integratedEM {RIVER} | R Documentation |
integratedEM
iteratively executes e-step and m-step until it
converges. This is a main function of RIVER.
integratedEM(Feat, Out, lambda, logistic.init, pseudoc, theta.init, costs, verbose = FALSE)
Feat |
Genomic features (G). |
Out |
Binary values of outlier status (E). |
lambda |
Selected lambda. |
logistic.init |
Smart initialization of beta (parameters between FR and G) from estimate of beta with E via multivariate logistic regression. |
pseudoc |
Pseudo count. |
theta.init |
Initial theta (parameters between FR (functionality of regulatory variant) and E). |
costs |
Candidate penalty parameter values for L2-regularization within logistic regression. |
verbose |
Logical option for showing extra information on progress. |
Best estimate of beta and theta, final multivariate logistic regression model, and posterior probabilities of FR.
Yungil Kim, ipw012@gmail.com
getFuncRvFeat
, getFuncRvPosteriors
,
mleTheta
, mleBeta
,
cv.glmnet
,
and https://github.com/ipw012/RIVER
dataInput <- getData(filename=system.file("extdata", "simulation_RIVER.gz", package = "RIVER"), ZscoreThrd=1.5) Feat <- scale(t(Biobase::exprs(dataInput))) # genomic features (G) Out <- as.vector(as.numeric(unlist(dataInput$Outlier))-1) # outlier status (E) theta.init=matrix(c(.99, .01, .3, .7), nrow=2) costs <- c(100, 10, 1, .1, .01, 1e-3, 1e-4) logisticAllCV <- glmnet::cv.glmnet(Feat, Out, lambda=costs, family="binomial", alpha = 0, nfolds=10) emModelAll <- integratedEM(Feat, Out, lambda=logisticAllCV$lambda.min, logistic.init=logisticAllCV$glmnet.fit, pseudoc=50, theta=theta.init, costs, verbose=FALSE)