estimateThreshold {DMRScan} | R Documentation

## EstimateWindowThresholds

### Description

Estimate window thresholds for sliding window, one unique value for each window size

### Usage

    estimateWindowThreshold(nProbe, windowSize, method = "siegmund",
      mcmc = 1000, nCPU = 1, submethod = "ar", ...)

### Arguments

`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 `arima`, when simulating data using the mcmc option, see arima.sim()

### Value

Returns a vector of the threshold for each window size

### Examples

    thresholdGrid <- estimateWindowThreshold(nProbe = 1000,
                                        windowSize = 3:8, method = "siegmund")

[Package DMRScan version 1.9.3 Index]