Description Usage Arguments Value Examples
Estimate window thresholds for sliding window, one unique value for each window size
1 2 | estimateWindowThreshold(nProbe, windowSize, method = "siegmund",
mcmc = 1000, nCPU = 1, submethod = "ar", ...)
|
nProbe |
The number of probes (CpGs) in the study. |
windowSize |
The different window sizes to be tested. Must be either one, or an ordered sequence of integers. |
method |
Gives the method by which the threshold is calculated. Can be either an analytical solution "siegmund", provided by Siegnumd et.al (2012), or an iterative process; either importance sampling "sampling", as suggested by Zhang (2012) or a full MCMC model "mcmc" which can account for any dependency structure, wich is pass to arima.sim, with ... |
mcmc |
The number of MCMC iterations to be used, when using either Important Sampling ("zhang") or MCMC estimation of the threshold. |
nCPU |
When calculating the thresholds on a cluster, how many CPUs should be used. This option is only compatible with the 'mcmc' method. |
submethod |
A character string indicating if an AR(5) or ARIMA model should be used. In the AR(5), the index runs from -2 to 2. A regular AR(p) model can be obtaine using ARIMA(p,0,0) instead. |
... |
Optinal parameters pased on to |
Returns a vector of the threshold for each window size
1 2 | thresholdGrid <- estimateWindowThreshold(nProbe = 1000,
windowSize = 3:8, method = "siegmund")
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