get_psi_iv_all: Calculate IV & PSI

In creditmodel: Toolkit for Credit Modeling, Analysis and Visualization

Description

get_iv_psi is used to calculate Information Value (IV) and Population Stability Index (PSI) of an independent variable. get_iv_psi_all can loop through IV & PSI for all specified independent variables.

Usage

get_psi_iv_all(
  dat,
  dat_test = NULL,
  x_list = NULL,
  target,
  ex_cols = NULL,
  pos_flag = NULL,
  breaks_list = NULL,
  occur_time = NULL,
  oot_pct = 0.7,
  equal_bins = FALSE,
  cut_bin = "equal_depth",
  tree_control = NULL,
  bins_control = NULL,
  bins_total = FALSE,
  best = TRUE,
  g = 10,
  as_table = TRUE,
  note = FALSE,
  parallel = FALSE,
  bins_no = TRUE
)

get_psi_iv(
  dat,
  dat_test = NULL,
  x,
  target,
  pos_flag = NULL,
  breaks = NULL,
  breaks_list = NULL,
  occur_time = NULL,
  oot_pct = 0.7,
  equal_bins = FALSE,
  cut_bin = "equal_depth",
  tree_control = NULL,
  bins_control = NULL,
  bins_total = FALSE,
  best = TRUE,
  g = 10,
  as_table = TRUE,
  note = FALSE,
  bins_no = TRUE
)

Arguments

dat	A data.frame with independent variables and target variable.
dat_test	A data.frame of test data. Default is NULL.
x_list	Names of independent variables.
target	The name of target variable.
ex_cols	A list of excluded variables. Regular expressions can also be used to match variable names. Default is NULL.
pos_flag	The value of positive class of target variable, default: "1".
breaks_list	A table containing a list of splitting points for each independent variable. Default is NULL.
occur_time	The name of the variable that represents the time at which each observation takes place.
oot_pct	Percentage of observations retained for overtime test (especially to calculate PSI). Defualt is 0.7
equal_bins	Logical, generates initial breaks for equal frequency or width binning.
cut_bin	A string, if equal_bins is TRUE, 'equal_depth' or 'equal_width', default is 'equal_depth'.
tree_control	Parameters of using Decision Tree to segment initial breaks. See detials: get_tree_breaks
bins_control	Parameters used to control binning. See detials: select_best_class, select_best_breaks
bins_total	Logical, total sum for each variable.
best	Logical, merge initial breaks to get optimal breaks for binning.
g	Number of initial breakpoints for equal frequency binning.
as_table	Logical, output results in a table. Default is TRUE.
note	Logical, outputs info. Default is TRUE.
parallel	Logical, parallel computing. Default is FALSE.
bins_no	Logical, add serial numbers to bins. Default is FALSE.
x	The name of an independent variable.
breaks	Splitting points for an independent variable. Default is NULL.

See Also

get_iv,get_iv_all,get_psi,get_psi_all

Examples

iv_list = get_psi_iv_all(dat = UCICreditCard[1:1000, ],
x_list = names(UCICreditCard)[3:5], equal_bins = TRUE,
target = "default.payment.next.month", ex_cols = "ID|apply_date")
get_psi_iv(UCICreditCard, x = "PAY_3",
target = "default.payment.next.month",bins_total = TRUE)

Example output

Package 'creditmodel' version 1.2.7
  Feature         bins cuts #total #expected expected_0 expected_1 #actual
1   PAY_3        00.NA   -1   5938      4089       3449        640    1849
2   PAY_3 01.(-Inf,-1]    1   4085      2859       2324        535    1226
3   PAY_3    02.(-1,1]  Inf  15768     11077       9167       1910    4691
4   PAY_3  03.(1, Inf] <NA>   4209      2975       1410       1565    1234
5   Total           --   --  30000     21000      16350       4650    9000
  actual_0 actual_1 %total %expected %actual %total_1 %expected_1 %actual_1
1     1563      286    0.2      0.19    0.21     0.16        0.16      0.15
2     1004      222   0.14      0.14    0.14     0.19        0.19      0.18
3     3849      842   0.53      0.53    0.52     0.17        0.17      0.18
4      598      636   0.14      0.14    0.14     0.52        0.53      0.52
5     7014     1986      1         1       1     0.22        0.22      0.22
  odds_ratio odds_ratio_s  PSIi   IVi
1      1.537            0 0.001 0.032
2      1.249        0.002     0 0.006
3      1.343        0.004     0 0.042
4      0.259            0     0 0.332
5          1        0.006 0.001 0.412

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