std_exposure: Simulate exposure data using default methods
In eesim: Simulate and Evaluate Time Series for Environmental Epidemiology

Description Usage Arguments Value Examples

Simulates binary or continuous exposure data with or without seasonal trends using default functions.

1 2	std_exposure(n, central, sd = NULL, trend = "no trend", exposure_type = "binary", slope, amp, start.date = "2000-01-01", ...)

`n`	A non-negative integer specifying the number of days to simulate.
`central`	A numeric value specifying the mean probability of exposure (for binary data) or the mean exposure value (for continuous data).
`sd`	A numeric value giving the standard deviation of the exposure values from the exposure trend line.
`trend`	A character string that specifies the desired trend function. 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. "curvilinear": A curved long-term trend with no seasonal trend. "cos1linear": A seasonal trend plus a linear long-term trend. See the package vignette for examples of the shapes of these trends.
`exposure_type`	A character string specifying the type of exposure. Choices are "binary" or "continuous".
`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 -1 and 1.
`start.date`	A date of the format "yyyy-mm-dd" from which to begin simulating daily exposures
`...`	Optional arguments to a custom trend function

A data frame with two columns: date (date) and simulated exposure values (x).

std_exposure(n = 5, central = .1, trend = "cos1", amp = .02)
std_exposure(n = 5, central = 50, sd = 5, trend = "cos3", amp = .6,
             exposure_type = "continuous", start.date = "2001-04-01")
std_exposure(n=50, central=.1, amp=.05,cust_expdraw=rnbinom,
             cust_expdraw_args=list(size=10))