data.generation: Data generation function

In mvnimpute: Simultaneously Impute the Missing and Censored Values

Data generation function

Description

Simulates multivariate normal data with missing and censored values. In this function, missing values will be generated first in the multivariate data, then censored values will be generated for the non-missing data.

Usage

data.generation(
  num_ind = 2000,
  mean_vec = rnorm(5),
  cov_mat = diag(5),
  miss_var = c(2, 3),
  miss_mech = "MCAR",
  miss_prob = c(0.2, 0.4),
  censor_var = 4,
  censor_type = "interval",
  censor_param = 0.1
)

Arguments

num_ind	number of subjects.
mean_vec	mean vectors.
cov_mat	covariance matrix.
miss_var	variables that have missing values.
miss_mech	missing mechanism. "MCAR" or "MAR". Default "MCAR".
miss_prob	missing data probability when missing data is MCAR.
censor_var	variables that have censored values.
censor_type	type of censoring. "interval", "right" or "left. Default "interval".
censor_param	rate parameter of the exponential distribution that the censoring times come from.

Value

A list containing the fully observed data, the observed data, the bounds information of the observed data and the data type indicator matrix.

Examples

### generate a multivariate normal dataset of 2000 sample size
### using the default arguments
data.generation()

mvnimpute documentation built on July 6, 2022, 5:08 p.m.