Draws and aligns decision tree and heatmap.

heat_tree() alias.

heat_tree(
  x,
  target_lab = NULL,
  data_test = NULL,
  task = c("classification", "regression"),
  feat_types = NULL,
  label_map = NULL,
  target_cols = NULL,
  target_legend = FALSE,
  clust_samps = TRUE,
  clust_target = TRUE,
  custom_layout = NULL,
  show = "heat-tree",
  heat_rel_height = 0.2,
  lev_fac = 1.3,
  panel_space = 0.001,
  print_eval = (!is.null(data_test)),
  ...
)

treeheatr(
  x,
  target_lab = NULL,
  data_test = NULL,
  task = c("classification", "regression"),
  feat_types = NULL,
  label_map = NULL,
  target_cols = NULL,
  target_legend = FALSE,
  clust_samps = TRUE,
  clust_target = TRUE,
  custom_layout = NULL,
  show = "heat-tree",
  heat_rel_height = 0.2,
  lev_fac = 1.3,
  panel_space = 0.001,
  print_eval = (!is.null(data_test)),
  ...
)

Arguments

x: Dataframe or a `party` or `partynode` object representing a custom tree. If a dataframe is supplied, conditional inference tree is computed. If a custom tree is supplied, it must follow the partykit syntax: https://cran.r-project.org/web/packages/partykit/vignettes/partykit.pdf
target_lab: Name of the column in data that contains target/label information.
data_test: Tidy test dataset. Required if `x` is a `partynode` object. If NULL, heatmap displays (training) data `x`.
task: Character string indicating the type of problem, either 'classification' (categorical outcome) or 'regression' (continuous outcome).
feat_types: Named vector indicating the type of each features, e.g., c(sex = 'factor', age = 'numeric'). If feature types are not supplied, infer from column type.
label_map: Named vector of the meaning of the target values, e.g., c(`0` = 'Edible', `1` = 'Poisonous').
target_cols: Character vectors representing the hex values of different level colors for targets, defaults to viridis option B.
target_legend: Logical. If TRUE, target legend is drawn.
clust_samps: Logical. If TRUE, hierarchical clustering would be performed among samples within each leaf node.
clust_target: Logical. If TRUE, target/label is included in hierarchical clustering of samples within each leaf node and might yield a more interpretable heatmap.
custom_layout: Dataframe with 3 columns: id, x and y for manually input custom layout.
show: Character string indicating which components of the decision tree-heatmap should be drawn. Can be 'heat-tree', 'heat-only' or 'tree-only'.
heat_rel_height: Relative height of heatmap compared to whole figure (with tree).
lev_fac: Relative weight of child node positions according to their levels, commonly ranges from 1 to 1.5. 1 for parent node perfectly in the middle of child nodes.
panel_space: Spacing between facets relative to viewport, recommended to range from 0.001 to 0.01.
print_eval: Logical. If TRUE, print evaluation of the tree performance. Defaults to TRUE when `data_test` is supplied.
...: Further arguments passed to `draw_tree()` and/or `draw_heat()`.

Value

A gtable/grob object of the decision tree (top) and heatmap (bottom).

Examples

heat_tree(penguins, target_lab = 'species')


# \donttest{
heat_tree(
  x = galaxy[1:100, ],
  target_lab = 'target',
  task = 'regression',
  terminal_vars = NULL,
  tree_space_bottom = 0)
#> Warning: binary variable(s) 4 treated as interval scaled

# }
treeheatr(penguins, target_lab = 'species')


treeheatr(
  x = galaxy[1:100, ],
  target_lab = 'target',
  task = 'regression',
  terminal_vars = NULL,
  tree_space_bottom = 0)
#> Warning: binary variable(s) 4 treated as interval scaled