Create missing at random (MAR) values by deleting values in one of two groups in a data frame or a matrix
delete_MAR_one_group(
ds,
p,
cols_mis,
cols_ctrl,
cutoff_fun = median,
prop = 0.5,
use_lpSolve = TRUE,
ordered_as_unordered = FALSE,
stochastic = FALSE,
...,
miss_cols,
ctrl_cols
)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.
Function that calculates the cutoff values in the
cols_ctrl.
Numeric of length one; (minimum) proportion of rows in group 1 (only used for unordered factors).
Logical; should lpSolve be used for the determination of
groups, if cols_ctrl[i] is an unordered factor.
Logical; should ordered factors be treated as unordered factors.
Logical; see details.
Further arguments passed to cutoff_fun.
Deprecated, use cols_mis instead.
Deprecated, use cols_ctrl instead.
An object of the same class as ds with missing values.
If ds[, cols_ctrl[i]] is an unordered factor, then the concept of a
cutoff value is not meaningful and cannot be applied.
Instead, a combinations of the levels of the unordered factor is searched that
guarantees at least a proportion of prop rows are in group 1
minimize the difference between prop and the proportion of
rows in group 1.
This can be seen as a binary search problem, which is solved by the solver
from the package lpSolve, if use_lpSolve = TRUE.
If use_lpSolve = FALSE, a very simple heuristic is applied.
The heuristic only guarantees that at least a proportion of prop rows
are in group 1.
The choice use_lpSolve = FALSE is not recommend and should only be
considered, if the solver of lpSolve fails.
If ordered_as_unordered = TRUE, then ordered factors will be treated
like unordered factors and the same binary search problem will be solved for
both types of factors.
If ordered_as_unordered = FALSE (the default), then ordered factors
will be grouped via cutoff_fun as described in Details.
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 rows of ds are divided into two groups.
Therefore, the cutoff_fun calculates a cutoff value for
cols_ctrl[i] (via cutoff_fun(ds[, cols_ctrl[i]], ...).
The group 1 consists of the rows, whose values in
cols_ctrl[i] are below the calculated cutoff value.
If the so defined group 1 is empty, the rows that are equal to the
cutoff value will be added to this group (otherwise, these rows will
belong to group 2).
The group 2 consists of the remaining rows, which are not part of group 1.
Now one of these two groups is chosen randomly.
In the chosen group, values are deleted in cols_mis[i].
In the other group, no missing values will be created in cols_mis[i].
If stochastic = FALSE (the default), then floor(nrow(ds) * p[i])
or ceiling(nrow(ds) * p[i]) values will be set NA in
column cols_mis[i] (depending on the grouping).
If stochastic = TRUE, each value in the group with missing values
will have a probability to be missing, to meet a proportion of
p[i] of missing values in cols_mis[i] in expectation.
The effect of stochastic is further discussed in
delete_MCAR.
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
Other functions to create MAR:
delete_MAR_1_to_x(),
delete_MAR_censoring(),
delete_MAR_rank()
# NOT RUN {
ds <- data.frame(X = 1:20, Y = 101:120)
delete_MAR_one_group(ds, 0.2, "X", "Y")
# }
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