# How To Use CGMissingDataR

# CGMissingDataR

CGMissingDataR is an R package based on the CGMissingData Python library for evaluating model performance under feature missingness by:

- injecting missing values into feature columns at specified masking rates,
- imputing missing values using a Multiple Imputation by Chained Equations (MICE)-style iterative imputer, and
- training Random Forest and k-Nearest Neighbors regressors to report Mean ABsolute Percentage Error (MAPE) and R across missingness levels.

## Installation

Before the installation, ensure that you have the following R packages installed:

    install.packages(c("FNN", "ranger", "mice"))

Install the development version of CGMissingDataR from GitHub:

    devtools::install_github("saraswatsh/CGMissingDataR")

## Example

Below is a brief example illustrating the usage of CGMissingDataR.

    library(CGMissingDataR)

    # Load example dataset
    data("CGMExampleData")
    results <- run_missingness_benchmark(CGMExampleData, mask_rates = c(0.05, 0.10, 0.15, 0.20),target_col = "LBORRES", # Running the missingness benchmark
    feature_cols = c("TimeDifferenceMinutes", "TimeSeries", "USUBJID"))
    #> Warning: Number of logged events: 1
    #> Warning: Number of logged events: 1
    #> Warning: Number of logged events: 1
    #> Warning: Number of logged events: 1
    print(results) # Displaying the results
    #>   MaskRate         Model      MAPE        R2
    #> 1       5% Random Forest  7.497932 0.7418421
    #> 2       5%           kNN  7.931831 0.7261704
    #> 3      10% Random Forest  8.510749 0.6683246
    #> 4      10%           kNN  9.157076 0.6309118
    #> 5      15% Random Forest  9.758954 0.5598508
    #> 6      15%           kNN 10.344360 0.5207368
    #> 7      20% Random Forest 10.189505 0.5363248
    #> 8      20%           kNN 10.766039 0.4920512