missing: Missing Data Functions

Description Usage Arguments

Description

Function that inputs simulated data and returns data frame with new response variable that includes missing data. Missing data types incorporated include dropout missing data, missing at random, and random missing data.

Usage

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missing_data(
  sim_data,
  resp_var = "sim_data",
  new_outcome = "sim_data2",
  clust_var = NULL,
  within_id = NULL,
  miss_prop = NULL,
  dropout_location = NULL,
  type = c("dropout", "random", "mar"),
  miss_cov,
  mar_prop
)

dropout_missing(
  sim_data,
  resp_var = "sim_data",
  new_outcome = "sim_data2",
  clust_var = "clustID",
  within_id = "withinID",
  miss_prop = NULL,
  dropout_location = NULL
)

random_missing(
  sim_data,
  resp_var = "sim_data",
  new_outcome = "sim_data2",
  miss_prop,
  clust_var = NULL,
  within_id = "withinID"
)

mar_missing(
  sim_data,
  resp_var = "sim_data",
  new_outcome = "sim_data2",
  miss_cov,
  mar_prop
)

Arguments

sim_data

Simulated data frame

resp_var

Character string of response variable with complete data.

new_outcome

Character string of new outcome variable name that includes the missing data.

clust_var

Cluster variable used for the grouping, set to NULL by default which means no clustering.

within_id

ID variable within each cluster.

miss_prop

Proportion of missing data overall

dropout_location

A vector the same length as the number of clusters representing the number of data observations for each individual.

type

The type of missing data to generate, currently supports droput, random, or missing at random (mar) missing data.

miss_cov

Covariate that the missing values are based on.

mar_prop

Proportion of missing data for each unique value specified in the miss_cov argument.


simglm documentation built on July 8, 2020, 5:46 p.m.