setModel: Set characteristics of the Bayesian mixture model for dosages
In polySegratioMM: Bayesian Mixture Models for Marker Dosage in Autopolyploids

Description Usage Arguments Value Author(s) See Also Examples

Used to automatically set up Bayesian finite mixture models for dosage allocation of dominant markers in autopolyploids given the number of components and ploidy level

1
2
3

setModel(n.components, ploidy.level, random.effect = FALSE, seg.ratios =NULL,
 ploidy.name = NULL, equal.variances=TRUE, 
 type.parents = c("heterogeneous", "homozygous"))

`n.components`	number of components for mixture model (less than or equal to maximum number of possible dosages)
`ploidy.level`	the number of homologous chromosomes, either as numeric or as a character string
`random.effect`	Logical indicating whether model contains random effect (Default: `FALSE`)
`seg.ratios`	segregation proportions for each marker provided as S3 class `segRatio`
`ploidy.name`	Can overide ploidy name here or allow it to be determined from `ploidy.level`
`equal.variances`	Logical indicating whether model contains separate or common variances for each component (Default: `TRUE`)
`type.parents`	"heterogeneous" if parental markers are 0,1 or "homogeneous" if parental markers are both 1

Returns object of class modelSegratioMM with components

`bugs.code`	text to be used by `JAGS` in the .bug file but without statements pertaining to priors
`n.components`	number of components for mixture model
`monitor.var`	names of variables to be monitored in `JAGS` run
`ploidy.level`	ploidy level
`random.effect`	Logical indicating whether model contains random effect (Default: `FALSE`)
`equal.variances`	Logical indicating equal or separate variances for each component
`E.segRatio`	Expected segregation ratios
`type.parents`	"heterogeneous" if parental markers are 0,1 or "homogeneous" if parental markers are both 1
`call`	function call

Peter Baker p.baker1@uq.edu.au

setPriors setInits expected.segRatio segRatio setControl dumpData dumpInits or for an easier way to run a segregation ratio mixture model see runSegratioMM

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)

## set up model with 3 components
x <- setModel(3,8)
print(x)