Performs studentized statistic based multiple comparison on samples.
Super classes
LearnNonparam::PermuTest -> LearnNonparam::KSampleTest -> LearnNonparam::MultipleComparison -> Studentized
Methods
Method new()
Create a new Studentized object.
Usage
Studentized$new(
type = c("permu", "asymp"),
method = c("bonferroni", "tukey"),
scoring = c("none", "rank", "vw", "expon"),
conf_level = 0.95,
n_permu = 10000
)Arguments
typea character string specifying the way to calculate the p-value.
methoda character string specifying whether to use Bonferroni's method or Tukey's HSD method.
scoringa character string specifying the scoring system.
conf_levela number between zero and one indicating the family-wise confidence level to use.
n_permuan integer indicating number of permutations for the permutation distribution. If set to
0, all permutations will be used.
Examples
t <- pmt(
"multcomp.studentized", method = "bonferroni"
)$test(Table3.3.1)$print()
#>
#> Multiple Comparison Based on Studentized Statistic
#>
#> scoring: none type: permu(10000) method: bonferroni
#>
#> family-wise confidence level: 95%
#>
#> statistic p-value
#> location_1 ~ location_2 0.02737145 0.9754
#> location_1 ~ location_3 -1.77914406 0.0844
#> location_1 ~ location_4 -3.41048230 0.0018 *
#> location_2 ~ location_3 -1.80651550 0.0868
#> location_2 ~ location_4 -3.43785375 0.0018 *
#> location_3 ~ location_4 -1.63133824 0.1154
t$type <- "asymp"
t
#>
#> Multiple Comparison Based on Studentized Statistic
#>
#> scoring: none type: asymp method: bonferroni
#>
#> family-wise confidence level: 95%
#>
#> statistic p-value
#> location_1 ~ location_2 0.02737145 0.978434752
#> location_1 ~ location_3 -1.77914406 0.090419680
#> location_1 ~ location_4 -3.41048230 0.002773484 *
#> location_2 ~ location_3 -1.80651550 0.085909110
#> location_2 ~ location_4 -3.43785375 0.002603664 *
#> location_3 ~ location_4 -1.63133824 0.118466249
t$scoring <- "rank"
t
#>
#> Multiple Comparison Based on Studentized Statistic
#>
#> scoring: rank type: asymp method: bonferroni
#>
#> family-wise confidence level: 95%
#>
#> statistic p-value
#> location_1 ~ location_2 0.1224745 0.902523250
#> location_1 ~ location_3 -1.3880442 0.165123590
#> location_1 ~ location_4 -2.6536139 0.007963489 *
#> location_2 ~ location_3 -1.5105187 0.130911128
#> location_2 ~ location_4 -2.7760884 0.005501725 *
#> location_3 ~ location_4 -1.2655697 0.205667180
t$method <- "tukey"
t
#>
#> Multiple Comparison Based on Studentized Statistic
#>
#> scoring: rank type: asymp method: tukey
#>
#> family-wise confidence level: 95%
#>
#> statistic p-value
#> location_1 ~ location_2 0.1732051 0.99934385
#> location_1 ~ location_3 1.9629909 0.50683497
#> location_1 ~ location_4 3.7527767 0.03978340 *
#> location_2 ~ location_3 2.1361960 0.43109997
#> location_2 ~ location_4 3.9259818 0.02817015 *
#> location_3 ~ location_4 1.7897858 0.58494735
t$scoring <- "none"
t
#>
#> Multiple Comparison Based on Studentized Statistic
#>
#> scoring: none type: asymp method: tukey
#>
#> family-wise confidence level: 95%
#>
#> statistic p-value
#> location_1 ~ location_2 0.03870907 0.99999236
#> location_1 ~ location_3 2.51608965 0.31194094
#> location_1 ~ location_4 4.82315032 0.01356971 *
#> location_2 ~ location_3 2.55479873 0.29951264
#> location_2 ~ location_4 4.86185939 0.01277452 *
#> location_3 ~ location_4 2.30706067 0.38453323
t$type <- "permu"
t
#>
#> Multiple Comparison Based on Studentized Statistic
#>
#> scoring: none type: permu(10000) method: tukey
#>
#> family-wise confidence level: 95%
#>
#> statistic p-value
#> location_1 ~ location_2 0.03870907 1.0000
#> location_1 ~ location_3 2.51608965 0.3172
#> location_1 ~ location_4 4.82315032 0.0150 *
#> location_2 ~ location_3 2.55479873 0.3052
#> location_2 ~ location_4 4.86185939 0.0143 *
#> location_3 ~ location_4 2.30706067 0.3921