#question 1

#to get started, do one simulation
#note we generate the *differences*, for 100 people

mydiff <- rnorm(100, 2, 7)
t.test(mydiff)$p.value

#now repeat this 1000 times

my.sims <- replicate(1000, {
    mydiff <- rnorm(100, 2, 7)
    t.test(mydiff)$p.value
})

hist(my.sims)
abline(v=0.05, lwd=2, col="red")

table(my.sims < 0.05)
mean(my.sims < 0.05)

#so we get about 80% power (pretty good)

#question 2

bpdata <- read.table("bpdata.csv", sep=",")

one.p <- function(outcome, snp){
              model<-lm(outcome~as.numeric(snp))
              coef(summary(model))[2,4]
          }
min.p<-function(outcome,snps){
       min(sapply(snps, function(snp) one.p(outcome,snp)))
       }
min.p(bpdata$sbp, bpdata[,5:15])

replicate(3, min.p(sample(bpdata$sbp),bpdata[,5:15]))
manyp <- replicate(100, min.p(sample(bpdata$sbp),bpdata[,5:15]))

}
}