Inject MCAR, MAR, or MNAR missingness — inject

Blanks cells of data under one of the three standard mechanisms. "MCAR" gives every row the same probability proportion, so missingness depends on nothing. "MAR" makes it depend on the fully observed predictor, which is itself never made missing. "MNAR" makes each target variable's missingness depend on its own value, so the values that disappear are not a random sample of them. For MAR and MNAR the probability increases with the driving variable's rank: its ties-averaged rank mapped into (0, 1), or 0.5 for a missing or constant driver, scaled by the multiplier that makes the mean probability equal proportion, and capped at 1. Each MAR target therefore shares one probability vector, while each MNAR target gets its own.

Usage

inject_missingness(
  data,
  mechanism = c("MCAR", "MAR", "MNAR"),
  proportion = 0.1,
  variables = NULL,
  predictor = NULL,
  seed = NULL
)

Arguments

data: Complete base data.frame, or a simulab_sim, with at least one row.
mechanism: Missingness mechanism, one of "MCAR" (the default), "MAR" or "MNAR".
proportion: Target missing fraction, the mean cell probability rather than a realized count. A single number between 0 and 1, defaulting to 0.1; the realized fraction is random around it and is reported in the missingness_summary component.
variables: Variables to make missing. A character vector of column names, or NULL (the default), which selects every column of data, identifiers included. Under "MAR" the predictor is removed from this set.
predictor: Fully observed predictor that drives MAR missingness. A single string naming a column of data, required when mechanism is "MAR" and ignored otherwise. Defaults to NULL.
seed: Optional random seed. A single number, or NULL (the default).

Value

A simulab_sim base data.frame with the columns and rows of data, the drawn cells of the target variables replaced by NA. Components: missingness, the cell-level mask reached with as.data.frame(x, what = "missingness"), one row per observation and target variable with columns observation, variable, probability, the logical missing and mechanism; and missingness_summary, one row per target variable with columns variable, realized_proportion and target_proportion.

Examples

data <- data.frame(id = 1:200, x = stats::rnorm(200), y = stats::rnorm(200))

result <- inject_missingness(
  data, mechanism = "MCAR", proportion = 0.2, variables = "y", seed = 1
)
head(result)
#> <simulab_sim:missingness> 6 rows x 3 columns
#>   id          x          y
#> 1  1  1.0744410 -0.3410670
#> 2  2  1.8956548  1.5024245
#> 3  3 -0.6029973  0.5283077
#> 4  4 -0.3908678  0.5421914
#> 5  5 -0.4162220 -0.1366734
#> 6  6 -0.3756574 -1.1367339
#> 
#> Other tables: missingness, missingness_summary. Read one with as.data.frame(x, what = "missingness").

# MAR missingness in `y` driven by the observed predictor `x`.
head(inject_missingness(
  data, mechanism = "MAR", proportion = 0.2,
  variables = "y", predictor = "x", seed = 1
))
#> <simulab_sim:missingness> 6 rows x 3 columns
#>   id          x          y
#> 1  1  1.0744410         NA
#> 2  2  1.8956548         NA
#> 3  3 -0.6029973  0.5283077
#> 4  4 -0.3908678  0.5421914
#> 5  5 -0.4162220 -0.1366734
#> 6  6 -0.3756574 -1.1367339
#> 
#> Other tables: missingness, missingness_summary. Read one with as.data.frame(x, what = "missingness").