
#>       13) Parch>=0.5 39  342089.30 119.73840 *
#>      7) Ticket_OTHER< 0.5 23   92995.43 163.10140  
#>       14) PassengerId>=469 12   32097.01 144.27710 *
#>       15) PassengerId< 469 11   52007.28 183.63710 *
tree$performance # metrics
#> $dictionary
#> [1] "RMSE: Root Mean Squared Error"       
#> [2] "MAE: Mean Average Error"             
#> [3] "MAPE: Mean Absolute Percentage Error"
#> [4] "MSE: Mean Squared Error"             
#> [5] "RSQ: R Squared"                      
#> [6] "RSQA: Adjusted R Squared"            
#> 
#> $metrics
#>       rmse      mae       mape      mse    rsq   rsqa
#> 1 35.09267 14.68424 0.03144996 1231.495 0.5007 0.5002
#> 
# Binary Tree
tree_var(dft, Survived_TRUE, explain = FALSE, cex = 0.8)$plot()
#> >>> One Hot Encoding applied to 6 variables: 'Pclass', 'Ticket', 'Cabin', 'Embarked', 'Survived', 'Sex'

# Multiclass tree
tree_var(dft[, c("Pclass", "Fare", "Age")], Pclass, ohse = FALSE)$plot()