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Pomodoro_Vignette

Seyma Kalay

2022-05-15

Source: vignettes/Pomodoro_Vignette.Rmd
Pomodoro_Vignette.Rmd

This package was set for the Credit Access studies. But it can be used for the binary and multiple factor variables. First thing let’s see the str of the sample_data with str(sample_data). Since the dataset is huge, let’s take the first 500 rows and set the study on it.

The following example run the multinominal logistic model in yvar. The function simplifies the 80/20 train test set using 10cv after scaled and center it.

#> Loading required package: ggplot2
#> Loading required package: lattice
#> + Fold01: mtry= 2 
#> - Fold01: mtry= 2 
#> + Fold01: mtry= 7 
#> - Fold01: mtry= 7 
#> + Fold01: mtry=12 
#> - Fold01: mtry=12 
#> + Fold02: mtry= 2 
#> - Fold02: mtry= 2 
#> + Fold02: mtry= 7 
#> - Fold02: mtry= 7 
#> + Fold02: mtry=12 
#> - Fold02: mtry=12 
#> + Fold03: mtry= 2 
#> - Fold03: mtry= 2 
#> + Fold03: mtry= 7 
#> - Fold03: mtry= 7 
#> + Fold03: mtry=12 
#> - Fold03: mtry=12 
#> + Fold04: mtry= 2 
#> - Fold04: mtry= 2 
#> + Fold04: mtry= 7 
#> - Fold04: mtry= 7 
#> + Fold04: mtry=12 
#> - Fold04: mtry=12 
#> + Fold05: mtry= 2 
#> - Fold05: mtry= 2 
#> + Fold05: mtry= 7 
#> - Fold05: mtry= 7 
#> + Fold05: mtry=12 
#> - Fold05: mtry=12 
#> + Fold06: mtry= 2 
#> - Fold06: mtry= 2 
#> + Fold06: mtry= 7 
#> - Fold06: mtry= 7 
#> + Fold06: mtry=12 
#> - Fold06: mtry=12 
#> + Fold07: mtry= 2 
#> - Fold07: mtry= 2 
#> + Fold07: mtry= 7 
#> - Fold07: mtry= 7 
#> + Fold07: mtry=12 
#> - Fold07: mtry=12 
#> + Fold08: mtry= 2 
#> - Fold08: mtry= 2 
#> + Fold08: mtry= 7 
#> - Fold08: mtry= 7 
#> + Fold08: mtry=12 
#> - Fold08: mtry=12 
#> + Fold09: mtry= 2 
#> - Fold09: mtry= 2 
#> + Fold09: mtry= 7 
#> - Fold09: mtry= 7 
#> + Fold09: mtry=12 
#> - Fold09: mtry=12 
#> + Fold10: mtry= 2 
#> - Fold10: mtry= 2 
#> + Fold10: mtry= 7 
#> - Fold10: mtry= 7 
#> + Fold10: mtry=12 
#> - Fold10: mtry=12 
#> Aggregating results
#> Selecting tuning parameters
#> Fitting mtry = 2 on full training set
#> Multi-class area under the curve: 0.7903

Estimate_Models

Estimate_Models function considers exog and xadd variables and set multiple models based on the selected exog and xadd. On the one hand exog is subtract the selected vector from the dataset and run the model for all the dataset and for the splits of the exog. On the other hand xadd add the selected vectors and run the model. Where the dnames are the unique values in exog this is to save the model estimates by their name.

