initializeMCMCObject {AnaCoDa} | R Documentation |
initializeMCMCObject
initializes a MCMC object to
perform a model fitting for a parameter and model object.
initializeMCMCObject(samples, thinning = 1, adaptive.width = 100, est.expression = TRUE, est.csp = TRUE, est.hyper = TRUE, est.mix = TRUE)
samples |
Number of samples to be produced when running the MCMC algorithm. No default value. |
thinning |
The thinning interval between consecutive observations. If set to 1, every step will be saved as a sample. Default value is 1. |
adaptive.width |
Number that determines how often the acceptance/rejection window should be altered. Default value is 100 samples. |
est.expression |
Boolean that tells whether or not synthesis rate values should be estimated in the MCMC algorithm run. Default value is TRUE. |
est.csp |
Boolean that tells whether or not codon specific values should be estimated in the MCMC algorithm run. Default value is TRUE. |
est.hyper |
Boolean that tells whether or not hyper parameters should be estimated in the MCMC algorithm run. Default value is TRUE. |
est.mix |
Boolean that tells whether or not the genes' mixture element should be estimated in the MCMC algorithm run. Default value is TRUE. |
initializeMCMCObject
sets up the MCMC object
(monte carlo markov chain) and returns the object so a model fitting can be done.
It is important to note that est.expression and est.hyper will affect one another
negatively if their values differ.
mcmc Returns an intialized MCMC object.
## initializing an object of type mcmc samples <- 2500 thinning <- 50 adaptiveWidth <- 25 ## estimate all parameter types mcmc <- initializeMCMCObject(samples = samples, thinning = thinning, adaptive.width=adaptiveWidth, est.expression=TRUE, est.csp=TRUE, est.hyper=TRUE, est.mix = TRUE) ## do not estimate expression values, initial conditions will remain constant mcmc <- initializeMCMCObject(samples = samples, thinning = thinning, adaptive.width=adaptiveWidth, est.expression=FALSE, est.csp=TRUE, est.hyper=TRUE, est.mix = TRUE) ## do not estimate hyper parameters, initial conditions will remain constant mcmc <- initializeMCMCObject(samples = samples, thinning = thinning, adaptive.width=adaptiveWidth, est.expression=TRUE, est.csp=TRUE, est.hyper=FALSE, est.mix = TRUE)