missing_control: Control parameters for synth_missing
In julianhatwell/arulesimp: Association Rules Based Method for Imputation

Control the parameters for generating patterns of missing data

1 2	missing_control(pattern, method, nr_cols, dep_cols, unobs_cols, mm_cols, beta_0, betas, prob, exact)

`pattern`	A character string indicating whether missing data will occur in a MCAR, MAR or MNAR pattern
`method`	Optional character string. Allowed values are "princomp", "carpita" and "wu_ranking" if pattern = MAR, and MNAR
`nr_cols`	A character vector containing names of columns subject to missingness. If empty, all will be used.
`dep_cols`	A character vector containing names of columns to be used as covariates for patterns MAR and MNAR
`unobs_cols`	A character vector containing names of covariates that will not be included in the output.
`beta_0`	A numeric scalar for the intercept term in carpita models
`betas`	A numeric vector of length = length(dep_cols) of coefficients for covariates to affect the missingness. Recommded values -3 < x < 3.
`prob`	A numeric scalar with value 0 < prob < 1. mutually exclusive with exact. specifies the proportion of included variables that will contain missingness.
`exact`	An integer scalar with value > 0 and < maximum number of data points subject to missingness. specifies an exact number of data points to substitue. mutually exclusive with prob.