treeheatr (version 0.2.3)

draw_heat: Draws the heatmap.

Description

Draws the heatmap to be placed below the decision tree.

Usage

draw_heat(
  dat,
  fit,
  feat_types = NULL,
  target_cols = NULL,
  target_lab_disp = fit$target_lab,
  trans_type = c("percentize", "normalize", "scale", "none"),
  clust_feats = TRUE,
  feats = NULL,
  show_all_feats = FALSE,
  p_thres = 0.05,
  cont_legend = "none",
  cate_legend = "none",
  cont_cols = ggplot2::scale_fill_viridis_c,
  cate_cols = ggplot2::scale_fill_viridis_d,
  panel_space = 0.001,
  target_space = 0.05,
  target_pos = "top"
)

Value

A ggplot2 grob object of the heatmap.

Arguments

dat

Dataframe with samples from original dataset ordered according to the clustering within each leaf node.

fit

party object, e.g., as output from partykit::ctree()

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.

target_cols

Character vectors representing the hex values of different level colors for targets, defaults to viridis option B.

target_lab_disp

Character string for displaying the label of target label. If not provided, use `target_lab`.

trans_type

Character string of 'normalize', 'scale' or 'none'. If 'scale', subtract the mean and divide by the standard deviation. If 'normalize', i.e., max-min normalize, subtract the min and divide by the max. If 'none', no transformation is applied. More information on what transformation to choose can be acquired here: https://cran.rstudio.com/package=heatmaply/vignettes/heatmaply.html#data-transformation-scaling-normalize-and-percentize

clust_feats

Logical. If TRUE, performs cluster on the features.

feats

Character vector of feature names to be displayed in the heatmap. If NULL, display features of which P values are less than `p_thres`.

show_all_feats

Logical. If TRUE, show all features regardless of `p_thres`.

p_thres

Numeric value indicating the p-value threshold of feature importance. Feature with p-values computed from the decision tree below this value will be displayed on the heatmap.

cont_legend

Function determining the options for legend of continuous variables, defaults to FALSE. If TRUE, use `guide_colorbar(barwidth = 10, barheight = 0.5, title = NULL)`. Any other [`guides()`](https://ggplot2.tidyverse.org/reference/guides.html) functions would also work.

cate_legend

Function determining the options for legend of categorical variables, defaults to FALSE. If TRUE, use `guide_legend(title = NULL)`. Any other [`guides()`](https://ggplot2.tidyverse.org/reference/guides.html) functions would also work.

cont_cols

Function determining color scale for continuous variable, defaults to `scale_fill_viridis_c(guide = cont_legend)`.

cate_cols

Function determining color scale for nominal categorical variable, defaults to `scale_fill_viridis_d(begin = 0.3, end = 0.9)`.

panel_space

Spacing between facets relative to viewport, recommended to range from 0.001 to 0.01.

target_space

Numeric value indicating spacing between the target label and the rest of the features

target_pos

Character string specifying the position of the target label on heatmap, can be 'top', 'bottom' or 'none'.

Examples

x <- compute_tree(penguins, target_lab = "species")
draw_heat(x$dat, x$fit)