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@statcompute
Last active May 4, 2019
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df <- readRDS("df.rds")
### SHOWING THE RESPONSE IN THE LAST COLUMN ###
head(df, 2)
#tot_derog tot_tr age_oldest_tr tot_open_tr tot_rev_tr tot_rev_debt tot_rev_line rev_util bureau_score ltv tot_income bad
# 6 7 46 NaN NaN NaN NaN 0 747 109 4800.00 0
# 0 21 153 6 1 97 4637 2 744 97 5833.33 0
source("mob.R")
bin_out <- batch_bin(df, 3)
bin_out$BinSum
# var nbin unique miss min median max ks iv
# tot_derog 7 29 213 0 0.0 32 20.0442 0.2599
# tot_tr 15 67 213 0 16.0 77 17.3002 0.1425
# ......
top <- paste(bin_out$BinSum[order(bin_out$BinSum[["iv"]], decreasing = T), ][1:6, "var"], sep = '')
par(mfrow = c(2, 3))
lapply(top, function(x) plot(bin_out$BinLst[[x]]$df[["woe"]],
log(bin_out$BinLst[[x]]$df[["bad_rate"]] / (1 - bin_out$BinLst[[x]]$df[["bad_rate"]])),
type = "b", main = x, cex.main = 2, xlab = paste("woe of", x), ylab = "logit(bad)", cex = 2, col = "red"))
df_woe <- batch_woe(df, bin_out$BinLst)
str(df_woe$df)
#'data.frame': 5837 obs. of 12 variables:
# $ idx_ : int 1 2 3 4 5 6 7 8 9 10 ...
# $ woe.tot_derog : num 0.656 -0.556 -0.556 0.274 0.274 ...
# $ woe.tot_tr : num 0.407 -0.322 -0.4 -0.322 0.303 ...
# ......
### PARSE VARIABLES WITH IV > 0.1 ###
x1 <- paste("woe", bin_out$BinSum[bin_out$BinSum[["iv"]] > 0.1, ]$var, sep = ".")
# "woe.tot_derog" "woe.tot_tr" "woe.age_oldest_tr" "woe.tot_rev_line" "woe.rev_util" "woe.bureau_score" "woe.ltv"
fml1 <- as.formula(paste("bad", paste(x1, collapse = " + "), sep = " ~ "))
sum1 <- summary(glm(fml1, data = cbind(bad = df$bad, df_woe$df), family = "binomial"))
### PARSE SIGNIFICANT VARIABLES WITH P-VALUE < 0.05 ###
x2 <- paste(row.names(sum1$coefficients)[sum1$coefficients[, 4] < 0.05][-1])
# "woe.age_oldest_tr" "woe.tot_rev_line" "woe.rev_util" "woe.bureau_score" "woe.ltv"
fml2 <- as.formula(paste("bad", paste(x2, collapse = " + "), sep = " ~ "))
mdl2 <- glm(fml2, data = cbind(bad = df$bad, df_woe$df), family = "binomial")
# Estimate Std. Error z value Pr(>|z|)
#(Intercept) -1.38600 0.03801 -36.461 < 2e-16 ***
#woe.age_oldest_tr 0.30376 0.08176 3.715 0.000203 ***
#woe.tot_rev_line 0.42935 0.06793 6.321 2.61e-10 ***
#woe.rev_util 0.29150 0.08721 3.342 0.000831 ***
#woe.bureau_score 0.83568 0.04974 16.803 < 2e-16 ***
#woe.ltv 0.97789 0.09121 10.721 < 2e-16 ***
pROC::roc(response = df$bad, predictor = fitted(mdl2))
# Area under the curve: 0.7751
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