delete_MAR_one_group {missMethods}

Create MAR values by deleting values in one of two groups

Description

Create missing at random (MAR) values by deleting values in one of two groups in a data frame or a matrix

Usage

delete_MAR_one_group(
  ds,
  p,
  cols_mis,
  cols_ctrl,
  cutoff_fun = median,
  prop = 0.5,
  use_lpSolve = TRUE,
  ordered_as_unordered = FALSE,
  n_mis_stochastic = FALSE,
  ...,
  miss_cols,
  ctrl_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.

cols_ctrl

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.

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.

...

Further arguments passed to cutoff_fun.

miss_cols

Deprecated, use cols_mis instead.

ctrl_cols

Deprecated, use cols_ctrl instead.

stochastic

Deprecated, use n_mis_stochastic instead.

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 p is too high, it is possible that a group contains not enough objects to reach nrow(ds) * p missing values. In this case, p is reduced to the maximum possible value (given the (random) group with missing data) and a warning is given. Obviously this case will occur regularly, if p > 0.5. Therefore, this function should normally not be called with p > 0.5. However, this can occur for smaller values of p, too (depending on the grouping). The warning can be silenced by setting the option missMethods.warn.too.high.p to false.

Value

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

Treatment of factors

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.

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_MNAR_one_group

Other functions to create MAR: delete_MAR_1_to_x(), delete_MAR_censoring(), delete_MAR_rank()

Examples

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