all_patterns: Missing data spike-in in various missing data patterns

In missCompare: Intuitive Missing Data Imputation Framework

Description

all_patterns spikes in missingness using MCAR, MAR, MNAR (default) and MAP (optional) patterns

Usage

all_patterns(
  X_hat,
  MD_pattern,
  NA_fraction,
  min_PDM = 10,
  assumed_pattern = NA
)

Arguments

X_hat	Simulated matrix with no missingness (Simulated_matrix output from the simulate function)
MD_pattern	Missing data pattern in the original dataset (MD_Pattern output from the get_data function)
NA_fraction	Fraction of missingness in the original dataset (Fraction_missingness output from the get_data function)
min_PDM	All patterns with number of observations less than this number will be removed from the missing data generation. This argument is necessary to be carefully set, as the function will fail or generate erroneous missing data patterns with very complicated missing data patterns. The default is 10, but for large datasets this number needs to be set higher to avoid errors. Please select a value based on the min_PDM_thresholds output from the get_data function
assumed_pattern	Vector of missingness types (must be same length as missingness fraction per variable). If this input is specified, the function will spike in missing datapoints in a MAP pattern as well.

Details

This function uses the generated simulated matrix and generates missing datapoints in MCAR, MAR and MNAR patterns. Optionally, in case the user defines an assumed pattern, the all_patterns function will also generate a MAP missingness pattern. It is suggested that the user carefully examines the missing data fractions, excludes variables with high missingness using the clean function. For more information on the functions that spike in missing data in MCAR, MAR, MNAR and MAP patterns, please see the functions MCAR, MAR, MNAR and MAP.

Value

MCAR_matrix	Matrix with MCAR pre-defined missingness pattern (default output)
MAR_matrix	Matrix with MAR pre-defined missingness pattern (default output)
MNAR_matrix	Matrix with MNAR pre-defined missingness pattern (default output)
MAP_matrix	Matrix with MAP pre-defined missingness pattern (optional output)

Examples

cleaned <- clean(clindata_miss, 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 = 20)

miss_list <- all_patterns(simulated$Simulated_matrix,
                    MD_pattern = metadata$MD_Pattern,
                    NA_fraction = metadata$Fraction_missingness,
                    min_PDM = 10,
                    assumed_pattern = c('MAR', 'MCAR', 'MCAR', 'MAR',
                                        'MNAR', 'MCAR', 'MAR', 'MCAR',
                                        'MCAR', 'MAR', 'MNAR'))

missCompare documentation built on Dec. 1, 2020, 9:09 a.m.