qbSim {qtlbim} | R Documentation |
Retrieve or recreate MCMC samples used in scan.pdf document.
data(qbSimMain) data(qbSimEpi)
Both calls to data
create qb
objects names qbSim
.
See vignette scan.pdf
or see scan.Rnw
in doc folder of package.
qb.genoprob
, qb.mcmc
, qb.sim.cross
data(qbSimMain) summary(qbSim) data(qbSimEpi) summary(qbSim) ## Not run: ## Setup for Simulated Data used in scan.pdf. n.ind <- 100 ## number of individuals n.mark <- 200 ## number of markers by.mark <- 1 ## cM spacing between markers qtl.positions <- n.mark / 2 ## position of QTL markers <- seq(0, n.mark, by = by.mark) names(markers) <- paste("M", markers, sep = "") sim.map <- list(ch1 = markers) sim.model <- matrix(c(1, qtl.positions, qtl.effect / 2), 1, 3) colnames(sim.model) <- c("chromosome","qtl-position","effect-size") n.iter <- 1000 ## number of iterations for MCMC qb.random.seed <- 1626 ## random seed for MCMC ## Genetic architecture for scan simulations: 3 QTL. qtl.positions <- rbind(qtl1 = c(chromosome = 1, locus = 5), qtl2 = c(chromosome = 1, locus = 50), qtl3 = c(chromosome = 2, locus = 33) ) qtl.positions qtl.main.model <- rbind(qtl1.main.effect = c(qtl = 1, main.effect.size = 0), qtl2.main.effect = c(qtl = 2, main.effect.size = 0), qtl3.main.effect = c(qtl = 3, main.effect.size = 0)) qtl.main.model qtl.epi.model <- rbind(qtl1.and.qtl3.epi.effect = c(qtl1 = 1, qtl2 = 3, epi.effect.size = 10)) qtl.epi.model ## SimEpi set.seed(1234) sim <- qb.sim.cross(len = rep(100, 2), n.mar = 10, eq.spacing = TRUE, n.ind = 100, type = "bc", missing.geno = 0.03, qtl.pos = qtl.positions, qtl.main = qtl.main.model, qtl.epis = qtl.epi.model) sim <- qb.genoprob(sim) qbSim <- qb.mcmc(sim, n.iter = n.iter, verbose = FALSE, n.thin = 40, seed = qb.random.seed) ## The next line saves qbSim as an external binary file. save("qbSim", file = "qbSimEpi.RData") ## SimMain qtl.main.model[2, "main.effect.size"] = 10 set.seed(1234) sim <- qb.sim.cross(len = rep(100, 2), n.mar = 10, eq.spacing = TRUE, n.ind = 100, type = "bc", missing.geno = 0.03, qtl.pos = qtl.positions, qtl.main = qtl.main.model, qtl.epis = NULL) ## After the data is simulated call qb.genoprob to fill in ## missing data. sim <- qb.genoprob(sim, step = 2) ## Call qb.mcmc and then analysis code. qbSim <- qb.mcmc(sim, n.iter = n.iter, verbose = FALSE, n.thin = 40, seed = qb.random.seed) ## The next line saves qbSim as an external binary file. save("qbSim", file = "qbSimMain.RData") ## End(Not run)