gg_partial_plot: Plot partial dependence for BRT models

In njtierney/treezy: Makes it Easier To Work With Decision Trees

Description

Uses the "partial_dependence" function to plot partial dependence for BRT models. Future work will be into finding a way to generalize these methods to rpart and randomForest models, as an S3 method. This code is bespoke at the moment, and isn't designed as a flexible way to create plots, so I would recommend that people who want to plot their own partial plots just use 'partial_dependence' and go from there.

Usage

gg_partial_plot(x, vars)

Arguments

x	The GBM model to be used
vars	The variables used in the GBM model, this is a character vector

Value

a faceted ggplot plot of the variables

Examples

## Not run: 

# using gbm.step from the dismo package

library(gbm)
library(dismo)

# load data

data(Anguilla_train)
anguilla_train <- Anguilla_train[1:200,]

# fit model
angaus_tc_5_lr_01 <- gbm.step(data = anguilla_train,
                              gbm.x = 3:14,
                              gbm.y = 2,
                              family = "bernoulli",
                              tree.complexity = 5,
                              learning.rate = 0.01,
                              bag.fraction = 0.5)

gg_partial_plot(angaus_tc_5_lr_01,
                   var = c("SegSumT",
                           "SegTSeas"))


## End(Not run)

njtierney/treezy documentation built on Oct. 10, 2019, 1:08 a.m.