missMethods (version 0.2.0)

delete_MNAR_rank: Create MNAR values using a ranking mechanism

Description

Create missing not at random (MNAR) values using a ranking mechanism in a data frame or a matrix

Usage

delete_MNAR_rank(ds, p, cols_mis, ties.method = "average", miss_cols)

Arguments

ds

A data frame or matrix in which missing values will be created.

p

A numeric vector with length one or equal to length cols_mis; the probability that a value is missing.

cols_mis

A vector of column names or indices of columns in which missing values will be created.

ties.method

How ties are handled. Passed to rank.

miss_cols

Deprecated, use cols_mis instead.

Value

An object of the same class as ds with missing values.

Details

The functions delete_MNAR_rank and delete_MAR_rank are sisters. The only difference between these two functions is the column that controls the generation of missing values. In delete_MAR_rank a separate column cols_ctrl[i] controls the generation of missing values in cols_mis[i]. In contrast, in delete_MNAR_rank the generation of missing values in cols_mis[i] is controlled by cols_mis[i] itself. All other aspects are identical for both functions. Therefore, further details can be found in delete_MAR_rank.

References

Santos, M. S., Pereira, R. C., Costa, A. F., Soares, J. P., Santos, J., & Abreu, P. H. (2019). Generating Synthetic Missing Data: A Review by Missing Mechanism. IEEE Access, 7, 11651-11667

See Also

delete_MAR_rank

Other functions to create MNAR: delete_MNAR_1_to_x(), delete_MNAR_censoring(), delete_MNAR_one_group()

Examples

# NOT RUN {
ds <- data.frame(X = 1:20, Y = 101:120)
delete_MNAR_rank(ds, 0.2, "X")
# }