smmcrit:
In musto101/wilcox_R: What the package does (short line)
Usage
1
Arguments
nuhat
C
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##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (nuhat, C)
{
if (C - round(C) != 0)
stop("The number of contrasts, C, must be an integer")
if (C >= 29)
stop("C must be less than or equal to 28")
if (C <= 0)
stop("C must be greater than or equal to 1")
if (nuhat < 2)
stop("The degrees of freedom must be greater than or equal to 2")
if (C == 1)
smmcrit <- qt(0.975, nuhat)
if (C >= 2) {
C <- C - 1
m1 <- matrix(0, 20, 27)
m1[1, ] <- c(5.57, 6.34, 6.89, 7.31, 7.65, 7.93, 8.17,
8.83, 8.57, 8.74, 8.89, 9.03, 9.16, 9.28, 9.39, 9.49,
9.59, 9.68, 9.77, 9.85, 9.92, 10, 10.07, 10.13, 10.2,
10.26, 10.32)
m1[2, ] <- c(3.96, 4.43, 4.76, 5.02, 5.23, 5.41, 5.56,
5.69, 5.81, 5.92, 6.01, 6.1, 6.18, 6.26, 6.33, 6.39,
6.45, 6.51, 6.57, 6.62, 6.67, 6.71, 6.76, 6.8, 6.84,
6.88, 6.92)
m1[3, ] <- c(3.38, 3.74, 4.01, 4.2, 4.37, 4.5, 4.62,
4.72, 4.82, 4.89, 4.97, 5.04, 5.11, 5.17, 5.22, 5.27,
5.32, 5.37, 5.41, 5.45, 5.49, 5.52, 5.56, 5.59, 5.63,
5.66, 5.69)
m1[4, ] <- c(3.09, 3.39, 3.62, 3.79, 3.93, 4.04, 4.14,
4.23, 4.31, 4.38, 4.45, 4.51, 4.56, 4.61, 4.66, 4.7,
4.74, 4.78, 4.82, 4.85, 4.89, 4.92, 4.95, 4.98, 5,
5.03, 5.06)
m1[5, ] <- c(2.92, 3.19, 3.39, 3.54, 3.66, 3.77, 3.86,
3.94, 4.01, 4.07, 4.13, 4.18, 4.23, 4.28, 4.32, 4.36,
4.39, 4.43, 4.46, 4.49, 4.52, 4.55, 4.58, 4.6, 4.63,
4.65, 4.68)
m1[6, ] <- c(2.8, 3.06, 3.24, 3.38, 3.49, 3.59, 3.67,
3.74, 3.8, 3.86, 3.92, 3.96, 4.01, 4.05, 4.09, 4.13,
4.16, 4.19, 4.22, 4.25, 4.28, 4.31, 4.33, 4.35, 4.38,
4.39, 4.42)
m1[7, ] <- c(2.72, 2.96, 3.13, 3.26, 3.36, 3.45, 3.53,
3.6, 3.66, 3.71, 3.76, 3.81, 3.85, 3.89, 3.93, 3.96,
3.99, 4.02, 4.05, 4.08, 4.1, 4.13, 4.15, 4.18, 4.19,
4.22, 4.24)
m1[8, ] <- c(2.66, 2.89, 3.05, 3.17, 3.27, 3.36, 3.43,
3.49, 3.55, 3.6, 3.65, 3.69, 3.73, 3.77, 3.8, 3.84,
3.87, 3.89, 3.92, 3.95, 3.97, 3.99, 4.02, 4.04, 4.06,
4.08, 4.09)
m1[9, ] <- c(2.61, 2.83, 2.98, 3.1, 3.19, 3.28, 3.35,
3.41, 3.47, 3.52, 3.56, 3.6, 3.64, 3.68, 3.71, 3.74,
3.77, 3.79, 3.82, 3.85, 3.87, 3.89, 3.91, 3.94, 3.95,
3.97, 3.99)
m1[10, ] <- c(2.57, 2.78, 2.93, 3.05, 3.14, 3.22, 3.29,
3.35, 3.4, 3.45, 3.49, 3.53, 3.57, 3.6, 3.63, 3.66,
3.69, 3.72, 3.74, 3.77, 3.79, 3.81, 3.83, 3.85, 3.87,
3.89, 3.91)
m1[11, ] <- c(2.54, 2.75, 2.89, 3.01, 3.09, 3.17, 3.24,
