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nu
and rhoSquare
, respectively), the variance of the noise (sigmaSquare
). It is possible
to choose the estimator of rhoSquare
(i.e. either $\hat{\rho}_1^2$ or $\hat{\rho}^2$) and by default $\hat{\rho}_1^2$ is used.
estGlobParam(y, nu=NULL, rhoSquare=NULL, sigmaSquare=NULL, typeEstRho=1)
nu=NULL
, then the algorithm estimates it on the sample.rhoSquare=NULL
, then the algorithm estimates it on the sample.sigmaSquare=NULL
, then the algorithm estimates it on the sample.rhoSquare
. If typeEstRho=1
, then the algorithm estimates rhoSquare
with $\hat{\rho}_1^2$, while if typeEstRho=0
, it estimates rhoSquare
with $\hat{\rho}^2$.nu
rhoSquare
sigmaSquare
##import the 10K data of cell line REC
data(rec10k)
##estimation of all the global parameters (the variance of the segment is estimated with \eqn{\hat{\rho}^2_1})
estGlobParam(rec10k$log2ratio)
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