partial_dependence_plot {creditmodel} | R Documentation |

`partial_dependence_plot`

is for generating a partial dependence plot.
`get_partial_dependence_plots`

is for ploting partial dependence of all vairables in x_list.

```
partial_dependence_plot(model, x, x_train, n.trees = NULL)
get_partial_dependence_plots(
model,
x_train,
x_list,
n.trees = NULL,
dir_path = getwd(),
save_data = TRUE,
plot_show = FALSE,
parallel = FALSE
)
```

`model` |
A data frame of training with predicted prob or score. |

`x` |
The name of an independent variable. |

`x_train` |
A data.frame with independent variables. |

`n.trees` |
Number of trees for best.iter of gbm. |

`x_list` |
Names of independent variables. |

`dir_path` |
The path for periodically saved graphic files. |

`save_data` |
Logical, save results in locally specified folder. Default is FALSE. |

`plot_show` |
Logical, show model performance in current graphic device. Default is FALSE. |

`parallel` |
Logical, parallel computing. Default is FALSE. |

```
sub = cv_split(UCICreditCard, k = 30)[[1]]
dat = UCICreditCard[sub,]
dat = re_name(dat, "default.payment.next.month", "target")
dat = data_cleansing(dat, target = "target", obs_id = "ID",
occur_time = "apply_date", miss_values = list("", -1))
train_test = train_test_split(dat, split_type = "OOT", prop = 0.7,
occur_time = "apply_date")
dat_train = train_test$train
dat_test = train_test$test
x_list = c("PAY_0", "LIMIT_BAL", "PAY_AMT5", "PAY_3", "PAY_2")
Formula = as.formula(paste("target", paste(x_list, collapse = ' + '), sep = ' ~ '))
set.seed(46)
lr_model = glm(Formula, data = dat_train[, c("target", x_list)], family = binomial(logit))
#plot partial dependency of one variable
partial_dependence_plot(model = lr_model, x ="LIMIT_BAL", x_train = dat_train)
#plot partial dependency of all variables
pd_list = get_partial_dependence_plots(model = lr_model, x_list = x_list[1:2],
x_train = dat_train, save_data = FALSE,plot_show = TRUE)
```

[Package *creditmodel* version 1.3.1 Index]