---
title: "Pomodoro_Vignette"
author: "Seyma Kalay"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Pomodoro_Vignette}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
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.

```{r RF, echo=FALSE}
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")
BchMk.RF <- RF_Model(sample_data, c(xvar, "networth"), yvar )
BchMk.RF$Roc$auc
```


### 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.

```{r EstModel, echo=T}
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"))
```

### Combined_Performance

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

```{r ComPerf, echo=T}
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)
```

