findDMCFB-method | R Documentation |
DMC identification via Bayesian functional regression models
findDMCFB( object, bwa, bwb, nBurn, nMC, nThin, alpha, sdv, nCores, pSize, sfiles ) ## S4 method for signature 'BSDMC' findDMCFB( object, bwa, bwb, nBurn, nMC, nThin, alpha, sdv, nCores, pSize, sfiles )
object |
A |
bwa |
An integer value specifying the band-width size of B-spline basis matrix for a natural cubic spline for the group-specific effects of the Bayesian functional regression model |
bwb |
An integer value specifying the band-width size of B-spline basis matrix for a natural cubic spline for the individual-specific effects of the Bayesian functional regression model |
nBurn |
An integer value specifying the number of burn-in samples |
nMC |
An integer value specifying the number of MCMC samples after burn-in |
nThin |
An integer value specifying the thining number in MCMC |
alpha |
A numeric value specifying the level of α in credible interval (1-α)\% |
sdv |
An double value specifying the standard deviation of priors |
nCores |
An integer value specifying the number of machine cores for parallel computing |
pSize |
An integer value specifying the number of cytosines in a regrion to be used in a Bayesian functiona regression model for DMC detection |
sfiles |
A logical value indicating whether files to be saved or not. |
BSDMC-class
object
Farhad Shokoohi <shokoohi@icloud.com>
set.seed(1980) nr <- 1000 nc <- 4 metht <- matrix(as.integer(runif(nr * nc, 0, 100)), nr) methc <- matrix(rbinom(n = nr * nc, c(metht), prob = runif(nr * nc)), nr, nc) methl <- methc / metht r1 <- GRanges(rep("chr1", nr), IRanges(1:nr, width = 1), strand = "*") names(r1) <- 1:nr cd1 <- DataFrame( Group = rep(c("G1", "G2"), each = nc / 2), row.names = LETTERS[1:nc] ) OBJ1 <- cBSDMC( rowRanges = r1, methReads = methc, totalReads = metht, methLevels = methl, colData = cd1 ) OBJ2 <- findDMCFB(OBJ1, bwa = 10, bwb = 10, nBurn = 50, nMC = 50, nThin = 1, alpha = 0.05, nCores = 2, pSize = 500, sfiles = FALSE ) OBJ2
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