binary_exposure: Simulate binary exposure data
In eesim: Simulate and Evaluate Time Series for Environmental Epidemiology

Description Usage Arguments Value Examples

Simulates a time series of binary exposure values with or without seasonal trends.

1
2
3

binary_exposure(n, p, trend = "no trend", slope, amp = 0.05,
  start.date = "2000-01-01", cust_expdraw = NULL,
  cust_expdraw_args = list(), custom_func = NULL, ...)

`n`	A non-negative integer specifying the number of days to simulate.
`p`	A numeric value between 0 and 1 giving the mean probability of exposure across study days.
`trend`	A character string that gives the trend function to use. Options are: "no trend": No trend, either seasonal or long-term (default). "cos1": A seasonal trend only. "cos2": A seasonal trend with variable amplitude across years. "cos3": A seasonal trend with steadily decreasing amplitude over time. "linear": A linear long-term trend with no seasonal trend. "monthly": Uses a user-specified probability of exposure for each month.
`slope`	A numeric value specifying the slope of the trend, to be used with `trend = "linear"` or `trend = "cos1linear"`.
`amp`	A numeric value specifying the amplitude of the seasonal trend. Must be between -.5 and .5.
`start.date`	A date of the format "yyyy-mm-dd" from which to begin simulating daily exposures
`cust_expdraw`	An R object name specifying a user-created function which determines the distribution of random noise off of the trend line. This function must have inputs "n" and "prob" and output a vector of simulated exposure values.
`cust_expdraw_args`	A list of arguments other than `n` required by the `cust_expdraw` function.
`custom_func`	An R object specifying a customized function from which to create a trend variable. Must accept arguments `n` and `p`.
`...`	Optional arguments to a custom trend function

A data frame with columns for the dates and daily exposure values for n days.

binary_exposure(n = 5, p = 0.1, trend = "cos1", amp = .02,
                start.date = "2001-02-01")
binary_exposure(n=10, p=.1, cust_expdraw=rnbinom,
                cust_expdraw_args=list(size=10))