### cubfits control variables.
### For method controls.
.CF.CT <- list(
model = c("roc"), # main models
type.p = c("lognormal_RW",
"lognormal_fix",
"lognormal_bias",
"logmixture"), # proposal for hyperparameters
type.Phi = c("RW_Norm"), # proposal for Phi
model.Phi = c("lognormal", "logmixture"), # prior of Phi
init.Phi = c("PM"), # initial methods for Phi
init.fit = c("RW_Norm", "current", "random"), # how is b proposed
parallel = c("lapply", "mclapply",
"task.pull", "pbdLapply"), # parallel functions
adaptive = c("simple", "none"), # method for adaptive mcmc
prior.dist.M = c("uniform", "normal"),
prior.dist.Sphi = c("uniform", "normal")
)
### For configuration of initial and draw scaling.
.CF.CONF <- list(
scale.phi.Obs = FALSE, # if phi.Obs were scaled to mean 1
init.b.Scale = 1, # initial b scale
init.phi.Scale = 1, # initial phi scale
p.nclass = 2, # # of classes if mixture phi
b.DrawScale = 1, # drawing scale for b if random walk
p.DrawScale = 0.1, # drawing scale for p if random walk
phi.DrawScale = 1, # random walk scale for phi
phi.pred.DrawScale = 1, # random walk scale for phi.pred
sigma.Phi.DrawScale = 1, # random walk scale for sigma.Phi
bias.Phi.DrawScale = 0.1, # random walk scale for bias.Phi
estimate.bias.Phi = FALSE, # if estimate bias of phi during MCMC
estimate.Phi.noise = TRUE, # estimate the noise in the phi observed data (sigma epsilon)
estimate.S.Phi = TRUE, # if FALSE, sPhi is fixed accross the run
compute.logL = TRUE # if compute logL in each iteration
)
### For optimization.
.CF.OP <- list(
optim.method = c("Brent"), # for optim()
stable.min.exp = .Machine$double.max.exp * 0.1, # minimum exponent
stable.max.exp = .Machine$double.max.exp * 0.5, # maximum exponent
lower.optim = 1e-4, # lower of d logL(x)
upper.optim = 1e2, # upper of d logL(x)
lower.integrate = 0.0, # lower of \int L(x)
upper.integrate = Inf # upper of \int L(x)
)
### For dumpping data.
.CF.DP <- list(
dump = FALSE, # if dumping within MCMC
iter = 1000, # iterations per dumping
prefix.dump = "dump_", # path and file names of dumping
verbose = FALSE, # if verbose
iterThin = 1, # iterations to thin chain
report = 10, # iterations to report
report.proc = 100 # iterations to report proc.time()
)
### For addaptive control.
.CF.AC <- list(
renew.iter = 100, # per renewing iterations
target.accept.lower = 0.2, # target acceptant rate lower bound
target.accept.upper = 0.3, # target acceptant rate upper bound
scale.increase = 1.2, # increase scale size
scale.decrease = 0.8, # decrease scale size
sigma.lower = 1e-2, # lower bound of relative scale size
sigma.upper = 1e2 # upper bound of relative scale size
)
### For parameters as reestimated for Yeast according to Yassour's data.
.CF.PARAM <- list(
# phi.meanlog = -0.441473, # yassour mean for log(phi)
# phi.sdlog = 1.393285, # yassour sd for log(phi)
phi.meanlog = -1.125, # mean of log(phi), -s^2/2
phi.sdlog = 1.5, # sd of log(phi)
prior.M.a = 0, # first parameter of density function of prior (e.g. dnorm(x, mean=a, sd=b))
prior.M.b = 0.35, # second parameter of density function of prior (e.g dnorm(x, mean=a, sd=b))
prior.Sphi.a = 0.52, # meanlog for lognormal Sphi prior
prior.Sphi.b = 0.33 # sdlog for lognormal Sphi prior
)
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