# With two.sided sometimes p-values exceed 1 - huge problem
trinomial.test(col1 = c(1,8,1), alternative = "two.sided")
trinomial.test(col1 = c(1,8,2), alternative = "two.sided")
# # These replicate Table 1
# > sum(sapply(X = 5:10, FUN = function(x){prob.nd.cumsum(10, nd = x, p_tie = 0.20)}))
# [1] 0.05539103
# > sum(sapply(X = 4:10, FUN = function(x){prob.nd.cumsum(10, nd = x, p_tie = 0.30)}))
# [1] 0.09355447
# > sum(sapply(X = 5:10, FUN = function(x){prob.nd.cumsum(10, nd = x, p_tie = 0.30)}))
# [1] 0.0436493
# >
# # More replicating of Table 1
# > sapplyvecouttmp <- sapply(X = 0:10, FUN = function(x){prob.nd.cumsum(10, nd = x, p_tie = 0.20)})
# > obsprobtmp4 <- prob.nd.cumsum(n = 10, nd = 4, p_tie = 0.20)
# > sum(sapplyvecouttmp[sapplyvecouttmp < obsprobtmp4])
# [1] 0.05539103
# > obsprobtmp6 <- prob.nd.cumsum(n = 10, nd = 6, p_tie = 0.20)
# > obsprobtmp5 <- prob.nd.cumsum(n = 10, nd = 5, p_tie = 0.20)
# > sum(sapplyvecouttmp[sapplyvecouttmp < obsprobtmp4])
# [1] 0.05539103
# > sum(sapplyvecouttmp[sapplyvecouttmp < obsprobtmp5])
# [1] 0.02468086
# > sum(sapplyvecouttmp[sapplyvecouttmp < obsprobtmp6])
# [1] 0.009148826
# > sum(sapplyvecouttmp[sapplyvecouttmp < obsprobtmp5])
# [1] 0.02468086
#
# > sum(sapplyvecouttmp[sapplyvecouttmp <= obsprobtmp5])
# [1] 0.05539103
################################################################################
# Misc scratch
probs_vec <- c()
for (index in 1:(n + 1)){
j <- index - 1
for (k in 0:((n - j)/ 2)){
probs_vec[index] <- prob.nd(n = n, nd = j, k = k, p_tie = p_tie)
}
}
prob_obs <-
gen.probs.obj(n = 10, alpha = 0.05)
gen.probs.obj(n = 10, alpha = 0.025)
tmpRR <- gen.probs.obj(n = 10, alpha = 0.025)$RejectionRegion
pncRR <- function(RRrow){
n <- sum(RRrow)
nd <- RRrow[1]-RRrow[3]
}
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