setModel function

Set characteristics of the Bayesian mixture model for dosages

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

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

Returns

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 p.baker1@uq.edu.au

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)