Test for Association Between Paired Samples

Performs correlation coefficient based two-sample association test on samples.

Super classes

LearnNonparam::PermuTest -> LearnNonparam::TwoSampleTest -> LearnNonparam::TwoSamplePairedTest -> LearnNonparam::TwoSampleAssociationTest -> Correlation

Methods

Public methods

• Correlation$new()

Inherited methods

• LearnNonparam::PermuTest$plot()
• LearnNonparam::PermuTest$print()
• LearnNonparam::PermuTest$test()

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Method new()

Create a new Correlation object.

Usage

Correlation$new(
  type = c("permu", "asymp"),
  method = c("pearson", "kendall", "spearman"),
  alternative = c("two_sided", "less", "greater"),
  n_permu = 10000
)

Arguments

type

a character string specifying the way to calculate the p-value.

method

a character string specifying the correlation coefficient to be used.

alternative

a character string specifying the alternative hypothesis.

n_permu

an integer indicating number of permutations for the permutation distribution. If set to 0, all permutations will be used.

Returns

A Correlation object.

Examples

pmt(
    "association.corr", method = "pearson",
    alternative = "greater", n_permu = 10000
)$test(Table5.1.2)$print()
#> 
#>  	 Test for Association Between Paired Samples 
#> 
#> scoring: none    type: permu(10000)    method: pearson
#> statistic = 198766597, p-value < 2.220446e-16
#> alternative hypothesis: true correlation is greater than 0

t <- pmt(
    "association.corr", method = "spearman",
    alternative = "two_sided", n_permu = 10000
)$test(Table5.1.2)$print()
#> 
#>  	 Test for Association Between Paired Samples 
#> 
#> scoring: rank    type: permu(10000)    method: spearman
#> statistic = 2059, p-value < 2.220446e-16
#> alternative hypothesis: true rho is not equal to 0

t$type <- "asymp"
t
#> 
#>  	 Test for Association Between Paired Samples 
#> 
#> scoring: rank    type: asymp    method: spearman
#> statistic = 0.8968008, p-value = 0.0002176436
#> alternative hypothesis: true rho is not equal to 0

t <- pmt(
    "association.corr", method = "kendall",
    alternative = "greater", n_permu = 0
)$test(Table5.2.2)$print()
#> 
#>  	 Test for Association Between Paired Samples 
#> 
#> scoring: rank    type: permu(151200)    method: kendall
#> statistic = 0.2222222, p-value = 0.187672
#> alternative hypothesis: true tau is greater than 0

t$type <- "asymp"
t
#> 
#>  	 Test for Association Between Paired Samples 
#> 
#> scoring: rank    type: asymp    method: kendall
#> statistic = 0.2222222, p-value = 0.1697136
#> alternative hypothesis: true tau is greater than 0