# Cyclic graph (b) example We use a Gibbs sampling scheme to generate a data-set with 200 individuals (according with cyclic graph (b)). Each phenotype is affected by 3 QTLs. We fixed the regression coefficients at 0.5, error variances at 0.025 and the QTL effects at 0.2, 0.3 and 0.4 for the three F2 genotypes. We used a burn-in of 2000 for the Gibbs sampler. ## Details For cyclic graphs, the output of the qdg function computes the log-likelihood up to the normalization constant (un-normalized log-likelihood). We can use the un-normalized log-likelihood to compare cyclic graphs with reversed directions (since they have the same normalization constant). However we cannot compare cyclic and acyclic graphs. ## References Chaibub Neto et al. (2008) Inferring causal phenotype networks from segregating populations. Genetics 179: 1089-1100. ```r data(cyclicb) ``` ## See Also `sim.cross`, `sim.geno`, `sim.map`, `skeleton`, `qdg`, `graph.qdg`, `generate.qtl.pheno` ## Examples ```r ## Not run: bp <- matrix(0, 6, 6) bp[2,1] <- bp[1,5] <- bp[3,1] <- bp[4,2] <- bp[5,4] <- bp[5,6] <- bp[6,3] <- 0.5 stdev <- rep(0.025, 6) ## Use R/qtl routines to simulate. set.seed(3456789) mymap <- sim.map(len = rep(100,20), n.mar = 10, eq.spacing = FALSE, include.x = FALSE) mycross <- sim.cross(map = mymap, n.ind = 200, type = "f2") mycross <- sim.geno(mycross, n.draws = 1) cyclicb.qtl <- generate.qtl.markers(cross = mycross, n.phe = 6) mygeno <- pull.geno(mycross)[, unlist(cyclicb.qtl$markers)] cyclicb.data <- generate.qtl.pheno("cyclicb", cross = mycross, burnin = 2000, bq = c(0.2,0.3,0.4), bp = bp, stdev = stdev, geno = mygeno) save(cyclicb.qtl, cyclicb.data, file = "cyclicb.RData", compress = TRUE) data(cyclicb) out <- qdg(cross=cyclicb.data, phenotype.names=paste("y",1:6,sep=""), marker.names=cyclicb.qtl$markers, QTL=cyclicb.qtl$allqtl, alpha=0.005, n.qdg.random.starts=10, skel.method="pcskel") gr <- graph.qdg(out) gr plot(gr) ## End(Not run) ```

cyclicb dataset