It generates plots for understand frequency, WoE by bins using performance_bin.
# S3 method for performance_bin
plot(x, typographic = TRUE, base_family = NULL, ...)Arguments
- x
an object of class "performance_bin", usually, a result of a call to performance_bin().
- typographic
logical. Whether to apply focuses on typographic elements to ggplot2 visualization. The default is TRUE. if TRUE provides a base theme that focuses on typographic elements using hrbrthemes package.
- base_family
character. The name of the base font family to use for the visualization. If not specified, the font defined in dlookr is applied. (See details)
- ...
further arguments to be passed from or to other methods.
Value
A ggplot2 object.
Details
The base_family is selected from "Roboto Condensed", "Liberation Sans Narrow", "NanumSquare", "Noto Sans Korean". If you want to use a different font, use it after loading the Google font with import_google_font().
Examples
# \donttest{
# Generate data for the example
heartfailure2 <- heartfailure
set.seed(123)
heartfailure2[sample(seq(NROW(heartfailure2)), 5), "creatinine"] <- NA
# Change the target variable to 0(negative) and 1(positive).
heartfailure2$death_event_2 <- ifelse(heartfailure2$death_event %in% "Yes", 1, 0)
# Binnig from creatinine to platelets_bin.
breaks <- c(0, 1, 2, 10)
heartfailure2$creatinine_bin <- cut(heartfailure2$creatinine, breaks)
# Diagnose performance binned variable
perf <- performance_bin(heartfailure2$death_event_2, heartfailure2$creatinine_bin)
perf
#> Bin CntRec CntPos CntNeg CntCumPos CntCumNeg RatePos RateNeg RateCumPos
#> 1 (0,1] 131 24 107 24 107 0.25000 0.52709 0.25000
#> 2 (1,2] 131 49 82 73 189 0.51042 0.40394 0.76042
#> 3 (2,10] 32 21 11 94 200 0.21875 0.05419 0.97917
#> 4 <NA> 5 2 3 96 203 0.02083 0.01478 1.00000
#> 5 Total 299 96 203 NA NA 1.00000 1.00000 NA
#> RateCumNeg Odds LnOdds WoE IV JSD AUC
#> 1 0.52709 0.22430 -1.49478 -0.74592 0.20669 0.02525 0.06589
#> 2 0.93103 0.59756 -0.51490 0.23396 0.02491 0.00311 0.20407
#> 3 0.98522 1.90909 0.64663 1.39548 0.22964 0.02658 0.04713
#> 4 1.00000 0.66667 -0.40547 0.34339 0.00208 0.00026 0.01462
#> 5 NA 0.47291 -0.74886 NA 0.46332 0.05520 0.33172
summary(perf)
#> ── Binning Table ──────────────────────── Several Metrics ──
#> Bin CntRec CntPos CntNeg RatePos RateNeg Odds WoE IV JSD
#> 1 (0,1] 131 24 107 0.25000 0.52709 0.22430 -0.74592 0.20669 0.02525
#> 2 (1,2] 131 49 82 0.51042 0.40394 0.59756 0.23396 0.02491 0.00311
#> 3 (2,10] 32 21 11 0.21875 0.05419 1.90909 1.39548 0.22964 0.02658
#> 4 <NA> 5 2 3 0.02083 0.01478 0.66667 0.34339 0.00208 0.00026
#> 5 Total 299 96 203 1.00000 1.00000 0.47291 NA 0.46332 0.05520
#> AUC
#> 1 0.06589
#> 2 0.20407
#> 3 0.04713
#> 4 0.01462
#> 5 0.33172
#>
#> ── General Metrics ─────────────────────────────────────────
#> • Gini index : -0.33657
#> • IV (Jeffrey) : 0.46332
#> • JS (Jensen-Shannon) Divergence : 0.0552
#> • Kolmogorov-Smirnov Statistics : 0.27709
#> • HHI (Herfindahl-Hirschman Index) : 0.39564
#> • HHI (normalized) : 0.19419
#> • Cramer's V : 0.31765
#>
#> ── Significance Tests ──────────────────── Chisquare Test ──
#> Bin A Bin B statistics p_value
#> 1 (0,1] (1,2] 11.86852 0.000570907
#> 2 (1,2] (2,10] 8.35901 0.003837797
#>
plot(perf)
# Diagnose performance binned variable without NA
perf <- performance_bin(heartfailure2$death_event_2, heartfailure2$creatinine_bin, na.rm = TRUE)
perf
#> Bin CntRec CntPos CntNeg CntCumPos CntCumNeg RatePos RateNeg RateCumPos
#> 1 (0,1] 131 24 107 24 107 0.25532 0.535 0.25532
#> 2 (1,2] 131 49 82 73 189 0.52128 0.410 0.77660
#> 3 (2,10] 32 21 11 94 200 0.22340 0.055 1.00000
#> 4 Total 294 94 200 NA NA 1.00000 1.000 NA
#> RateCumNeg Odds LnOdds WoE IV JSD AUC
#> 1 0.535 0.22430 -1.49478 -0.73975 0.20689 0.02529 0.06830
#> 2 0.945 0.59756 -0.51490 0.24012 0.02672 0.00333 0.21154
#> 3 1.000 1.90909 0.64663 1.40165 0.23604 0.02731 0.04886
#> 4 NA 0.47000 -0.75502 NA 0.46966 0.05592 0.32870
summary(perf)
#> ── Binning Table ──────────────────────── Several Metrics ──
#> Bin CntRec CntPos CntNeg RatePos RateNeg Odds WoE IV JSD
#> 1 (0,1] 131 24 107 0.25532 0.535 0.22430 -0.73975 0.20689 0.02529
#> 2 (1,2] 131 49 82 0.52128 0.410 0.59756 0.24012 0.02672 0.00333
#> 3 (2,10] 32 21 11 0.22340 0.055 1.90909 1.40165 0.23604 0.02731
#> 4 Total 294 94 200 1.00000 1.000 0.47000 NA 0.46966 0.05592
#> AUC
#> 1 0.06830
#> 2 0.21154
#> 3 0.04886
#> 4 0.32870
#>
#> ── General Metrics ─────────────────────────────────────────
#> • Gini index : -0.34261
#> • IV (Jeffrey) : 0.46966
#> • JS (Jensen-Shannon) Divergence : 0.05592
#> • Kolmogorov-Smirnov Statistics : 0.27968
#> • HHI (Herfindahl-Hirschman Index) : 0.40893
#> • HHI (normalized) : 0.11339
#> • Cramer's V : 0.31765
#>
#> ── Significance Tests ──────────────────── Chisquare Test ──
#> Bin A Bin B statistics p_value
#> 1 (0,1] (1,2] 11.86852 0.000570907
#> 2 (1,2] (2,10] 8.35901 0.003837797
#>
plot(perf)
plot(perf, typographic = FALSE)
# }