Generate missing values in a times-series — generate

First and last values are nor missing. Missing indices can be generated at random (Binomial distribution) or using a Markov Chain (if consecutive missing values are deemed more likely). The markov chain is parametrised in terms of the steady state probability of a value being missing and the probability that the next value is observed when the current value is also observed.

Usage

generate_missing(
  N,
  type = c("random", "markovchain"),
  p_mis = 0.25,
  p_obs_obs = 0.75
)

Arguments

N: Length of the time-series
type: Method to generate the missing values. One of "random" or "markovchain"
p_mis: Probability of a given value to be missing (steady state probability for type = "markovchain")
p_obs_obs: Probability of the next value being observed when the current is observed (for type = "markovchain")

Value

Logical vector of length N

Examples

generate_missing(10)
#>  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
generate_missing(10, type = "markovchain")
#>  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE