Generates an MCMC model fit of the BaySIC BMR model
1  baysic.fit(dat.out, snv.cat, covar = NULL, excl.list = NULL, burn.in = 10000,n.samp = 25000, fn.jags = "baysic.jags", prior = NULL)

dat.out 
Output from 
snv.cat 
a 
covar 
optional G \times Q matrix of genelevel covariate data, where G is the total number of genes and Q the number of covariates. 
excl.list 
optional vector of genes to be excluded from model fitting process. The format of 
burn.in 
an integer; represents the burnin size to apply in the MCMC model fitting using JAGS. Defaults to 10,000 
n.samp 
an integer; represents the size of the MCMC posterior sample draw from the fitted model. Defaults to 25,000 
fn.jags 
a character string; corresponds to the file name and location of the JAGS model file to be written. Defaults to "baysic.jags" in the current working directory. 
prior 
optional vector of prior distribution specifications (as character strings). If 
Returns a list
object with the following components:
fit.post 
an 
covar 

snv.cat 
the 
excl.list 

Nicholas B. Larson
1 2 3 4 5 6 7 8 9 10 11  ## Not run:
data(example.dat)
data(ccds.19)
baysic.dat.ex<baysic.data(example.dat,ccds.19)
snv.cat.ex<list()
snv.cat.ex[[1]]<grep("[^T]C[^G]",colnames(ccds.19)[c(1:2)])
snv.cat.ex[[2]]<unique(c(grep("TC.",colnames(ccds.19)[c(1:2)]),grep(".CG",colnames(ccds.19)[c(1:2)])))
snv.cat.ex[[3]]<grep(".T.",colnames(ccds.19)[c(1:2)])
baysic.fit.ex<baysic.fit(baysic.dat.ex,snv.cat.ex)
## End(Not run)

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
Please suggest features or report bugs with the GitHub issue tracker.
All documentation is copyright its authors; we didn't write any of that.