sample_data <- sample_data[c(1:750),]
yvar <- c("Loan.Type")
xvar <- c("sex", "married", "age", "havejob", "educ", "political.afl",
"rural", "region", "fin.intermdiaries", "fin.knowldge", "income")
CCP.RF <- Estimate_Models(sample_data, yvar, xvec = xvar, exog = "political.afl",
xadd = c("networth", "networth_homequity", "liquid.assets"),
type = "RF", dnames = c("0","1"))
#> + Fold01: mtry= 2 
#> - Fold01: mtry= 2 
#> + Fold01: mtry= 6 
#> - Fold01: mtry= 6 
#> + Fold01: mtry=11 
#> - Fold01: mtry=11 
#> + Fold02: mtry= 2 
#> - Fold02: mtry= 2 
#> + Fold02: mtry= 6 
#> - Fold02: mtry= 6 
#> + Fold02: mtry=11 
#> - Fold02: mtry=11 
#> + Fold03: mtry= 2 
#> - Fold03: mtry= 2 
#> + Fold03: mtry= 6 
#> - Fold03: mtry= 6 
#> + Fold03: mtry=11 
#> - Fold03: mtry=11 
#> + Fold04: mtry= 2 
#> - Fold04: mtry= 2 
#> + Fold04: mtry= 6 
#> - Fold04: mtry= 6 
#> + Fold04: mtry=11 
#> - Fold04: mtry=11 
#> + Fold05: mtry= 2 
#> - Fold05: mtry= 2 
#> + Fold05: mtry= 6 
#> - Fold05: mtry= 6 
#> + Fold05: mtry=11 
#> - Fold05: mtry=11 
#> + Fold06: mtry= 2 
#> - Fold06: mtry= 2 
#> + Fold06: mtry= 6 
#> - Fold06: mtry= 6 
#> + Fold06: mtry=11 
#> - Fold06: mtry=11 
#> + Fold07: mtry= 2 
#> - Fold07: mtry= 2 
#> + Fold07: mtry= 6 
#> - Fold07: mtry= 6 
#> + Fold07: mtry=11 
#> - Fold07: mtry=11 
#> + Fold08: mtry= 2 
#> - Fold08: mtry= 2 
#> + Fold08: mtry= 6 
#> - Fold08: mtry= 6 
#> + Fold08: mtry=11 
#> - Fold08: mtry=11 
#> + Fold09: mtry= 2 
#> - Fold09: mtry= 2 
#> + Fold09: mtry= 6 
#> - Fold09: mtry= 6 
#> + Fold09: mtry=11 
#> - Fold09: mtry=11 
#> + Fold10: mtry= 2 
#> - Fold10: mtry= 2 
#> + Fold10: mtry= 6 
#> - Fold10: mtry= 6 
#> + Fold10: mtry=11 
#> - Fold10: mtry=11 
#> Aggregating results
#> Selecting tuning parameters
#> Fitting mtry = 11 on full training set
#> + Fold01: mtry= 2 
#> - Fold01: mtry= 2 
#> + Fold01: mtry= 6 
#> - Fold01: mtry= 6 
#> + Fold01: mtry=11 
#> - Fold01: mtry=11 
#> + Fold02: mtry= 2 
#> - Fold02: mtry= 2 
#> + Fold02: mtry= 6 
#> - Fold02: mtry= 6 
#> + Fold02: mtry=11 
#> - Fold02: mtry=11 
#> + Fold03: mtry= 2 
#> - Fold03: mtry= 2 
#> + Fold03: mtry= 6 
#> - Fold03: mtry= 6 
#> + Fold03: mtry=11 
#> - Fold03: mtry=11 
#> + Fold04: mtry= 2 
#> - Fold04: mtry= 2 
#> + Fold04: mtry= 6 
#> - Fold04: mtry= 6 
#> + Fold04: mtry=11 
#> - Fold04: mtry=11 
#> + Fold05: mtry= 2 
#> - Fold05: mtry= 2 
#> + Fold05: mtry= 6 
#> - Fold05: mtry= 6 
#> + Fold05: mtry=11 
#> - Fold05: mtry=11 
#> + Fold06: mtry= 2 
#> - Fold06: mtry= 2 
#> + Fold06: mtry= 6 
#> - Fold06: mtry= 6 
#> + Fold06: mtry=11 
#> - Fold06: mtry=11 
#> + Fold07: mtry= 2 
#> - Fold07: mtry= 2 
#> + Fold07: mtry= 6 
#> - Fold07: mtry= 6 
#> + Fold07: mtry=11 
