scorecard_ply calculates credit score using the results from scorecard.

scorecard_ply(dt, card, only_total_score = TRUE, print_step = 0L,
  replace_blank_na = TRUE, var_kp = NULL)



A data frame, which is the original dataset for training model.


The scorecard generated from the function scorecard.


Logical, Defaults to TRUE. If it is TRUE, then the output includes only total credit score; Otherwise, if it is FALSE, the output includes both total and each variable's credit score.


A non-negative integer. Defaults to 1. If print_step>0, print variable names by each print_step-th iteration. If print_step=0, no message is print.


Logical. Replace blank values with NA. Defaults to TRUE. This argument should be the same with woebin's.


Name of force kept variables, such as id column. Defaults to NULL.


A data frame in score values

See also


# \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 = FALSE) # }