# perf_psi: PSI In scorecard: Credit Risk Scorecard

## Description

perf_psi calculates population stability index (PSI) for both total credit score and variables. It can also creates graphics to display score distribution and bad rate trends.

## Usage

 1 2 perf_psi(score, label = NULL, title = NULL, show_plot = TRUE, positive = "bad|1", threshold_variable = 20, var_skip = NULL, ...) 

## Arguments

 score A list of credit score for actual and expected data samples. For example, score = list(actual = scoreA, expect = scoreE). label A list of label value for actual and expected data samples. For example, label = list(actual = labelA, expect = labelE). Defaults to NULL. title Title of plot, Defaults to NULL. show_plot Logical. Defaults to TRUE. positive Value of positive class, Defaults to "bad|1". threshold_variable Integer. Defaults to 20. If the number of unique values > threshold_variable, the provided score will be counted as total credit score, otherwise, it is variable score. var_skip Name of variables that are not score, such as id column. It should be the same with the var_kp in scorecard_ply function. Defaults to NULL. ... Additional parameters.

## Details

The population stability index (PSI) formula is displayed below:

PSI = ∑((Actual\% - Expected\%)*(\ln(\frac{Actual\%}{Expected\%}))).

The rule of thumb for the PSI is as follows: Less than 0.1 inference insignificant change, no action required; 0.1 - 0.25 inference some minor change, check other scorecard monitoring metrics; Greater than 0.25 inference major shift in population, need to delve deeper.

## Value

A data frame of psi and graphics of credit score distribution

perf_eva gains_table
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 # data preparing ------ # load germancredit data data("germancredit") # filter variable via missing rate, iv, identical value rate dt_f = var_filter(germancredit, "creditability") # breaking dt into train and test dt_list = split_df(dt_f, "creditability") label_list = lapply(dt_list, function(x) x$creditability) # woe binning ------ bins = woebin(dt_list$train, "creditability") # converting train and test into woe values dt_woe_list = lapply(dt_list, function(x) woebin_ply(x, bins)) # glm ------ m1 = glm(creditability ~ ., family = binomial(), data = dt_woe_list$train) # vif(m1, merge_coef = TRUE) # Select a formula-based model by AIC m_step = step(m1, direction="both", trace=FALSE) m2 = eval(m_step$call) # vif(m2, merge_coef = TRUE) # predicted proability pred_list = lapply(dt_woe_list, function(x) predict(m2, type = 'response', x)) # scorecard ------ card = scorecard(bins, m2) # credit score, only_total_score = TRUE score_list = lapply(dt_list, function(x) scorecard_ply(x, card)) # credit score, only_total_score = FALSE score_list2 = lapply(dt_list, function(x) scorecard_ply(x, card, only_total_score=FALSE)) ###### perf_eva examples ###### # Example I, one datset ## predicted p1 perf_eva(pred = pred_list$train, label=dt_list$train$creditability, title = 'train') ## predicted score # perf_eva(pred = score_list$train, label=dt_list$train$creditability, title = 'train') # Example II, multiple datsets ## predicted p1 perf_eva(pred = pred_list, label = label_list) ## predicted score # perf_eva(score_list, label_list) ###### perf_psi examples ###### # Example I # only total psi psi1 = perf_psi(score = score_list, label = label_list) psi1$psi # psi data frame psi1$pic # pic of score distribution # modify colors # perf_psi(score = score_list, label = label_list, # line_color='#FC8D59', bar_color=c('#FFFFBF', '#99D594')) # Example II # both total and variable psi psi2 = perf_psi(score = score_list, label = label_list) # psi2$psi # psi data frame # psi2$pic # pic of score distribution ###### gains_table examples ###### # Example I, input score and label can be a list or a vector g1 = gains_table(score = score_list$train, label = label_list$train) g2 = gains_table(score = score_list, label = label_list) # Example II, specify the bins number and type g3 = gains_table(score = score_list, label = label_list, bin_num = 20) g4 = gains_table(score = score_list, label = label_list, method = 'width')