get_psi_plots: Plot PSI(Population Stability Index)

In creditmodel: Toolkit for Credit Modeling, Analysis and Visualization

Description

You can use the psi_plot to plot PSI of your data. get_psi_plots can loop through plots for all specified independent variables.

Usage

get_psi_plots(
  dat_train,
  dat_test = NULL,
  x_list = NULL,
  ex_cols = NULL,
  breaks_list = NULL,
  occur_time = NULL,
  g = 10,
  plot_show = TRUE,
  save_data = FALSE,
  file_name = NULL,
  parallel = FALSE,
  g_width = 8,
  dir_path = tempdir()
)

psi_plot(
  dat_train,
  x,
  dat_test = NULL,
  occur_time = NULL,
  g_width = 8,
  breaks_list = NULL,
  breaks = NULL,
  g = 10,
  plot_show = TRUE,
  save_data = FALSE,
  dir_path = tempdir()
)

Arguments

dat_train	A data.frame with independent variables.
dat_test	A data.frame of test data. Default is NULL.
x_list	Names of independent variables.
ex_cols	A list of excluded variables. Regular expressions can also be used to match variable names. Default is NULL.
breaks_list	A table containing a list of splitting points for each independent variable. Default is NULL.
occur_time	The name of occur time.
g	Number of initial breakpoints for equal frequency binning.
plot_show	Logical, show model performance in current graphic device. Default is FALSE.
save_data	Logical, save results in locally specified folder. Default is FALSE.
file_name	The name for periodically saved data file. Default is NULL.
parallel	Logical, parallel computing. Default is FALSE.
g_width	The width of graphs.
dir_path	The path for periodically saved graphic files.
x	The name of an independent variable.
breaks	Splitting points for a continues variable.

Examples

train_test = train_test_split(UCICreditCard[1:1000,], split_type = "Random",
 prop = 0.8, save_data = FALSE)
dat_train = train_test$train
dat_test = train_test$test
get_psi_plots(dat_train[, c(8, 9)], dat_test = dat_test[, c(8, 9)])

Example output

Package 'creditmodel' version 1.2.7
   Feature         Bins actual expected Ac_pct Ex_pct PSI_i   PSI
1    PAY_0        00.NA     43      171  21.5%  21.4% 0.000 0.007
2    PAY_0  01.(-Inf,0]    112      440    56%    55% 0.000 0.007
3    PAY_0     02.(0,1]     24      112    12%    14% 0.003 0.007
4    PAY_0     03.(1,2]     19       65   9.5%   8.1% 0.002 0.007
5    PAY_0  04.(2, Inf]      2       12     1%   1.5% 0.002 0.007
6    PAY_2        00.NA     44      161    22%  20.1% 0.002 0.006
7    PAY_2 01.(-Inf,-2]     27      103  13.5%  12.9% 0.000 0.006
8    PAY_2    02.(-2,0]    104      421    52%  52.6% 0.000 0.006
9    PAY_2     03.(0,2]     23      103  11.5%  12.9% 0.002 0.006
10   PAY_2  04.(2, Inf]      2       12     1%   1.5% 0.002 0.006

creditmodel documentation built on Jan. 7, 2022, 5:06 p.m.