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Generate Simulated Data of Seasonal Waves as a tsd object

Source: R/generate_seasonal_data.R
generate_seasonal_data.Rd

This function generates a simulated dataset of seasonal waves with trend and noise. This function assumes 365 days, 52 weeks, and 12 months per year. Leap years are not included in the calculation.

Usage

generate_seasonal_data(
  years = 3,
  start_date = as.Date("2021-05-26"),
  amplitude = 100,
  mean = 100,
  phase = 0,
  trend_rate = NULL,
  noise_overdispersion = NULL,
  relative_epidemic_concentration = 1,
  time_interval = c("weeks", "days", "months"),
  lower_bound = 1e-06,
  samples = NULL
)

Arguments

years

An integer specifying the number of years of data to simulate.

start_date

A date representing the start date of the simulated data.

amplitude

A number specifying the amplitude of the seasonal wave. The output will fluctuate within the range [mean - amplitude, mean + amplitude].

mean

A number specifying the mean of the seasonal wave.

phase

A numeric value (in radians) representing the horizontal shift of the sine wave, hence the phase shift of the seasonal wave. The phase must be between zero and 2*pi.

trend_rate

A numeric value specifying the exponential growth/decay rate.

noise_overdispersion

A numeric value specifying the overdispersion of the generated data. 0 means deterministic, 1 gives Poisson or binomial noise, and values greater than one use a negative binomial distribution for count data or a beta-binomial distribution for binomial data.

relative_epidemic_concentration

A numeric that transforms the reference sinusoidal season. A value of 1 gives the pure sinusoidal curve, and greater values concentrate the epidemic around the peak.

time_interval

A character vector specifying the time interval. Choose between 'days', 'weeks', or 'months'.

lower_bound

A numeric value that can be used to ensure that intensities are always greater than zero, which is needed for noisy count data. Binomial probabilities are allowed to remain zero.

samples

An optional positive integer specifying a constant number of samples tested at each time point, or an integer vector with one value per generated time point. When supplied, mean, amplitude, and the resulting seasonal wave are interpreted as proportions, and cases are drawn from a binomial distribution. In this mode, noise_overdispersion = 1 (or NULL) gives binomial variation, values greater than one give quasi-binomial variation using a beta-binomial distribution, and zero gives deterministic rounded counts.

Value

A tsd object with simulated data containing:

  • 'time': The time point for the corresponding data.

  • 'cases': The number of cases at the time point.

  • 'proportion': The proportion of positive samples for binomial data. (optional)

  • 'samples': The number of samples tested for binomial data. (optional)

Examples

# Generate simulated data of seasonal waves

#With default arguments
default_sim <- generate_seasonal_data()
plot(default_sim)


#With an exponential growth rate trend
trend_sim <- generate_seasonal_data(trend_rate = 1.001)
plot(trend_sim)


#With noise
noise_sim <- generate_seasonal_data(noise_overdispersion = 2)
plot(noise_sim)


# With binomial data (positive samples out of samples tested)
binomial_sim <- generate_seasonal_data(mean = 0.3, amplitude = 0.2, samples = 100)
plot(binomial_sim)


#With distinct parameters, trend and noise
sim_data <- generate_seasonal_data(
  years = 2,
  start_date = as.Date("2022-05-26"),
  amplitude = 2000,
  mean = 3000,
  trend_rate = 1.002,
  noise_overdispersion = 1.1,
  time_interval = c("weeks")
)
plot(sim_data, time_interval = "2 months")