R version 3.1.1 (2014-07-10) -- "Sock it to Me"
Copyright (C) 2014 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin13.1.0 (64-bit)
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> library(qtl)
> data(map10)
> map10 <- map10[1:2]
> set.seed(8789993)
> simcross <- sim.cross(map10, n.ind=125, type="bc",
+ model=rbind(c(1, 50, 1.5), c(2, 50, 0)))
> simcross$pheno[,1] <- simcross$pheno[,1] + rnorm(nind(simcross), 0, 2*simcross$qtlgeno[,2])
> simcross <- calc.genoprob(simcross)
> out <- scanonevar(simcross,
+ tol=0.01)
> summary(out, format="allpeaks")
chr pos neglogP_mean pos neglogP_disp
1 1 58.6 1.65 107.5 0.841
2 2 62.2 1.17 51.8 5.817
>
> ####
>
> data(fake.bc)
> fake.bc <- fake.bc[1:2,1:150] # only chr 1 and 2, and first 100 individuals
> fake.bc <- calc.genoprob(fake.bc, step=5)
> out <- scanonevar(fake.bc,
+ tol=0.01)
> summary(out, format="allpeaks")
chr pos neglogP_mean pos neglogP_disp
1 1 9.8 0.995 20 0.514
2 2 30.0 2.200 50 1.702
> covar <- fake.bc$pheno[,c("sex", "age")]
> out <- scanonevar(fake.bc, mean_covar=covar, var_covar=covar,
+ tol=0.01)
> summary(out, format="allpeaks")
chr pos neglogP_mean pos neglogP_disp
1 1 5 0.725 37.1 2.31
2 2 30 5.202 30.0 2.28
>
> #########Simulate a vQTL on Chromosome 1########
>
> chromo=1
> qtl.position=14 # 50 cM
> N=nind(fake.bc)
> a1<-fake.bc$geno[[chromo]]$prob[,,1]
> y <- fake.bc$pheno$pheno1
> y <- y + rnorm(N,0,exp(a1[,qtl.position]))
> out <- scanonevar(fake.bc, y, mean_covar=covar, var_covar=covar)
> summary(out, format="allpeaks")
chr pos neglogP_mean pos neglogP_disp
1 1 45 0.784 70.0 5.781
2 2 0 0.368 21.8 0.672
>
> out <- scanonevar(fake.bc, y, mean_covar=covar,
+ tol=0.01)
> summary(out, format="allpeaks")
chr pos neglogP_mean pos neglogP_disp
1 1 45 0.746 70.0 6.012
2 2 0 0.380 72.1 0.617
>
> out <- scanonevar(fake.bc, y, var_covar=covar,
+ tol=0.01)
> summary(out, format="allpeaks")
chr pos neglogP_mean pos neglogP_disp
1 1 45.0 0.896 70.0 3.41
2 2 72.1 0.645 21.8 0.53
>
> out <- scanonevar(fake.bc, y,
+ tol=0.01)
> summary(out, format="allpeaks")
chr pos neglogP_mean pos neglogP_disp
1 1 45.0 0.838 70.0 3.486
2 2 72.1 0.654 21.8 0.486
>
> proc.time()
user system elapsed
8.548 0.071 8.682
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