rf.partial.ci: Random Forests regression partial dependency plot with...

In jeffreyevans/rfUtilities: Random Forests Model Selection and Performance Evaluation

Random Forests regression partial dependency plot with confidence intervals

Description

Plots the partial dependency, and associated confidence intervals, of a random forests regression model

Usage

rf.partial.ci(m, x, yname, xname, lci = 0.25, uci = 0.75, delta = FALSE)

Arguments

m	randomForest regression object
x	data.frame or matrix of independent variables used to build model
yname	Name of the dependent variable
xname	Name of the independent variable for calculating partial dependence
lci	Percentile of predictions to plot as the lower bound.
uci	Percentile of predictions to plot as the upper bound.
delta	Plot change in prediction between the independent variable and simulated values (default delta = NULL)

Value

recordedplot object to recall plot

Note

depends: randomForest

Author(s)

Jeffrey S. Evans <[e-mail]>

Examples

 library(randomForest)
 data(airquality)
 airquality <- na.omit(airquality)
 rf.ozone <- randomForest(y=airquality[,"Ozone"], airquality[,2:ncol(airquality)])

 par(mfrow=c(2,2))
   for(i in c("Solar.R", "Wind", "Temp", "Day")){
     rf.partial.ci(m=rf.ozone, x=airquality, yname="Ozone", xname=i, delta=TRUE) 
   } 

jeffreyevans/rfUtilities documentation built on Nov. 12, 2023, 6:52 p.m.