delete_MNAR_1_to_x: Create MNAR values using MNAR1:x In missMethods: Methods for Missing Data

 delete_MNAR_1_to_x R Documentation

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,
...,
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

`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.