calculatePower: Estimate the power of the random thinning test based on the...
In JeremySilver/RandomThinning: Random Thinning Tests and Auxiliary Functions

Description Usage Arguments Value Examples

Estimate the power of the random thinning test based on the properties a given time-series

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calculatePower(ts, p, nMC = 1000, nThin = 1000, its = NULL,
  fr = c(0.001, 0.025, 0.01, 0.025, 0.1), pThreshold = 0.05,
  useNApattern = FALSE)

`ts`	A time-series (a numeric vector)
`p`	Length of the period to test for (a positive integer)
`nMC`	Number of samples to estimate the power (a positive integer)
`nThin`	Number of resamples to use in the random thinning test (a positive integer)
`its`	Indices of which bin the values of x correspond to (a numeric vector of integers between 1 and p). This is optional, and taken to be cyclical by default.
`fr`	Which fractions to consider discarding (a numeric vector, with values between 0 and 1, although it is best if they are kept between between 0.001 and 0.2)
`pThreshold`	P-value threshold to use when calculating the power (a positive scalar between 0 and 1, although it is recommended to be around 0.05)
`useNApattern`	Logical value - if `TRUE`, the synthetic time-series inherit the same missingness pattern as in the original time-series

A numeric vector, with one entry per value of fr, giving the power of the test for this particular value

## Generate a random time-series:
##  - length = 1000
##  - signal period = 8
##  - signal to noise ratio = 0.2
set.seed(42)

x <- rep(rnorm(8)*0.2,length.out = 1000) + rnorm(1000)
calculatePower(ts = x, p = 8, nMC = 200, nThin = 200,
                           fr = c(0.01,0.025,0.1))