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.

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.tc5.lr01,
# var = c("SegSumT",
# "SegTSeas"))
#
## ---------------------------------------------