Description Usage Arguments Details Value Author(s) Examples
Interfaces to coin
functions that can be used
in a pipeline implemented by magrittr
.
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 | ntbt_ansari_test(data, ...)
ntbt_chisq_test(data, ...)
ntbt_cmh_test(data, ...)
ntbt_conover_test(data, ...)
ntbt_fisyat_test(data, ...)
ntbt_fligner_test(data, ...)
ntbt_friedman_test(data, ...)
ntbt_independence_test(data, ...)
ntbt_klotz_test(data, ...)
ntbt_koziol_test(data, ...)
ntbt_kruskal_test(data, ...)
ntbt_lbl_test(data, ...)
ntbt_logrank_test(data, ...)
ntbt_maxstat_test(data, ...)
ntbt_median_test(data, ...)
ntbt_mh_test(data, ...)
ntbt_mood_test(data, ...)
ntbt_normal_test(data, ...)
ntbt_oneway_test(data, ...)
ntbt_quade_test(data, ...)
ntbt_quadrant_test(data, ...)
ntbt_sign_test(data, ...)
ntbt_symmetry_test(data, ...)
ntbt_taha_test(data, ...)
ntbt_savage_test(data, ...)
ntbt_spearman_test(data, ...)
ntbt_wilcox_test(data, ...)
ntbt_wilcoxsign_test(data, ...)
|
data |
data frame, tibble, list, ... |
... |
Other arguments passed to the corresponding interfaced function. |
Interfaces call their corresponding interfaced function.
Object returned by interfaced function.
Roberto Bertolusso
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 | ## Not run:
library(intubate)
library(magrittr)
library(coin)
## Tests of Independence in Two- or Three-Way Contingency Tables
## Please contribute better example
## Original function to interface
chisq_test(Plant ~ Type, data = CO2)
cmh_test(Plant ~ Type, data = CO2)
lbl_test(Plant ~ Type, data = CO2)
## The interface puts data as first parameter
ntbt_chisq_test(CO2, Plant ~ Type)
ntbt_cmh_test(CO2, Plant ~ Type)
ntbt_lbl_test(CO2, Plant ~ Type)
## so it can be used easily in a pipeline.
CO2 %>%
ntbt_chisq_test(Plant ~ Type)
CO2 %>%
ntbt_cmh_test(Plant ~ Type)
CO2 %>%
ntbt_lbl_test(Plant ~ Type)
## Correlation Tests
## Original function to interface
## Asymptotic Spearman test
spearman_test(CONT ~ INTG, data = USJudgeRatings)
## Asymptotic Fisher-Yates test
fisyat_test(CONT ~ INTG, data = USJudgeRatings)
## Asymptotic quadrant test
quadrant_test(CONT ~ INTG, data = USJudgeRatings)
## Asymptotic Koziol-Nemec test
koziol_test(CONT ~ INTG, data = USJudgeRatings)
## The interface puts data as first parameter
## Asymptotic Spearman test
ntbt_spearman_test(USJudgeRatings, CONT ~ INTG)
## Asymptotic Fisher-Yates test
ntbt_fisyat_test(USJudgeRatings, CONT ~ INTG)
## Asymptotic quadrant test
ntbt_quadrant_test(USJudgeRatings, CONT ~ INTG)
## Asymptotic Koziol-Nemec test
ntbt_koziol_test(USJudgeRatings, CONT ~ INTG)
## so it can be used easily in a pipeline.
## Asymptotic Spearman test
USJudgeRatings %>%
ntbt_spearman_test(CONT ~ INTG)
## Asymptotic Fisher-Yates test
USJudgeRatings %>%
ntbt_fisyat_test(CONT ~ INTG)
## Asymptotic quadrant test
USJudgeRatings %>%
ntbt_quadrant_test(CONT ~ INTG)
## Asymptotic Koziol-Nemec test
USJudgeRatings %>%
ntbt_koziol_test(CONT ~ INTG)
## ntbt_independence_test: General Independence Test
## Original function to interface
independence_test(asat ~ group, data = asat, distribution = "exact",
alternative = "greater",
ytrafo = function(data)
trafo(data, numeric_trafo = normal_trafo),
xtrafo = function(data)
trafo(data, factor_trafo = function(x)
matrix(x == levels(x)[1], ncol = 1)))
## The interface puts data as first parameter
ntbt_independence_test(asat, asat ~ group, distribution = "exact",
alternative = "greater",
ytrafo = function(data)
trafo(data, numeric_trafo = normal_trafo),
xtrafo = function(data)
trafo(data, factor_trafo = function(x)
matrix(x == levels(x)[1], ncol = 1)))