3.29, 3.35, 3.39, 3.43, 3.47, 3.51, 3.54, 3.57, 3.6,
3.63, 3.65, 3.68, 3.7, 3.72, 3.74, 3.76, 3.78, 3.8,
3.82, 3.83)
m1[12, ] <- c(2.49, 2.69, 2.83, 2.94, 3.02, 3.09, 3.16,
3.21, 3.26, 3.3, 3.34, 3.38, 3.41, 3.45, 3.48, 3.5,
3.53, 3.55, 3.58, 3.59, 3.62, 3.64, 3.66, 3.68, 3.69,
3.71, 3.73)
m1[13, ] <- c(2.46, 2.65, 2.78, 2.89, 2.97, 3.04, 3.09,
3.15, 3.19, 3.24, 3.28, 3.31, 3.35, 3.38, 3.4, 3.43,
3.46, 3.48, 3.5, 3.52, 3.54, 3.56, 3.58, 3.59, 3.61,
3.63, 3.64)
m1[14, ] <- c(2.43, 2.62, 2.75, 2.85, 2.93, 2.99, 3.05,
3.11, 3.15, 3.19, 3.23, 3.26, 3.29, 3.32, 3.35, 3.38,
3.4, 3.42, 3.44, 3.46, 3.48, 3.5, 3.52, 3.54, 3.55,
3.57, 3.58)
m1[15, ] <- c(2.41, 2.59, 2.72, 2.82, 2.89, 2.96, 3.02,
3.07, 3.11, 3.15, 3.19, 3.22, 3.25, 3.28, 3.31, 3.33,
3.36, 3.38, 3.39, 3.42, 3.44, 3.46, 3.47, 3.49, 3.5,
3.52, 3.53)
m1[16, ] <- c(2.38, 2.56, 2.68, 2.77, 2.85, 2.91, 2.97,
3.02, 3.06, 3.09, 3.13, 3.16, 3.19, 3.22, 3.25, 3.27,
3.29, 3.31, 3.33, 3.35, 3.37, 3.39, 3.4, 3.42, 3.43,
3.45, 3.46)
m1[17, ] <- c(2.35, 2.52, 2.64, 2.73, 2.8, 2.87, 2.92,
2.96, 3.01, 3.04, 3.07, 3.11, 3.13, 3.16, 3.18, 3.21,
3.23, 3.25, 3.27, 3.29, 3.3, 3.32, 3.33, 3.35, 3.36,
3.37, 3.39)
m1[18, ] <- c(2.32, 2.49, 2.6, 2.69, 2.76, 2.82, 2.87,
2.91, 2.95, 2.99, 3.02, 3.05, 3.08, 3.09, 3.12, 3.14,
3.17, 3.18, 3.2, 3.22, 3.24, 3.25, 3.27, 3.28, 3.29,
3.31, 3.32)
m1[19, ] <- c(2.29, 2.45, 2.56, 2.65, 2.72, 2.77, 2.82,
2.86, 2.9, 2.93, 2.96, 2.99, 3.02, 3.04, 3.06, 3.08,
3.1, 3.12, 3.14, 3.16, 3.17, 3.19, 3.2, 3.21, 3.23,
3.24, 3.25)
m1[20, ] <- c(2.24, 2.39, 2.49, 2.57, 2.63, 2.68, 2.73,
2.77, 2.79, 2.83, 2.86, 2.88, 2.91, 2.93, 2.95, 2.97,
2.98, 3.01, 3.02, 3.03, 3.04, 3.06, 3.07, 3.08, 3.09,
3.11, 3.12)
if (nuhat >= 200)
smmcrit <- m1[20, C]
if (nuhat < 200) {
nu <- c(2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16,
18, 20, 24, 30, 40, 60, 200)
temp <- abs(nu - nuhat)
find <- order(temp)
if (temp[find[1]] == 0)
smmcrit <- m1[find[1], C]
if (temp[find[1]] != 0) {
if (nuhat > nu[find[1]]) {
smmcrit <- m1[find[1], C] - (1/nu[find[1]] -
1/nuhat) * (m1[find[1], C] - m1[find[1] +
1, C])/(1/nu[find[1]] - 1/nu[find[1] + 1])
}
if (nuhat < nu[find[1]]) {
smmcrit <- m1[find[1] - 1, C] - (1/nu[find[1] -
1] - 1/nuhat) * (m1[find[1] - 1, C] - m1[find[1],
C])/(1/nu[find[1] - 1] - 1/nu[find[1]])
}
}
}
}
smmcrit
}