#> - Fold07: mtry=11 
#> + Fold08: mtry= 2 
#> - Fold08: mtry= 2 
#> + Fold08: mtry= 6 
#> - Fold08: mtry= 6 
#> + Fold08: mtry=11 
#> - Fold08: mtry=11 
#> + Fold09: mtry= 2 
#> - Fold09: mtry= 2 
#> + Fold09: mtry= 6 
#> - Fold09: mtry= 6 
#> + Fold09: mtry=11 
#> - Fold09: mtry=11 
#> + Fold10: mtry= 2 
#> - Fold10: mtry= 2 
#> + Fold10: mtry= 6 
#> - Fold10: mtry= 6 
#> + Fold10: mtry=11 
#> - Fold10: mtry=11 
#> Aggregating results
#> Selecting tuning parameters
#> Fitting mtry = 2 on full training set
#> + Fold01: mtry= 2 
#> - Fold01: mtry= 2 
#> + Fold01: mtry= 6 
#> - Fold01: mtry= 6 
#> + Fold01: mtry=11 
#> - Fold01: mtry=11 
#> + Fold02: mtry= 2 
#> - Fold02: mtry= 2 
#> + Fold02: mtry= 6 
#> - Fold02: mtry= 6 
#> + Fold02: mtry=11 
#> - Fold02: mtry=11 
#> + Fold03: mtry= 2 
#> - Fold03: mtry= 2 
#> + Fold03: mtry= 6 
#> - Fold03: mtry= 6 
#> + Fold03: mtry=11 
#> - Fold03: mtry=11 
#> + Fold04: mtry= 2 
#> - Fold04: mtry= 2 
#> + Fold04: mtry= 6 
#> - Fold04: mtry= 6 
#> + Fold04: mtry=11 
#> - Fold04: mtry=11 
#> + Fold05: mtry= 2 
#> - Fold05: mtry= 2 
#> + Fold05: mtry= 6 
#> - Fold05: mtry= 6 
#> + Fold05: mtry=11 
#> - Fold05: mtry=11 
#> + Fold06: mtry= 2 
#> - Fold06: mtry= 2 
#> + Fold06: mtry= 6 
#> - Fold06: mtry= 6 
#> + Fold06: mtry=11 
#> - Fold06: mtry=11 
#> + Fold07: mtry= 2 
#> - Fold07: mtry= 2 
#> + Fold07: mtry= 6 
#> - Fold07: mtry= 6 
#> + Fold07: mtry=11 
#> - Fold07: mtry=11 
#> + Fold08: mtry= 2 
#> - Fold08: mtry= 2 
#> + Fold08: mtry= 6 
#> - Fold08: mtry= 6 
#> + Fold08: mtry=11 
#> - Fold08: mtry=11 
#> + Fold09: mtry= 2 
#> - Fold09: mtry= 2 
#> + Fold09: mtry= 6 
#> - Fold09: mtry= 6 
#> + Fold09: mtry=11 
#> - Fold09: mtry=11 
#> + Fold10: mtry= 2 
#> - Fold10: mtry= 2 
#> + Fold10: mtry= 6 
#> - Fold10: mtry= 6 
#> + Fold10: mtry=11 
#> - Fold10: mtry=11 
#> Aggregating results
#> Selecting tuning parameters
#> Fitting mtry = 6 on full training set
#> + Fold01: mtry= 2 
#> - Fold01: mtry= 2 
#> + Fold01: mtry= 6 
#> - Fold01: mtry= 6 
#> + Fold01: mtry=11 
#> - Fold01: mtry=11 
#> + Fold02: mtry= 2 
#> - Fold02: mtry= 2 
#> + Fold02: mtry= 6 
#> - Fold02: mtry= 6 
#> + Fold02: mtry=11 
#> - Fold02: mtry=11 
#> + Fold03: mtry= 2 
#> - Fold03: mtry= 2 
#> + Fold03: mtry= 6 
#> - Fold03: mtry= 6 
#> + Fold03: mtry=11 
#> - Fold03: mtry=11 
#> + Fold04: mtry= 2 
#> - Fold04: mtry= 2 
#> + Fold04: mtry= 6 
#> - Fold04: mtry= 6 
#> + Fold04: mtry=11 
#> - Fold04: mtry=11 
#> + Fold05: mtry= 2 
#> - Fold05: mtry= 2 
#> + Fold05: mtry= 6 
#> - Fold05: mtry= 6 
#> + Fold05: mtry=11 
#> - Fold05: mtry=11 
#> + Fold06: mtry= 2 
#> - Fold06: mtry= 2 
#> + Fold06: mtry= 6 
#> - Fold06: mtry= 6 
#> + Fold06: mtry=11 
#> - Fold06: mtry=11 
#> + Fold07: mtry= 2 
#> - Fold07: mtry= 2 
#> + Fold07: mtry= 6 
#> - Fold07: mtry= 6 
#> + Fold07: mtry=11 
#> - Fold07: mtry=11 
#> + Fold08: mtry= 2 
#> - Fold08: mtry= 2 
#> + Fold08: mtry= 6 
#> - Fold08: mtry= 6 
#> + Fold08: mtry=11 
#> - Fold08: mtry=11 
#> + Fold09: mtry= 2 
#> - Fold09: mtry= 2 
#> + Fold09: mtry= 6 
#> - Fold09: mtry= 6 
#> + Fold09: mtry=11 
#> - Fold09: mtry=11 
#> + Fold10: mtry= 2 
#> - Fold10: mtry= 2 
#> + Fold10: mtry= 6 
#> - Fold10: mtry= 6 
#> + Fold10: mtry=11 
#> - Fold10: mtry=11 
#> Aggregating results
#> Selecting tuning parameters
#> Fitting mtry = 2 on full training set
#> + Fold01: mtry= 2 
#> - Fold01: mtry= 2 
#> + Fold01: mtry= 6 
#> - Fold01: mtry= 6 
#> + Fold01: mtry=11 
#> - Fold01: mtry=11 
#> + Fold02: mtry= 2 
#> - Fold02: mtry= 2 
#> + Fold02: mtry= 6 
#> - Fold02: mtry= 6 
#> + Fold02: mtry=11 
#> - Fold02: mtry=11 
#> + Fold03: mtry= 2 
#> - Fold03: mtry= 2 
#> + Fold03: mtry= 6 
#> - Fold03: mtry= 6 
#> + Fold03: mtry=11 
#> - Fold03: mtry=11 
#> + Fold04: mtry= 2 
#> - Fold04: mtry= 2 
#> + Fold04: mtry= 6 
#> - Fold04: mtry= 6 
#> + Fold04: mtry=11 
#> - Fold04: mtry=11 
#> + Fold05: mtry= 2 
#> - Fold05: mtry= 2 
#> + Fold05: mtry= 6 
#> - Fold05: mtry= 6 
#> + Fold05: mtry=11 
#> - Fold05: mtry=11 
#> + Fold06: mtry= 2 
#> - Fold06: mtry= 2 
#> + Fold06: mtry= 6 
#> - Fold06: mtry= 6 
#> + Fold06: mtry=11 
#> - Fold06: mtry=11 
#> + Fold07: mtry= 2 
#> - Fold07: mtry= 2 
#> + Fold07: mtry= 6 
#> - Fold07: mtry= 6 
#> + Fold07: mtry=11 
#> - Fold07: mtry=11 
#> + Fold08: mtry= 2 
#> - Fold08: mtry= 2 
#> + Fold08: mtry= 6 
#> - Fold08: mtry= 6 
#> + Fold08: mtry=11 
#> - Fold08: mtry=11 
#> + Fold09: mtry= 2 
#> - Fold09: mtry= 2 
#> + Fold09: mtry= 6 
#> - Fold09: mtry= 6 
#> + Fold09: mtry=11 
#> - Fold09: mtry=11 
#> + Fold10: mtry= 2 
#> - Fold10: mtry= 2 
#> + Fold10: mtry= 6 
#> - Fold10: mtry= 6 
#> + Fold10: mtry=11 
#> - Fold10: mtry=11 
#> Aggregating results
#> Selecting tuning parameters
#> Fitting mtry = 2 on full training set
#> + Fold01: mtry= 2 
#> - Fold01: mtry= 2 
#> + Fold01: mtry= 6 
#> - Fold01: mtry= 6 
#> + Fold01: mtry=11 
#> - Fold01: mtry=11 
#> + Fold02: mtry= 2 
#> - Fold02: mtry= 2 
#> + Fold02: mtry= 6 
#> - Fold02: mtry= 6 
#> + Fold02: mtry=11 
#> - Fold02: mtry=11 
#> + Fold03: mtry= 2 
#> - Fold03: mtry= 2 
#> + Fold03: mtry= 6 
#> - Fold03: mtry= 6 
#> + Fold03: mtry=11 
#> - Fold03: mtry=11 
#> + Fold04: mtry= 2 
#> - Fold04: mtry= 2 
#> + Fold04: mtry= 6 
#> - Fold04: mtry= 6 
#> + Fold04: mtry=11 
#> - Fold04: mtry=11 
#> + Fold05: mtry= 2 
#> - Fold05: mtry= 2 
#> + Fold05: mtry= 6 