## so it can be used easily in a pipeline.
asat %>%
ntbt_independence_test(asat ~ group, distribution = "exact",
alternative = "greater",
ytrafo = function(data)
trafo(data, numeric_trafo = normal_trafo),
xtrafo = function(data)
trafo(data, factor_trafo = function(x)
matrix(x == levels(x)[1], ncol = 1)))
## Two- and K-Sample Location Tests
## Tritiated Water Diffusion Across Human Chorioamnion
## Hollander and Wolfe (1999, p. 110, Tab. 4.1)
diffusion <- data.frame(
pd = c(0.80, 0.83, 1.89, 1.04, 1.45, 1.38, 1.91, 1.64, 0.73, 1.46,
1.15, 0.88, 0.90, 0.74, 1.21),
age = factor(rep(c("At term", "12-26 Weeks"), c(10, 5)))
)
ex <- data.frame(
y = c(3, 4, 8, 9, 1, 2, 5, 6, 7),
x = factor(rep(c("no", "yes"), c(4, 5)))
)
## Original function to interface
kruskal_test(pd ~ age, data = diffusion, distribution = "exact")
median_test(y ~ x, data = ex, distribution = "exact")
normal_test(pd ~ age, data = diffusion, distribution = "exact", conf.int = TRUE)
oneway_test(pd ~ age, data = diffusion)
savage_test(pd ~ age, data = diffusion, distribution = "exact", conf.int = TRUE)
wilcox_test(pd ~ age, data = diffusion, distribution = "exact", conf.int = TRUE)
## The interface puts data as first parameter
ntbt_kruskal_test(diffusion, pd ~ age, distribution = "exact")
ntbt_median_test(ex, y ~ x, distribution = "exact")
ntbt_normal_test(diffusion, pd ~ age, distribution = "exact", conf.int = TRUE)
ntbt_oneway_test(diffusion, pd ~ age)
ntbt_savage_test(diffusion, pd ~ age, distribution = "exact", conf.int = TRUE)
ntbt_wilcox_test(diffusion, pd ~ age, distribution = "exact", conf.int = TRUE)
## so it can be used easily in a pipeline.
diffusion %>%
ntbt_kruskal_test(pd ~ age, distribution = "exact")
ex %>%
ntbt_median_test(y ~ x, distribution = "exact")
diffusion %>%
ntbt_normal_test(pd ~ age, distribution = "exact", conf.int = TRUE)
diffusion %>%
ntbt_oneway_test(pd ~ age)
diffusion %>%
ntbt_savage_test(pd ~ age, distribution = "exact", conf.int = TRUE)
diffusion %>%
ntbt_wilcox_test(pd ~ age, distribution = "exact", conf.int = TRUE)
performance <- matrix(
c(794, 150,
86, 570),
nrow = 2, byrow = TRUE,
dimnames = list(
"First" = c("Approve", "Disprove"),
"Second" = c("Approve", "Disprove")
)
)
## ntbt_mh_test: Marginal Homogeneity Tests
## Effectiveness of different media for the growth of diphtheria
## Cochran (1950, Tab. 2)
cases <- c(4, 2, 3, 1, 59)
n <- sum(cases)
cochran <- data.frame(
diphtheria = factor(
unlist(rep(list(c(1, 1, 1, 1),
c(1, 1, 0, 1),
c(0, 1, 1, 1),
c(0, 1, 0, 1),
c(0, 0, 0, 0)),
cases))
),
media = factor(rep(LETTERS[1:4], n)),
case = factor(rep(seq_len(n), each = 4))
)
## Original function to interface
mh_test(diphtheria ~ media | case, data = cochran)
## The interface puts data as first parameter
ntbt_mh_test(cochran, diphtheria ~ media | case)
## so it can be used easily in a pipeline.
cochran %>%
ntbt_mh_test(diphtheria ~ media | case)
## ntbt_maxstat_test: Generalized Maximally Selected Statistics
## Original function to interface
maxstat_test(counts ~ coverstorey, data = treepipit)
## The interface puts data as first parameter
ntbt_maxstat_test(treepipit, counts ~ coverstorey)
