test_missForest: Testing the 'missForest' missing data imputation algorithm
In missCompare: Intuitive Missing Data Imputation Framework

Description Usage Arguments Details Value Examples

test_missForest tests the imputation accuracy of the 'missForest' missing data imputation algorithm on matrices with various missing data patterns

1	test_missForest(X_hat, list)

`X_hat`	Simulated matrix with no missingness (this matrix will be used to obtain the error between the original and imputed values). (Simulated_matrix output from the `simulate` function)
`list`	List of matrices with various missingness patterns (MCAR, MAR, MNAR and optionally, MAP). (The input is ideally the R object that was generated using the `all_patterns` function)

This function tests the imputation accuracy of the 'missForest' missing data imputation algorithm by comparing the original simulated matrix with no missingness and the imputed matrices generated by the algorithm using the matrices with MCAR, MAR, MNAR and (optionally) MAP missingness patterns. The function calculates root-mean-square error (RMSE), mean absolute error (MAE), Kolmogorov–Smirnov D test statistic (KS) between the imputed datapoints and the original datapoints (that were subsequently set to missing). The function will also calculate the cumulative computation time for imputing all datasets. The function will automatically detect whether there is a MAP matrix in the list and calculate RMSE for all matrices provided in the list.

`Comp_time`	Computation time of imputation using method (default output)
`MCAR_RMSE`	Root-mean-square error (RMSE) between the indexed original values and the imputed values in an MCAR missingness pattern (default output)
`MAR_RMSE`	Root-mean-square error (RMSE) between the indexed original values and the imputed values in an MAR missingness pattern (default output)
`MNAR_RMSE`	Root-mean-square error (RMSE) between the indexed original values and the imputed values in an MNAR missingness pattern (default output)
`MAP_RMSE`	Root-mean-square error (RMSE) between the indexed original values and the imputed values in an MAP missingness pattern (optional output)
`MCAR_MAE`	Mean absolute error (MAE) between the indexed original values and the imputed values in an MCAR missingness pattern (default output)
`MAR_MAE`	Mean absolute error (MAE) between the indexed original values and the imputed values in an MAR missingness pattern (default output)
`MNAR_MAE`	Mean absolute error (MAE) between the indexed original values and the imputed values in an MNAR missingness pattern (default output)
`MAP_MAE`	Mean absolute error (MAE) between the indexed original values and the imputed values in an MAP missingness pattern (optional output)
`MCAR_KS`	Kolmogorov–Smirnov test statistic (KS) between the indexed original values and the imputed values in an MCAR missingness pattern (default output)
`MAR_KS`	Kolmogorov–Smirnov test statistic (KS) between the indexed original values and the imputed values in an MAR missingness pattern (default output)
`MNAR_KS`	Kolmogorov–Smirnov test statistic (KS) between the indexed original values and the imputed values in an MNAR missingness pattern (default output)
`MAP_KS`	Kolmogorov–Smirnov test statistic (KS) between the indexed original values and the imputed values in an MAP missingness pattern (optional output)

clindata_miss_mini <- clindata_miss[1:80,1:4]
cleaned <- clean(clindata_miss_mini, missingness_coding = -9)
metadata <- get_data(cleaned)
simulated <- simulate(rownum = metadata$Rows, colnum = metadata$Columns,
cormat = metadata$Corr_matrix)
miss_list <- all_patterns(simulated$Simulated_matrix,
                    MD_pattern = metadata$MD_Pattern,
                    NA_fraction = metadata$Fraction_missingness,
                    min_PDM = 2)

test_missForest(X_hat = simulated$Simulated_matrix, list = miss_list)