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ll.gdi<-function(counts,tm,tf){
# child genotype
C1=c(2,2,1,2,1,1,1,2,1,1,0,1,0,1,0,0)
# imprint variable
Im=c(0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,0)
# Mating type variables
MT1=c(1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
MT2=c(0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0)
MT3=c(0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0)
MT4=c(0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0)
MT5=c(0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0)
MT6=c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1)
# offset for transmission probability -- TRD
offset.trd=c(log(1),
log(tf),log(1-tf),log(tm),log(1-tm),
log(1),log(1),
log(tm*tf),
log(tm*(1-tf)),log((1-tm)*tf),
log((1-tm)*(1-tf)),
log(tm),log(1-tm),log(tf),log(1-tf),
log(1))
# offset for M=F=C=1
offset.111= c(rep(0,8),rep(0,2),rep(0,6))
# Fit log-linear model without TRD adjustment
mod.0=glm(counts~MT1+MT2+MT3+MT4+MT5,
family=poisson,offset=offset.111)
mod.wb=glm(counts~MT1+MT2+MT3+MT4+MT5+C1+Im,
family=poisson,offset=offset.111)
# Fit log-linear model with TRD adjustment
mod.trd.0=glm(counts~MT1+MT2+MT3+MT4+MT5,
family=poisson,
offset=offset.trd)
mod.trd.wb=glm(counts~MT1+MT2+MT3+MT4+MT5+C1+Im,
family=poisson,
offset=offset.trd)
# Acquire deviance for the 2 models
dev.wb=anova(mod.0,mod.wb)$Deviance[2]
dev.trd.wb=anova(mod.trd.0,mod.trd.wb)$Deviance[2]
stat=list(
summary(mod.wb)$coeff[7:8,],
summary(mod.trd.wb)$coeff[7:8,],
dev.wb,
dev.trd.wb
)
names(stat)=c('Mod1','Mod2','DevM1','DevM2')
stat
}
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