Author: Seyma Kalay
Date: 2022-03-26

# Pomodoro_Vignette

#### Seyma Kalay

#### 2022-03-26

    library(pomodoro)

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: 0.7598

### 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 = 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 = 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 = 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

### 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)