plot_oot_perf: plot_oot_perf 'plot_oot_perf' is for ploting performance of...
In creditmodel: Toolkit for Credit Modeling, Analysis and Visualization

plot_oot_perf plot_oot_perf is for ploting performance of cross time samples in the future

plot_oot_perf(
  dat_test,
  x,
  occur_time,
  target,
  k = 3,
  g = 10,
  period = "month",
  best = FALSE,
  equal_bins = TRUE,
  pl = "rate",
  breaks = NULL,
  cut_bin = "equal_depth",
  gtitle = NULL,
  perf_dir_path = NULL,
  save_data = FALSE,
  plot_show = TRUE
)

`dat_test`	A data frame of testing dataset with predicted prob or score.
`x`	The name of prob or score variable.
`occur_time`	The name of the variable that represents the time at which each observation takes place.
`target`	The name of target variable.
`k`	If period is NULL, number of equal frequency samples.
`g`	Number of breaks for prob or score.
`period`	OOT period, 'weekly' and 'month' are available.if NULL, use k equal frequency samples.
`best`	Logical, merge initial breaks to get optimal breaks for binning.
`equal_bins`	Logical, generates initial breaks for equal frequency or width binning.
`pl`	'lift' is for lift chart plot,'rate' is for positive rate plot.
`breaks`	Splitting points of prob or score.
`cut_bin`	A string, if equal_bins is TRUE, 'equal_depth' or 'equal_width', default is 'equal_depth'.
`gtitle`	The title of the graph & The name for periodically saved graphic file.
`perf_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 TRUE.

sub = cv_split(UCICreditCard, k = 30)[[1]]
dat = UCICreditCard[sub,]
dat = re_name(dat, "default.payment.next.month", "target")
x_list = c("PAY_0", "LIMIT_BAL", "PAY_AMT5", "PAY_3", "PAY_2")
dat = data_cleansing(dat, target = "target", obs_id = "ID",x_list = x_list,
occur_time = "apply_date", miss_values = list("", -1))
dat = process_nas(dat)
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
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))

dat_train$pred_LR = round(predict(lr_model, dat_train[, x_list], type = "response"), 5)
dat_test$pred_LR = round(predict(lr_model, dat_test[, x_list], type = "response"), 5)
plot_oot_perf(dat_test = dat_test, occur_time = "apply_date", target = "target", x = "pred_LR")