delete_MNAR_1_to_x: Create MNAR values using MNAR1:x

In missMethods: Methods for Missing Data

Create MNAR values using MNAR1:x

Description

Create missing not at random (MNAR) values using MNAR1:x in a data frame or a matrix

Usage

delete_MNAR_1_to_x(
  ds,
  p,
  cols_mis,
  x,
  cutoff_fun = median,
  prop = 0.5,
  use_lpSolve = TRUE,
  ordered_as_unordered = FALSE,
  n_mis_stochastic = FALSE,
  x_stochastic = FALSE,
  add_realized_x = FALSE,
  ...,
  miss_cols,
  stochastic
)

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.
x	Numeric with length one (0 < x < Inf); odds are 1 to x for the probability of a value to be missing in group 1 against the probability of a value to be missing in group 2 (see details).
cutoff_fun	Function that calculates the cutoff values in the cols_ctrl.
prop	Numeric of length one; (minimum) proportion of rows in group 1 (only used for unordered factors).
use_lpSolve	Logical; should lpSolve be used for the determination of groups, if cols_ctrl[i] is an unordered factor.
ordered_as_unordered	Logical; should ordered factors be treated as unordered factors.
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.
x_stochastic	Logical; should the odds be stochastic or deterministic.
add_realized_x	Logical; if TRUE the realized odds for cols_mis will be returned (as attribute).
...	Further arguments passed to cutoff_fun.
miss_cols	Deprecated, use cols_mis instead.
stochastic	Deprecated, use n_mis_stochastic instead.

Details

The functions delete_MNAR_1_to_x and delete_MAR_1_to_x are sisters. The only difference between these two functions is the column that controls the generation of missing values. In delete_MAR_1_to_x a separate column cols_ctrl[i] controls the generation of missing values in cols_mis[i]. In contrast, in delete_MNAR_1_to_x 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_1_to_x.

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_1_to_x

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

Examples

ds <- data.frame(X = 1:20, Y = 101:120)
delete_MNAR_1_to_x(ds, 0.2, "X", x = 3)

missMethods documentation built on Sept. 16, 2022, 5:08 p.m.