Description Details Author(s) References Examples

These functions provide tools for estimating marker dosage for
dominant markers in regular autopolyploids via Bayesian mixture
model. Wrappers are provided for generating MCMC samples
using the `JAGS`

software. Convergence diagnostics and posterior
distribution densities are provided by the `coda`

package.

Package: | polySegratioMM |

Type: | Package |

Version: | 0.6-2 |

Date: | 2012-04-10 |

License: | GPL-3 |

The simplest way to fit a model is to use `runSegratioMM`

.
Given segregation ratios and a ploidy level, a mixture model is
constructed with default priors and initial values and `JAGS`

run
to produce an MCMC sample for statistical inference.

A standard model may be set up with `setModel`

where two
parameters are set, namely `ploidy.level`

or the number of
homologous chromosomes set either as a numeric or as a character
string and also `n.components`

or the number of components for
mixture model (less than or equal to maximum number of possible
dosages).

Vague or strong priors may be constructed automatically using
`setPriors`

. Plots of standard conjugate distributions may
be obtained using `DistributionPlotBinomial`

`DistributionPlotGamma`

and
`DistributionPlotNorm`

.

If necessary, other operations like setting up initial values or the
control files for `JAGS`

may be set using `setInits`

`setControl`

`dumpData`

`dumpInits`

`writeControlFile`

`writeJagsFile`

. Once the
`BUGS`

files and `JAGS`

control files are set up then
`JAGS`

may be run using `runJags`

and results read
using `readJags`

.

Convergence diagnostics may be carried out using `coda`

or the
convenience wrapper `diagnosticsJagsMix`

.

Dose allocation can be carried out using `dosagesJagsMix`

.

Plots may be produced and objects printed and summarised using standard
`print`

and `plot`

methods. Plots of theoretical
binomial distributions with different ploidy levels and sample sizes may
be obtained with `plotFitted`

. In addition,
`plotFitted`

produces a lattice plot of the observed
segregation ratios and fitted mixture model on the logit scale.

Peter Baker [email protected]

Baker P, Jackson P, and Aitken K. (2010) Bayesian estimation of marker dosage in sugarcane and other autopolyploids.

*TAG Theoretical and Applied Genetics***120**(8): 1653-1672.J B S Haldane (1930) Theoretical genetics of autopolyploids.

*Journal of genetics***22**359–372Ripol, M I et al (1999) Statistical aspects of genetic mapping in autopolyploids.

*Gene***235**31–41JAGS http://www-fis.iarc.fr/~martyn/software/jags/ and http://streaming.stat.iastate.edu/wiki/index.php/JAGS\_Guide

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
## simulate small autooctaploid data set of 100 markers for 50 individuals
## with %70 Single, %20 Double and %10 Triple Dose markers
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=400,n.individuals=275)
##print(a1)
sr <- segregationRatios(a1$markers)
x <- setModel(3,8) # autooctapolid mode with 3 components
## Not run:
## fit simple model in one hit with default priors, inits etc
## warning: this is too small an MCMC sample so should give inaccurate
## answers but it could still take quite a while
x.run <- runSegratioMM(sr, x, burn.in=2000, sample=5000)
print(x.run)
## plot observed segregation ratios, fitted model and expected distribution
plot(x.run, theoretical=TRUE)
## End(Not run)
``` |

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