generate_data: Generate simulated data
In inbo/multimput: Using Multiple Imputation to Address Missing Data

generate_data

R Documentation

Generate simulated data

Description

Generate data for a regular monitoring design. The counts follow a negative binomial distribution with given size parameters and the true mean mu depending on a year, period and site effect. All effects are independent from each other and have, on the log-scale, a normal distribution with zero mean and given standard deviation.

Usage

generate_data(
  intercept = 2,
  n_year = 24,
  n_period = 6,
  n_site = 20,
  year_factor = FALSE,
  period_factor = FALSE,
  site_factor = FALSE,
  trend = 0.01,
  sd_rw_year = 0.1,
  amplitude_period = 1,
  mean_phase_period = 0,
  sd_phase_period = 0.2,
  sd_site = 1,
  sd_rw_site = 0.02,
  sd_noise = 0.01,
  size = 2,
  n_run = 10,
  as_list = FALSE,
  details = FALSE
)

Arguments

`intercept`	The global mean on the log-scale.
`n_year`	The number of years.
`n_period`	The number of periods.
`n_site`	The number of sites.
`year_factor`	Convert year to a factor. Defaults to `FALSE`.
`period_factor`	Convert period to a factor. Defaults to `FALSE`.
`site_factor`	Convert site to a factor. Defaults to `FALSE`.
`trend`	The long-term linear trend on the log-scale.
`sd_rw_year`	The standard deviation of the year effects on the log-scale.
`amplitude_period`	The amplitude of the periodic effect on the log-scale.
`mean_phase_period`	The mean of the phase of the periodic effect among years. Defaults to `0`.
`sd_phase_period`	The standard deviation of the phase of the periodic effect among years.
`sd_site`	The standard deviation of the site effects on the log-scale.
`sd_rw_site`	The standard deviation of the random walk along year per site on the log-scale.
`sd_noise`	The standard deviation of the noise effects on the log-scale.
`size`	The size parameter of the negative binomial distribution.
`n_run`	The number of runs with the same mu.
`as_list`	Return the dataset as a list rather than a data.frame. Defaults to `FALSE`.
`details`	Add variables containing the year, period and site effects. Defaults tot `FALSE`.

Value

A data.frame with five variables. Year, Month and Site are factors identifying the location and time of monitoring. Mu is the true mean of the negative binomial distribution in the original scale. Count are the simulated counts.