| polySegratioMM-package {polySegratioMM} | R Documentation |
Bayesian Mixture Models for Marker Dosage in Autopolyploids
Description
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.
Details
| Package: | polySegratioMM |
| Type: | Package |
| Version: | 0.6-4 |
| Date: | 2018-03-22 |
| 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.
Author(s)
Peter Baker p.baker1@uq.edu.au
References
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 22 359–372
Ripol, M I et al (1999) Statistical aspects of genetic mapping in autopolyploids. Gene 235 31–41
Examples
## 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)