setModel: Set characteristics of the Bayesian mixture model for dosages

In polySegratioMM: Bayesian Mixture Models for Marker Dosage in Autopolyploids

Set characteristics of the Bayesian mixture model for dosages

Description

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

Usage

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

Arguments

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

Value

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

Author(s)

Peter Baker [e-mail]

See Also

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

Examples

## 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)

polySegratioMM documentation built on Feb. 25, 2026, 5:08 p.m.