Created
November 14, 2012 21:11
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regression for horse problem
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set.seed(10) | |
lm.power <- c() | |
spd.power <- c() | |
for (n in range) { | |
spd.pvalue <- c() | |
lm.pvalue <- c() | |
for (j in 1:1E2) { | |
mat <- c() | |
samplesize <- 1E2 | |
for (i in 1:samplesize) { | |
mat <- rbind(mat,sample(1:n, prob=c(rep(1.2,floor(n / 2)), rep(1, n - floor(n / 2))))) | |
} | |
distances <- apply(mat, 1, permdist) | |
logd <- apply(mat, 1, tolodds) | |
# creating log-odds of start | |
n1 <- (1:n) / (n+1) | |
n2 <- log(n1 / (1 - n1)) | |
logn <- rep(n2,samplesize) | |
# THIS LINE IS THE REGRESSION THING. THE OTHER LINES ARE NOT THAT IMPORTANT | |
# calculate the p-value of the linear model test | |
lm.pvalue <- c(lm.pvalue,summary(lm(as.vector(logd)~logn))$coef[2,4]) | |
spd <- (distances - means[n-first+1])/sds[n-first+1] | |
average <- mean(spd) | |
# calculate the p-value of the permutation test (CLT) | |
spd.pvalue <- c(spd.pvalue, pnorm(average*sqrt(length(distances)))) | |
} | |
# calculate the simulated power | |
spd.power <- c(spd.power, sum(spd.pvalue < 0.05 | spd.pvalue > 0.95)/length(spd.pvalue)) | |
lm.power <- c(lm.power, sum(lm.pvalue < 0.05 | lm.pvalue > 0.95)/length(lm.pvalue)) | |
} |
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