musto101/wilcox_R documentation built on May 23, 2019, 10:52 a.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Usage
1 |
Arguments
nuhat |
|
C |
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | ##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (nuhat, C)
{
if (C - round(C) != 0)
stop("The number of contrasts, C, must be an integer")
if (C >= 29)
stop("C must be less than or equal to 28")
if (C <= 0)
stop("C must be greater than or equal to 1")
if (nuhat < 2)
stop("The degrees of freedom must be greater than or equal to 2")
if (C == 1)
smmcrit <- qt(0.975, nuhat)
if (C >= 2) {
C <- C - 1
m1 <- matrix(0, 20, 27)
m1[1, ] <- c(5.57, 6.34, 6.89, 7.31, 7.65, 7.93, 8.17,
8.83, 8.57, 8.74, 8.89, 9.03, 9.16, 9.28, 9.39, 9.49,
9.59, 9.68, 9.77, 9.85, 9.92, 10, 10.07, 10.13, 10.2,
10.26, 10.32)
m1[2, ] <- c(3.96, 4.43, 4.76, 5.02, 5.23, 5.41, 5.56,
5.69, 5.81, 5.92, 6.01, 6.1, 6.18, 6.26, 6.33, 6.39,
6.45, 6.51, 6.57, 6.62, 6.67, 6.71, 6.76, 6.8, 6.84,
6.88, 6.92)
m1[3, ] <- c(3.38, 3.74, 4.01, 4.2, 4.37, 4.5, 4.62,
4.72, 4.82, 4.89, 4.97, 5.04, 5.11, 5.17, 5.22, 5.27,
5.32, 5.37, 5.41, 5.45, 5.49, 5.52, 5.56, 5.59, 5.63,
5.66, 5.69)
m1[4, ] <- c(3.09, 3.39, 3.62, 3.79, 3.93, 4.04, 4.14,
4.23, 4.31, 4.38, 4.45, 4.51, 4.56, 4.61, 4.66, 4.7,
4.74, 4.78, 4.82, 4.85, 4.89, 4.92, 4.95, 4.98, 5,
5.03, 5.06)
m1[5, ] <- c(2.92, 3.19, 3.39, 3.54, 3.66, 3.77, 3.86,
3.94, 4.01, 4.07, 4.13, 4.18, 4.23, 4.28, 4.32, 4.36,
4.39, 4.43, 4.46, 4.49, 4.52, 4.55, 4.58, 4.6, 4.63,
4.65, 4.68)
m1[6, ] <- c(2.8, 3.06, 3.24, 3.38, 3.49, 3.59, 3.67,
3.74, 3.8, 3.86, 3.92, 3.96, 4.01, 4.05, 4.09, 4.13,
4.16, 4.19, 4.22, 4.25, 4.28, 4.31, 4.33, 4.35, 4.38,
4.39, 4.42)
m1[7, ] <- c(2.72, 2.96, 3.13, 3.26, 3.36, 3.45, 3.53,
3.6, 3.66, 3.71, 3.76, 3.81, 3.85, 3.89, 3.93, 3.96,
3.99, 4.02, 4.05, 4.08, 4.1, 4.13, 4.15, 4.18, 4.19,
4.22, 4.24)
m1[8, ] <- c(2.66, 2.89, 3.05, 3.17, 3.27, 3.36, 3.43,
3.49, 3.55, 3.6, 3.65, 3.69, 3.73, 3.77, 3.8, 3.84,
3.87, 3.89, 3.92, 3.95, 3.97, 3.99, 4.02, 4.04, 4.06,
4.08, 4.09)
m1[9, ] <- c(2.61, 2.83, 2.98, 3.1, 3.19, 3.28, 3.35,
3.41, 3.47, 3.52, 3.56, 3.6, 3.64, 3.68, 3.71, 3.74,
3.77, 3.79, 3.82, 3.85, 3.87, 3.89, 3.91, 3.94, 3.95,
3.97, 3.99)
m1[10, ] <- c(2.57, 2.78, 2.93, 3.05, 3.14, 3.22, 3.29,
3.35, 3.4, 3.45, 3.49, 3.53, 3.57, 3.6, 3.63, 3.66,
3.69, 3.72, 3.74, 3.77, 3.79, 3.81, 3.83, 3.85, 3.87,
3.89, 3.91)
m1[11, ] <- c(2.54, 2.75, 2.89, 3.01, 3.09, 3.17, 3.24,
