Description Usage Arguments Details Value Author(s) References Examples
EM algorithm for PPCCA model jackkniifing.
1 | jEM(n, Y, idx, nj, p, Covars, Alphaopt, Wopt, Sigopt, Iq, repV, eps, Np, Nvp, C2p, Ip, picon, V)
|
n |
Number of samples |
Y |
Expression data |
idx |
Identify samples |
nj |
Number of jackknifing samples |
p |
Number of features |
Covars |
Covariates |
Alphaopt |
Regression coefficients |
Wopt |
Loadings |
Sigopt |
Error variance |
Iq |
Identity matrix q by q |
repV |
Vector for maximum number of EM iteration in a vector |
eps |
Smallest value for convergence assessment |
Np |
Some constant; n by p |
Nvp |
Some constant n by v by p |
C2p |
Some constant for the prior of the error variance |
Ip |
Identity matrix of p by p |
picon |
Some constant for normal density computation |
V |
Maximum number of EM iterations. |
EM algorithm for PPCCA model jackkniifing.
EM algorithm for PPCCA model jackkniifing.
Nyamundanda, G., Poudel, P., Patil, Y. and Sadanandam, A.
Nyamundanda, G., Poudel, P., Patil, Y. and Sadanandam, A., 2017. A novel and robust statistical method to diagnose and correct batch effects in genomic data.
1 2 3 | ##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
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