Description Usage Arguments Details Value Note Author(s) References See Also Examples

This function provides codon usage bias fits with observed ORFs and expressions which possibly contains measurement errors.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
cubfits(reu13.df.obs, phi.Obs, y, n,
nIter = 1000,
b.Init = NULL, init.b.Scale = .CF.CONF$init.b.Scale,
b.DrawScale = .CF.CONF$b.DrawScale,
b.RInit = NULL,
p.Init = NULL, p.nclass = .CF.CONF$p.nclass,
p.DrawScale = .CF.CONF$p.DrawScale,
phi.Init = NULL, init.phi.Scale = .CF.CONF$init.phi.Scale,
phi.DrawScale = .CF.CONF$phi.DrawScale,
model = .CF.CT$model[1], model.Phi = .CF.CT$model.Phi[1],
adaptive = .CF.CT$adaptive[1],
verbose = .CF.DP$verbose,
iterThin = .CF.DP$iterThin, report = .CF.DP$report)
``` |

`reu13.df.obs` |
a |

`phi.Obs` |
a |

`y` |
a |

`n` |
a |

`nIter` |
number of iterations after burn-in iterations. |

`b.Init` |
initial values for parameters |

`init.b.Scale` |
for initial |

`b.DrawScale` |
scaling factor for adaptive MCMC with random walks
when drawing new |

`b.RInit` |
initial values (in a list) for |

`p.Init` |
initial values for hyper-parameters. |

`p.nclass` |
number of components for |

`p.DrawScale` |
scaling factor for adaptive MCMC with random walks
when drawing new |

`phi.Init` |
initial values for Phi. |

`init.phi.Scale` |
for initial phi if |

`phi.DrawScale` |
scaling factor for adaptive MCMC with random walks when drawing new Phi. |

`model` |
model to be fitted, currently "roc" only. |

`model.Phi` |
prior model for Phi, currently "lognormal". |

`adaptive` |
adaptive method of MCMC for proposing new |

`verbose` |
print iteration messages. |

`iterThin` |
thinning iterations. |

`report` |
number of iterations to report more information. |

This function correctly and carefully implements a combining version of Shah and Gilchrist (2011) and Wallace et al. (2013).

Total number of MCMC iterations is `nIter + 1`

, but the
outputs may be thinned to `nIter / iterThin + 1`

iterations.

Temporary result dumping may be controlled by `.CF.DP`

.

A list contains three big lists of MCMC traces including:
`b.Mat`

for mutation and selection coefficients of `b`

,
`p.Mat`

for hyper-parameters, and
`phi.Mat`

for expected expression values Phi.
All lists are of length `nIter / iterThin + 1`

and
each element contains the output of each iteration.

All lists also can be binded as trace matrices, such as via
`do.call("rbind", b.Mat)`

yielding a matrix of dimension number of
iterations by number of parameters. Then, those traces can be analyzed
further via other MCMC packages such as coda.

Note that `phi.Init`

need to be normalized to mean 1.

`p.DrawScale`

may cause scaling prior if adaptive MCMC is used, and
it can result in non-exits of equilibrium distribution.

Wei-Chen Chen [email protected].

https://github.com/snoweye/cubfits/

Shah P. and Gilchrist M.A. “Explaining complex codon usage patterns with selection for translational efficiency, mutation bias, and genetic drift” Proc Natl Acad Sci USA (2011) 108:10231–10236.

Wallace E.W.J., Airoldi E.M., and Drummond D.A. “Estimating Selection on Synonymous Codon Usage from Noisy Experimental Data” Mol Biol Evol (2013) 30(6):1438–1453.

DataIO, DataConverting,
`cubappr()`

and `cubpred()`

.

1 2 3 4 5 6 | ```
## Not run:
suppressMessages(library(cubfits, quietly = TRUE))
demo(roc.train, 'cubfits', ask = F, echo = F)
## End(Not run)
``` |

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