#> - Fold05: mtry= 6 
#> + Fold05: mtry=11 
#> - Fold05: mtry=11 
#> + Fold06: mtry= 2 
#> - Fold06: mtry= 2 
#> + Fold06: mtry= 6 
#> - Fold06: mtry= 6 
#> + Fold06: mtry=11 
#> - Fold06: mtry=11 
#> + Fold07: mtry= 2 
#> - Fold07: mtry= 2 
#> + Fold07: mtry= 6 
#> - Fold07: mtry= 6 
#> + Fold07: mtry=11 
#> - Fold07: mtry=11 
#> + Fold08: mtry= 2 
#> - Fold08: mtry= 2 
#> + Fold08: mtry= 6 
#> - Fold08: mtry= 6 
#> + Fold08: mtry=11 
#> - Fold08: mtry=11 
#> + Fold09: mtry= 2 
#> - Fold09: mtry= 2 
#> + Fold09: mtry= 6 
#> - Fold09: mtry= 6 
#> + Fold09: mtry=11 
#> - Fold09: mtry=11 
#> + Fold10: mtry= 2 
#> - Fold10: mtry= 2 
#> + Fold10: mtry= 6 
#> - Fold10: mtry= 6 
#> + Fold10: mtry=11 
#> - Fold10: mtry=11 
#> Aggregating results
#> Selecting tuning parameters
#> Fitting mtry = 2 on full training set
#> + Fold01: mtry= 2 
#> - Fold01: mtry= 2 
#> + Fold01: mtry= 6 
#> - Fold01: mtry= 6 
#> + Fold01: mtry=11 
#> - Fold01: mtry=11 
#> + Fold02: mtry= 2 
#> - Fold02: mtry= 2 
#> + Fold02: mtry= 6 
#> - Fold02: mtry= 6 
#> + Fold02: mtry=11 
#> - Fold02: mtry=11 
#> + Fold03: mtry= 2 
#> - Fold03: mtry= 2 
#> + Fold03: mtry= 6 
#> - Fold03: mtry= 6 
#> + Fold03: mtry=11 
#> - Fold03: mtry=11 
#> + Fold04: mtry= 2 
#> - Fold04: mtry= 2 
#> + Fold04: mtry= 6 
#> - Fold04: mtry= 6 
#> + Fold04: mtry=11 
#> - Fold04: mtry=11 
#> + Fold05: mtry= 2 
#> - Fold05: mtry= 2 
#> + Fold05: mtry= 6 
#> - Fold05: mtry= 6 
#> + Fold05: mtry=11 
#> - Fold05: mtry=11 
#> + Fold06: mtry= 2 
#> - Fold06: mtry= 2 
#> + Fold06: mtry= 6 
#> - Fold06: mtry= 6 
#> + Fold06: mtry=11 
#> - Fold06: mtry=11 
#> + Fold07: mtry= 2 
#> - Fold07: mtry= 2 
#> + Fold07: mtry= 6 
#> - Fold07: mtry= 6 
#> + Fold07: mtry=11 
#> - Fold07: mtry=11 
#> + Fold08: mtry= 2 
#> - Fold08: mtry= 2 
#> + Fold08: mtry= 6 
#> - Fold08: mtry= 6 
#> + Fold08: mtry=11 
#> - Fold08: mtry=11 
#> + Fold09: mtry= 2 
#> - Fold09: mtry= 2 
#> + Fold09: mtry= 6 
#> - Fold09: mtry= 6 
#> + Fold09: mtry=11 
#> - Fold09: mtry=11 
#> + Fold10: mtry= 2 
#> - Fold10: mtry= 2 
#> + Fold10: mtry= 6 
#> - Fold10: mtry= 6 
#> + Fold10: mtry=11 
#> - Fold10: mtry=11 
#> Aggregating results
#> Selecting tuning parameters
#> Fitting mtry = 2 on full training set
#> + Fold01: mtry= 2 
#> - Fold01: mtry= 2 
#> + Fold01: mtry= 6 
#> - Fold01: mtry= 6 
#> + Fold01: mtry=11 
#> - Fold01: mtry=11 
#> + Fold02: mtry= 2 
#> - Fold02: mtry= 2 
#> + Fold02: mtry= 6 
#> - Fold02: mtry= 6 
#> + Fold02: mtry=11 
#> - Fold02: mtry=11 
#> + Fold03: mtry= 2 
#> - Fold03: mtry= 2 
#> + Fold03: mtry= 6 
#> - Fold03: mtry= 6 
#> + Fold03: mtry=11 
#> - Fold03: mtry=11 
#> + Fold04: mtry= 2 
#> - Fold04: mtry= 2 
#> + Fold04: mtry= 6 
