twohk {bqtl}  R Documentation 
Fits all one and two gene models (without interactions aka 'epistasis') in an intercross, backcross, or recombinant inbred line. Uses a linear approximation to the likelihood, i.e. the expected allele states are used.
twohk(varcov, ana.obj, ...)
varcov 
An object produced by 
ana.obj 
An 
... 
Additional arguments override the default choices of
candidate loci ( 
The marginal posterior (integrating over regression parameters and dispersion) is calculated for each one and two gene model under the assumed correctness of the regression model using expected genotypes given marker values. This amounts to linearizing the likelihood with respect to the (possibly unknown) locus states. For models where the loci are fully informative markers this is the true posterior.
A list with components:
loc.1 
The marginal posterior for each one gene model relative to
a no gene model. For

loc.2 
The marginal posterior for each locus — obtained by summing
over all two gene models that include that locus— relative to
a no gene model. For

coefs.1 
The regression coefficients for the genetic effect for
each locus. For 
coefs.2 
The marginal posterior mean of regression coefficients
for the genetic effect for each locus  obtained by averaging over
all two gene models that include that locus according to the
posterior masses. For 
Charles C. Berry cberry@ucsd.edu
Haley C.S. and Knott S.A. (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69,315324.
data(little.ana.bc)
little.vc<make.varcov(little.ana.bc$data[,little.ana.bc$reg.names],
little.ana.bc$data$bc.phenotype)
little.2< twohk(little.vc,little.ana.bc,rparm=1)
print( c(odds.1=sum(little.2$loc.1),odds.2=sum(little.2$loc.2)) )
par(mfrow=c(3,2))
little.pe < 2 * little.2$loc.2 / sum(little.2$loc.2) #locuswise posterior expectation
plot(little.ana.bc,little.pe,type="h",ylab="E(genes")
rm(little.2,little.vc,little.pe,little.ana.bc)