3.29, 3.35, 3.39, 3.43, 3.47, 3.51, 3.54, 3.57, 3.6,
3.63, 3.65, 3.68, 3.7, 3.72, 3.74, 3.76, 3.78, 3.8,
3.82, 3.83)
m1[12, ] <- c(2.49, 2.69, 2.83, 2.94, 3.02, 3.09, 3.16,
3.21, 3.26, 3.3, 3.34, 3.38, 3.41, 3.45, 3.48, 3.5,
3.53, 3.55, 3.58, 3.59, 3.62, 3.64, 3.66, 3.68, 3.69,
3.71, 3.73)
m1[13, ] <- c(2.46, 2.65, 2.78, 2.89, 2.97, 3.04, 3.09,
3.15, 3.19, 3.24, 3.28, 3.31, 3.35, 3.38, 3.4, 3.43,
3.46, 3.48, 3.5, 3.52, 3.54, 3.56, 3.58, 3.59, 3.61,
3.63, 3.64)
m1[14, ] <- c(2.43, 2.62, 2.75, 2.85, 2.93, 2.99, 3.05,
3.11, 3.15, 3.19, 3.23, 3.26, 3.29, 3.32, 3.35, 3.38,
3.4, 3.42, 3.44, 3.46, 3.48, 3.5, 3.52, 3.54, 3.55,
3.57, 3.58)
m1[15, ] <- c(2.41, 2.59, 2.72, 2.82, 2.89, 2.96, 3.02,
3.07, 3.11, 3.15, 3.19, 3.22, 3.25, 3.28, 3.31, 3.33,
3.36, 3.38, 3.39, 3.42, 3.44, 3.46, 3.47, 3.49, 3.5,
3.52, 3.53)
m1[16, ] <- c(2.38, 2.56, 2.68, 2.77, 2.85, 2.91, 2.97,
3.02, 3.06, 3.09, 3.13, 3.16, 3.19, 3.22, 3.25, 3.27,
3.29, 3.31, 3.33, 3.35, 3.37, 3.39, 3.4, 3.42, 3.43,
3.45, 3.46)
m1[17, ] <- c(2.35, 2.52, 2.64, 2.73, 2.8, 2.87, 2.92,
2.96, 3.01, 3.04, 3.07, 3.11, 3.13, 3.16, 3.18, 3.21,
3.23, 3.25, 3.27, 3.29, 3.3, 3.32, 3.33, 3.35, 3.36,
3.37, 3.39)
m1[18, ] <- c(2.32, 2.49, 2.6, 2.69, 2.76, 2.82, 2.87,
2.91, 2.95, 2.99, 3.02, 3.05, 3.08, 3.09, 3.12, 3.14,
3.17, 3.18, 3.2, 3.22, 3.24, 3.25, 3.27, 3.28, 3.29,
3.31, 3.32)
m1[19, ] <- c(2.29, 2.45, 2.56, 2.65, 2.72, 2.77, 2.82,
2.86, 2.9, 2.93, 2.96, 2.99, 3.02, 3.04, 3.06, 3.08,
3.1, 3.12, 3.14, 3.16, 3.17, 3.19, 3.2, 3.21, 3.23,
3.24, 3.25)
m1[20, ] <- c(2.24, 2.39, 2.49, 2.57, 2.63, 2.68, 2.73,
2.77, 2.79, 2.83, 2.86, 2.88, 2.91, 2.93, 2.95, 2.97,
2.98, 3.01, 3.02, 3.03, 3.04, 3.06, 3.07, 3.08, 3.09,
3.11, 3.12)
if (nuhat >= 200)
smmcrit <- m1[20, C]
if (nuhat < 200) {
nu <- c(2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16,
18, 20, 24, 30, 40, 60, 200)
temp <- abs(nu - nuhat)
find <- order(temp)
if (temp[find[1]] == 0)
smmcrit <- m1[find[1], C]
if (temp[find[1]] != 0) {
if (nuhat > nu[find[1]]) {
smmcrit <- m1[find[1], C] - (1/nu[find[1]] -
1/nuhat) * (m1[find[1], C] - m1[find[1] +
1, C])/(1/nu[find[1]] - 1/nu[find[1] + 1])
}
if (nuhat < nu[find[1]]) {
smmcrit <- m1[find[1] - 1, C] - (1/nu[find[1] -
1] - 1/nuhat) * (m1[find[1] - 1, C] - m1[find[1],
C])/(1/nu[find[1] - 1] - 1/nu[find[1]])
}
}
}
}
smmcrit
}
|
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