visTree: Visualization of subgroups for decision trees

In 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) 

Example output

visTree documentation built on May 1, 2019, 9:46 p.m.