dpeakFit {dpeak} | R Documentation |
Fit a deconvolution model.
dpeakFit( object, ... ) ## S4 method for signature 'DpeakData' dpeakFit( object, objectMotif=NULL, estDeltaSigma="common", init="localmax", nTop=100, lbDelta = 25, lbSigma = 25, psize=21, maxComp=5, pConst=0.2, nCore=1, verbose=FALSE, iterInit=50, iterMain=25, epsilon=1e-6 )
object |
Object of class |
objectMotif |
Object of class |
estDeltaSigma |
Approach to estimate delta and sigma parameters for SET data.
Possible values are either |
init |
Approach to initialize locations of binding events.
Possible values are |
nTop |
Number of candidate regions used to estimate common delta and sigma estimates.
Relevant only when |
lbDelta |
Lower bound for delta parameter. |
lbSigma |
Lower bound for sigma parameter. |
psize |
Approximate size of the binding protein of interest. |
maxComp |
Maximum possible number of binding events in each peak region. |
pConst |
Value to determine the plateau in the BIC curve. Should be a value larger than zero and smaller than one. |
nCore |
Number of CPUs to be used when parallel computing is utilized. |
verbose |
Use verbose mode?
Possible values are either |
iterInit |
Iteration number for initial estimation of binding sites. |
iterMain |
Iteration number for main estimation of binding sites. |
epsilon |
Criterion to stop iteration for binding site estimation. |
... |
Other parameters to be passed through to generic |
Parallel computing can be utilized for faster computation
if parallel
package is installed.
Users can change the number of CPUs to be used by changing the argument nCore
.
Construct DpeakFit
class object.
Dongjun Chung
data(exampleData) exampleFit <- dpeakFit(exampleData, maxComp = 5)