## so it can be used easily in a pipeline.
treepipit %>%
ntbt_maxstat_test(counts ~ coverstorey)
## Two- and K-Sample Scale Tests
## Serum Iron Determination Using Hyland Control Sera
## Hollander and Wolfe (1999, p. 147, Tab 5.1)
sid <- data.frame(
serum = c(111, 107, 100, 99, 102, 106, 109, 108, 104, 99,
101, 96, 97, 102, 107, 113, 116, 113, 110, 98,
107, 108, 106, 98, 105, 103, 110, 105, 104,
100, 96, 108, 103, 104, 114, 114, 113, 108, 106, 99),
method = gl(2, 20, labels = c("Ramsay", "Jung-Parekh"))
)
## Original function to interface
ansari_test(serum ~ method, data = sid)
conover_test(serum ~ method, data = sid)
fligner_test(serum ~ method, data = sid)
klotz_test(serum ~ method, data = sid)
mood_test(serum ~ method, data = sid)
taha_test(serum ~ method, data = sid)
## The interface puts data as first parameter
ntbt_ansari_test(sid, serum ~ method)
ntbt_conover_test(sid, serum ~ method)
ntbt_fligner_test(sid, serum ~ method)
ntbt_klotz_test(sid, serum ~ method)
ntbt_mood_test(sid, serum ~ method)
ntbt_taha_test(sid, serum ~ method)
## so it can be used easily in a pipeline.
sid %>%
ntbt_ansari_test(serum ~ method)
sid %>%
ntbt_conover_test(serum ~ method)
sid %>%
ntbt_fligner_test(serum ~ method)
sid %>%
ntbt_klotz_test(serum ~ method)
sid %>%
ntbt_mood_test(serum ~ method)
sid %>%
ntbt_taha_test(serum ~ method)
## ntbt_logrank_test: Two- and K-Sample Tests for Censored Data
## Example data (Callaert, 2003, Tab.1)
callaert <- data.frame(
time = c(1, 1, 5, 6, 6, 6, 6, 2, 2, 2, 3, 4, 4, 5, 5),
group = factor(rep(0:1, c(7, 8)))
)
## Original function to interface
logrank_test(Surv(time) ~ group, data = callaert, distribution = "exact")
## The interface puts data as first parameter
ntbt_logrank_test(callaert, Surv(time) ~ group, distribution = "exact")
## so it can be used easily in a pipeline.
callaert %>%
ntbt_logrank_test(Surv(time) ~ group, distribution = "exact")
## ntbt_symmetry_test: General Symmetry Test
## One-sided exact Fisher-Pitman test for paired observations
y1 <- c(1.83, 0.50, 1.62, 2.48, 1.68, 1.88, 1.55, 3.06, 1.30)
y2 <- c(0.878, 0.647, 0.598, 2.05, 1.06, 1.29, 1.06, 3.14, 1.29)
dta <- data.frame(
y = c(y1, y2),
x = gl(2, length(y1)),
block = factor(rep(seq_along(y1), 2))
)
## Original function to interface
symmetry_test(y ~ x | block, data = dta, distribution = "exact", alternative = "greater")
## The interface puts data as first parameter
ntbt_symmetry_test(dta, y ~ x | block, distribution = "exact", alternative = "greater")
## so it can be used easily in a pipeline.
dta %>%
ntbt_symmetry_test(y ~ x | block, distribution = "exact", alternative = "greater")
## Symmetry Tests
## Data with explicit group and block information
dta <- data.frame(y = c(y1, y2), x = gl(2, length(y1)),
block = factor(rep(seq_along(y1), 2)))
## Original function to interface
## For two samples, the sign test is equivalent to the Friedman test...
sign_test(y ~ x | block, data = dta, distribution = "exact")
friedman_test(y ~ x | block, data = dta, distribution = "exact")
## ...and the signed-rank test is equivalent to the Quade test
wilcoxsign_test(y ~ x | block, data = dta, distribution = "exact")
quade_test(y ~ x | block, data = dta, distribution = "exact")
## The interface puts data as first parameter
ntbt_sign_test(dta, y ~ x | block, distribution = "exact")
ntbt_friedman_test(dta, y ~ x | block, distribution = "exact")
ntbt_wilcoxsign_test(dta, y ~ x | block, distribution = "exact")
ntbt_quade_test(dta, y ~ x | block, distribution = "exact")
## so it can be used easily in a pipeline.
dta %>%
ntbt_sign_test(y ~ x | block, distribution = "exact")
dta %>%
ntbt_friedman_test(y ~ x | block, distribution = "exact")
dta %>%
ntbt_wilcoxsign_test(y ~ x | block, distribution = "exact")
dta %>%
ntbt_quade_test(y ~ x | block, distribution = "exact")
## End(Not run)
|
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