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

Welcome to ClientVPS Mirrors

How To Use CGMissingDataR

CGMissingDataR

Installation

Example