delete_MNAR_censoring {missMethods}

Create MNAR values using a censoring mechanism

Description

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

Usage

delete_MNAR_censoring(
  ds,
  p,
  cols_mis,
  n_mis_stochastic = FALSE,
  where = "lower",
  sorting = TRUE,
  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.

n_mis_stochastic

Logical, should the number of missing values be stochastic? If n_mis_stochastic = TRUE, the number of missing values for a column with missing values cols_mis[i] is a random variable with expected value nrow(ds) * p[i]. If n_mis_stochastic = FALSE, the number of missing values will be deterministic. Normally, the number of missing values for a column with missing values cols_mis[i] is round(nrow(ds) * p[i]). Possible deviations from this value, if any exists, are documented in Details.

where

Controls where missing values are created; one of "lower", "upper" or "both" (see details).

sorting

Logical; should sorting be used or a quantile as a threshold.

miss_cols

Deprecated, use cols_mis instead.

Details

The functions delete_MNAR_censoring and delete_MAR_censoring are sisters. The only difference between these two functions is the column that controls the generation of missing values. In delete_MAR_censoring a separate column cols_ctrl[i] controls the generation of missing values in cols_mis[i]. In contrast, in delete_MNAR_censoring 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_censoring.

Value

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

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_censoring

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

Examples

ds <- data.frame(X = 1:20, Y = 101:120)
delete_MNAR_censoring(ds, 0.2, "X")