visTree {visTree}

Visualization of subgroups for decision trees

Description

This visualization characterizes subgroups defined by a decision tree structure and identifies the range of covariate values associated with outcome values in each subgroup.

Usage

visTree(cond.tree, rng = NULL, interval = FALSE, color.type = 1,
  alpha = 0.5, add.h.axis = TRUE, add.p.axis = TRUE,
  text.round = 1, text.main = 1.5, text.bar = 1.5,
  text.title = 1.5, text.label = 1.5, text.axis = 1.5,
  text.percentile = 0.7, density.line = TRUE)

Arguments

cond.tree

Decision tree generated as a party object.

rng

Restrict plotting to a particular set of nodes. Default value is set as NULL.

interval

logical. Continuous outcome (interval = FALSE) and Categorical outcome (interval = TRUE).

color.type

Color palettes (rainbow_hcl = 1; heat_hcl = 2; terrain_hcl = 3; sequential_hcl = 4 ; diverge_hcl = 5)

alpha

Transparency for horizontal colored bars in each subplot. Values between 0 to 1.

add.h.axis

logical. Add axis for the outcome distribution (add.h.axis = TRUE), remove axis for the outcome (add.h.axis = FALSE).

add.p.axis

logical. Add axis for the percentiles (add.p.axis = TRUE) computed over covariate values, remove axis for the percentiles (add.p.axis = FALSE).

text.round

Round the threshold displayed on the horizontal bar

text.main

Change the size of the main titles

text.bar

Change the size of the text in the horizontal bar

text.title

Change the size of the text in the title

text.label

Change the size of the axis annotation

text.axis

Change the size of the text of axis labels

text.percentile

Change the size of the percentile title

density.line

logical. Draw a density line. (density.line = TRUE).

Author(s)

Ashwini Venkatasubramaniam and Julian Wolfson

Examples

data(blsdata)
newblsdata<-blsdata[,c(7,21, 22,23, 24, 25, 26)]
## Continuous response
ptree1<-partykit::ctree(kcal24h0~., data = newblsdata)
visTree(ptree1, text.axis = 1.3, text.label = 1.2, text.bar = 1.2, alpha = 0.5)

## Repeated covariates in the splits of the decision tree
ptree2<-partykit::ctree(kcal24h0~skcal+rrvfood+resteating+age, data = blsdata)
visTree(ptree2, text.axis = 1.3, text.label = 1.2, text.bar = 1.2, alpha = 0.5)

## Categorical response
blsdataedit<-blsdata[,-7]
blsdataedit$bin<-0 
blsdataedit$bin<-cut(blsdata$kcal24h0, unique(quantile(blsdata$kcal24h0)), 
include.lowest = TRUE, dig.lab = 4)
names(blsdataedit)[26]<-"kcal24h0"
ptree3<-partykit::ctree(kcal24h0~hunger+rrvfood+resteating+liking, data = blsdataedit)
visTree(ptree3, interval = TRUE,  color.type = 1, alpha = 0.6, 
text.percentile = 1.2, text.bar = 1.8)

## Other decision trees (e.g., rpart) 
ptree4<-rpart::rpart(kcal24h0~wanting+liking+rrvfood, data = newblsdata, 
control = rpart::rpart.control(cp = 0.029))
visTree(ptree4, text.bar = 1.8, text.label = 1.4, text.round = 1, 
density.line = TRUE, text.percentile = 1.3)

## Change the color scheme and transparency of the horizontal bars
ptree1<-partykit::ctree(kcal24h0~., data = newblsdata)
visTree(ptree1, text.axis = 1.3, text.label = 1.2, text.bar = 1.2, alpha = 0.65, 
color.type = 3)

## Remove the axes corresponding to the percentiles and the response values.
ptree1<-partykit::ctree(kcal24h0~., data = newblsdata)
visTree(ptree1, text.axis = 1.3, text.label = 1.2, text.bar = 1.2, alpha = 0.65, 
color.type = 3, add.p.axis = FALSE, add.h.axis = FALSE) 

# Remove the density line over the histograms 
ptree1<-partykit::ctree(kcal24h0~., data = newblsdata)
visTree(ptree1, text.axis = 1.3, text.label = 1.2, text.bar = 1.2, alpha = 0.65, 
color.type = 3, density.line = FALSE)