scorecard creates a scorecard based on the results from woebin and glm.

scorecard(bins, model, points0 = 600, odds0 = 1/19, pdo = 50,
  basepoints_eq0 = FALSE)

Arguments

bins

Binning information generated from woebin function.

model

A glm model object.

points0

Target points, default 600.

odds0

Target odds, default 1/19. Odds = p/(1-p).

pdo

Points to Double the Odds, default 50.

basepoints_eq0

Logical, Defaults to FALSE. If it is TRUE, the basepoints will equally distribute to each variable.

Value

A list of scorecard data frames

See also

Examples

# \donttest{ # load germancredit data data("germancredit") # filter variable via missing rate, iv, identical value rate dt_sel = var_filter(germancredit, "creditability")
#> [INFO] filtering variables ...
# woe binning ------ bins = woebin(dt_sel, "creditability")
#> [INFO] creating woe binning ...
dt_woe = woebin_ply(dt_sel, bins)
#> [INFO] converting into woe values ...
# glm ------ m = glm(creditability ~ ., family = binomial(), data = dt_woe) # summary(m) # Select a formula-based model by AIC m_step = step(m, direction="both", trace=FALSE) m = eval(m_step$call) # summary(m) # predicted proability # dt_pred = predict(m, type='response', dt_woe) # performace # ks & roc plot # perf_eva(dt_woe$creditability, dt_pred) # scorecard # Example I # creat a scorecard card = scorecard(bins, m) card2 = scorecard2(bins=bins, dt=germancredit, y='creditability', x=sub('_woe', '', names(coef(m))[-1])) # credit score # Example I # only total score score1 = scorecard_ply(germancredit, card) # Example II # credit score for both total and each variable score2 = scorecard_ply(germancredit, card, only_total_score = F) # }