missing: Missing Data Functions

In simglm: Simulate Models Based on the Generalized Linear Model

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

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 dropout, 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 Feb. 7, 2022, 9:08 a.m.