appRIVER {RIVER} | R Documentation |
appRIVER
trains RIVER with all instances and computes posterior
probabilities of FR for downstream analyses.
appRIVER(dataInput, pseudoc = 50, theta_init = matrix(c(0.99, 0.01, 0.3, 0.7), nrow = 2), costs = c(100, 10, 1, 0.1, 0.01, 0.001, 1e-04), verbose = FALSE)
dataInput |
An object of ExpressionSet class which contains input data required for all functions in RIVER including genomic features, outlier status, and N2 pairs. |
pseudoc |
Pseudo count. |
theta_init |
Initial values of theta. |
costs |
Candidate penalty parameter values for L2-regularized logistic regression. |
verbose |
Logical option for showing extra information on progress. |
A list which contains subject IDs, gene names, posterior probabilities from GAM and RIVER, and estimated parameters from RIVER with used hyperparameters.
To input a vector of candidate penalty values makes
glmnet
faster than to input a single penalty value
Yungil Kim, ipw012@gmail.com
cv.glmnet
, predict
,
integratedEM
, testPosteriors
,
getData
, exprs
dataInput <- getData(filename=system.file("extdata", "simulation_RIVER.gz", package = "RIVER"), ZscoreThrd=1.5) postprobs <- appRIVER(dataInput, verbose=TRUE)