Create missing not at random (MNAR) values using a ranking mechanism in a data frame or a matrix
delete_MNAR_rank(
ds,
p,
cols_mis,
n_mis_stochastic = FALSE,
ties.method = "average",
miss_cols
)An object of the same class as ds with missing values.
A data frame or matrix in which missing values will be created.
A numeric vector with length one or equal to length cols_mis;
the probability that a value is missing.
A vector of column names or indices of columns in which missing values will be created.
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.
How ties are handled. Passed to rank.
Deprecated, use cols_mis instead.
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.
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
delete_MAR_rank
Other functions to create MNAR:
delete_MNAR_1_to_x(),
delete_MNAR_censoring(),
delete_MNAR_one_group()
ds <- data.frame(X = 1:20, Y = 101:120)
delete_MNAR_rank(ds, 0.2, "X")
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