#> - Fold04: mtry= 6 
#> + Fold04: mtry=11 
#> - Fold04: mtry=11 
#> + Fold05: mtry= 2 
#> - Fold05: mtry= 2 
#> + Fold05: mtry= 6 
#> - Fold05: mtry= 6 
#> + Fold05: mtry=11 
#> - Fold05: mtry=11 
#> + Fold06: mtry= 2 
#> - Fold06: mtry= 2 
#> + Fold06: mtry= 6 
#> - Fold06: mtry= 6 
#> + Fold06: mtry=11 
#> - Fold06: mtry=11 
#> + Fold07: mtry= 2 
#> - Fold07: mtry= 2 
#> + Fold07: mtry= 6 
#> - Fold07: mtry= 6 
#> + Fold07: mtry=11 
#> - Fold07: mtry=11 
#> + Fold08: mtry= 2 
#> - Fold08: mtry= 2 
#> + Fold08: mtry= 6 
#> - Fold08: mtry= 6 
#> + Fold08: mtry=11 
#> - Fold08: mtry=11 
#> + Fold09: mtry= 2 
#> - Fold09: mtry= 2 
#> + Fold09: mtry= 6 
#> - Fold09: mtry= 6 
#> + Fold09: mtry=11 
#> - Fold09: mtry=11 
#> + Fold10: mtry= 2 
#> - Fold10: mtry= 2 
#> + Fold10: mtry= 6 
#> - Fold10: mtry= 6 
#> + Fold10: mtry=11 
#> - Fold10: mtry=11 
#> Aggregating results
#> Selecting tuning parameters
#> Fitting mtry = 2 on full training set
#> + Fold01: mtry= 2 
#> - Fold01: mtry= 2 
#> + Fold01: mtry= 6 
#> - Fold01: mtry= 6 
#> + Fold01: mtry=11 
#> - Fold01: mtry=11 
#> + Fold02: mtry= 2 
#> - Fold02: mtry= 2 
#> + Fold02: mtry= 6 
#> - Fold02: mtry= 6 
#> + Fold02: mtry=11 
#> - Fold02: mtry=11 
#> + Fold03: mtry= 2 
#> - Fold03: mtry= 2 
#> + Fold03: mtry= 6 
#> - Fold03: mtry= 6 
#> + Fold03: mtry=11 
#> - Fold03: mtry=11 
#> + Fold04: mtry= 2 
#> - Fold04: mtry= 2 
#> + Fold04: mtry= 6 
#> - Fold04: mtry= 6 
#> + Fold04: mtry=11 
#> - Fold04: mtry=11 
#> + Fold05: mtry= 2 
#> - Fold05: mtry= 2 
#> + Fold05: mtry= 6 
#> - Fold05: mtry= 6 
#> + Fold05: mtry=11 
#> - Fold05: mtry=11 
#> + Fold06: mtry= 2 
#> - Fold06: mtry= 2 
#> + Fold06: mtry= 6 
#> - Fold06: mtry= 6 
#> + Fold06: mtry=11 
#> - Fold06: mtry=11 
#> + Fold07: mtry= 2 
#> - Fold07: mtry= 2 
#> + Fold07: mtry= 6 
#> - Fold07: mtry= 6 
#> + Fold07: mtry=11 
#> - Fold07: mtry=11 
#> + Fold08: mtry= 2 
#> - Fold08: mtry= 2 
#> + Fold08: mtry= 6 
#> - Fold08: mtry= 6 
#> + Fold08: mtry=11 
#> - Fold08: mtry=11 
#> + Fold09: mtry= 2 
#> - Fold09: mtry= 2 
#> + Fold09: mtry= 6 
#> - Fold09: mtry= 6 
#> + Fold09: mtry=11 
#> - Fold09: mtry=11 
#> + Fold10: mtry= 2 
#> - Fold10: mtry= 2 
#> + Fold10: mtry= 6 
#> - Fold10: mtry= 6 
#> + Fold10: mtry=11 
#> - Fold10: mtry=11 
#> Aggregating results
#> Selecting tuning parameters
#> Fitting mtry = 2 on full training set

Combined_Performance

Estimate_Models gives the results based on the splits of the exog. Combined_Performance prints out the total performance of these splits.

Sub.CCP.RF <- list(Mdl.1 = CCP.RF$EstMdl$`D.1+networth`,
Mdl.0 = CCP.RF$EstMdl$`D.0+networth`)
CCP.NoCCP.RF <- Combined_Performance (Sub.CCP.RF)