Create missing at random (MAR) values using a ranking mechanism in a data frame or a matrix
delete_MAR_rank(
ds,
p,
cols_mis,
cols_ctrl,
n_mis_stochastic = FALSE,
ties.method = "average",
miss_cols,
ctrl_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.
A vector of column names or indices of columns, which
controls the creation of missing values in cols_mis. Must be of the
same length as cols_mis.
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.
Deprecated, use cols_ctrl instead.
This function creates missing at random (MAR) values in the columns
specified by the argument cols_mis.
The probability for missing values is controlled by p.
If p is a single number, then the overall probability for a value to
be missing will be p in all columns of cols_mis.
(Internally p will be replicated to a vector of the same length as
cols_mis.
So, all p[i] in the following sections will be equal to the given
single number p.)
Otherwise, p must be of the same length as cols_mis.
In this case, the overall probability for a value to be missing will be
p[i] in the column cols_mis[i].
The position of the missing values in cols_mis[i] is controlled by
cols_ctrl[i].
The following procedure is applied for each pair of cols_ctrl[i] and
cols_mis[i] to determine the positions of missing values:
At first, the probability for a value to be missing is calculated. This
probability for a missing value in a row of cols_mis[i] is
proportional to the rank of the value in cols_ctrl[i] in the same row.
If n_mis_stochastic = FALSE these probabilities are given to the
prob argument of sample. If n_mis_stochastic
= TRUE, they are scaled to sum up to nrow(ds) * p[i]. Then for each
probability a uniformly distributed random number is generated. If this
random number is less than the probability, the value in cols_mis[i]
is set NA.
The ranks are calculated via rank.
The argument ties.method is directly passed to this function.
Possible choices for ties.method are documented in
rank.
For high values of p it is mathematically not possible to get
probabilities proportional to the ranks. In this case, a warning is given.
This warning can be silenced by setting the option
missMethods.warn.too.high.p to false.
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
rank, delete_MNAR_rank
Other functions to create MAR:
delete_MAR_1_to_x(),
delete_MAR_censoring(),
delete_MAR_one_group()
ds <- data.frame(X = 1:20, Y = 101:120)
delete_MAR_rank(ds, 0.2, "X", "Y")
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