Controls {cubfits} | R Documentation

## Default Controlling Options

### Description

Default controls of cubfits include for models, optimizations, MCMC, plotting, global variables, etc.

### Usage

      .cubfitsEnv
      .CF.CT
      .CF.CONF
      .CF.GV
      .CF.DP
      .CF.OP
      .CF.AC
      .CF.PT
      .CF.PARAM
      .CO.CT

### Format

All are in lists and contain several controlling options.

### Details

See `init.function()` for use cases of these objects.

- `.cubfitEnv` is a default environment to dynamically save functions and objects.

- `.CF.CT` is main controls of models. It currently includes

  `model` | main models
  `type.p` | proposal for hyper-parameters
  `type.Phi` | proposal for Phi
  `model.Phi` | prior of Phi
  `init.Phi` | initial methods for Phi
  `init.fit` | how is coefficient proposed
  `parallel` | parallel functions
  `adaptive` | method for adaptive MCMC

- `.CF.CONF` controls the initial and draw scaling. It currently includes

  `scale.phi.Obs` | if phi were scaled to mean 1
  `init.b.Scale` | initial b scale
  `init.phi.Scale` | initial phi scale
  `p.nclass` | number of classes if mixture phi
  `b.DrawScale` | drawing scale for b if random walk
  `p.DrawScale` | drawing scale for p if random walk
  `phi.DrawScale` | random walk scale for phi
  `phi.pred.DrawScale` | random walk scale for phi.pred
  `sigma.Phi.DrawScale` | random walk scale for sigma.Phi
  `bias.Phi.DrawScale` | random walk scale for bias.Phi
  `estimate.bias.Phi` | if estimate bias of phi during MCMC
  `compute.logL` | if compute logL in each iteration

- `.CF.GV` contains global variables for amino acids and codons. It currently includes

  `amino.acid` | amino acids
  `amino.acid.3` | amino acids
  `synonymous.codon` | synonymous codons of amino acids
  `amino.acid.split` | amino acid 'S' is split
  `amino.acid.split.3` | amino acid 'S' is split
  `synonymous.codon.split` | synonymous codons of split amino acid

- `.CF.OP` controls optimizations. It currently includes

  `optim.method` | method for `optim`()
  `stable.min.exp` | minimum exponent
  `stable.max.exp` | maximum exponent
  `E.Phi` | expected Phi
  `lower.optim` | lower of derivative of logL(x)
  `upper.optim` | upper of derivative of logL(x)
  `lower.integrate` | lower of integration of L(x)
  `upper.integrate` | upper of integration of L(x)

- `.CF.DP` is for dumping MCMC iterations. It currently includes

  `dump` | if dumping within MCMC
  `iter` | iterations per dumping
  `prefix.dump` | path and file names of dumping
  `verbose` | if verbose
  `iterThin` | iterations to thin chain
  `report` | iterations to report
  `report.proc` | iterations to report `proc.time`()

- `.CF.AC` controls adaptive MCMC. It currently includes

  `renew.iter` | per renewing iterations
  `target.accept.lower` | target acceptant rate lower bound
  `target.accept.upper` | target acceptant rate upper bound
  `scale.increase` | increase scale size
  `scale.decrease` | decrease scale size
  `sigma.lower` | lower bound of relative scale size
  `sigma.upper` | upper bound of relative scale size

- `.CF.PT` controls the plotting format. It currently includes

  `color` | color for codons.

- `.CF.PARAM` controls the parameters and hyperparameters of priors. It currently includes

  `phi.meanlog` | mean of phi in loca scale
  `phi.sdlog` | standard deviation of phi in loca scale

- `.CO.CT` controls the constrained optimization function. It currently includes

  `debug` | message printing level of debugging.

### Author(s)

Wei-Chen Chen wccsnow@gmail.com.

### References

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

### See Also

`init.function()`, `cubfits()`, `cubpred()`, `cubappr()`, and `mixnormerr.optim()`.

### Examples

    ## Not run:
    suppressMessages(library(cubfits, quietly = TRUE))

    .CF.CT
    .CF.CONF
    .CF.DP
    .CF.GV
    .CF.OP
    .CF.AC
    .CF.PT
    .CF.PARAM
    .CO.CT

    ls(.cubfitsEnv)
    init.function()
    ls(.cubfitsEnv)

    ## End(Not run)

[Package cubfits version 0.1-3 Index]