Construct test objects in a unified way.
Usage
pmt(key, ...)
pmts(
which = c("all", "onesample", "twosample", "ksample", "multcomp", "paired", "rcbd",
"association", "table")
)
define_pmt(
inherit = c("twosample", "ksample", "paired", "rcbd", "association", "table"),
statistic,
rejection = c("lr", "l", "r"),
scoring = "none",
n_permu = 10000,
name = "User-Defined Permutation Test",
alternative = NULL,
depends = character(),
plugins = character(),
includes = character()
)Arguments
- key
a character string specifying the test. Check
pmts()for valid keys.- ...
extra parameters passed to the constructor.
- which
a character string specifying the desired tests.
- inherit
a character string specifying the type of permutation test.
- statistic
definition of the test statistic. See Details.
- rejection
a character string specifying where the rejection region is.
- scoring
one of: - a character string in
c("none", "rank", "vw", "expon")specifying the scoring system - a function that takes a numeric vector and returns an equal-length score vector- n_permu
an integer indicating number of permutations for the permutation distribution. If set to
0, all permutations will be used.- name
a character string specifying the name of the test.
- alternative
a character string describing the alternative hypothesis.
- depends, plugins, includes
passed to
Rcpp::cppFunction().
Value
a test object corresponding to the specified key.
a data frame containing keys and corresponding tests implemented in this package.
a test object based on the specified statistic.
Details
The test statistic can be defined using either R or Rcpp, with the statistic parameter specified as:
R: a function returning a closure that returns a double.Rcpp: a character string defining a captureless lambda (since C++11) returning another lambda that captures by value, accepts parameters of the same type as const references, and returns a double.
When using Rcpp, the parameters for different inherit are listed as follows. Note that the parameter names are for illustration only.
"twosample":(Rcpp::NumericVector sample_1, Rcpp::NumericVector sample_2)"ksample":(Rcpp::NumericVector combined_sample, Rcpp::IntegerVector one_based_group_index)"paired":(Rcpp::NumericVector sample_1, Rcpp::NumericVector sample_2)"rcbd":(Rcpp::NumericMatrix block_as_column_data)"association":(Rcpp::NumericVector sample_1, Rcpp::NumericVector sample_2)"table":(Rcpp::IntegerMatrix contingency_table)
Defining the test statistic using R follows a similar approach. The purpose of this design is to pre-calculate certain constants that remain invariant during permutation.
Examples
pmt("twosample.wilcoxon")
#> <Wilcoxon>
#> Inherits from: <TwoSampleLocationTest>
#> Public:
#> alternative: active binding
#> conf_int: active binding
#> conf_level: active binding
#> correct: active binding
#> data: active binding
#> estimate: active binding
#> initialize: function (type = c("permu", "asymp"), alternative = c("two_sided",
#> method: active binding
#> n_permu: active binding
#> null_value: active binding
#> p_value: active binding
#> plot: function (style = c("graphics", "ggplot2"), ...)
#> print: function ()
#> scoring: active binding
#> statistic: active binding
#> test: function (...)
#> type: active binding
#> Private:
#> .alternative: two_sided
#> .autoplot: function (...)
#> .calculate: function ()
#> .calculate_extra: function ()
#> .calculate_n_permu: function ()
#> .calculate_p: function ()
#> .calculate_p_permu: function ()
#> .calculate_score: function ()
#> .calculate_side: function ()
#> .calculate_statistic: function ()
#> .compile_statistic_closure: function ()
#> .conf_int: NULL
#> .conf_level: 0.95
#> .correct: TRUE
#> .data: NULL
#> .define: function ()
#> .estimate: NULL
#> .link: +
#> .method: default
#> .n_permu: 10000
#> .name: Two-Sample Wilcoxon Test
#> .null_value: 0
#> .on_alternative_change: function ()
#> .on_conf_level_change: function ()
#> .on_method_change: function ()
#> .on_n_permu_change: function ()
#> .on_null_value_change: function ()
#> .on_scoring_change: function ()
#> .on_type_change: function ()
#> .p_value: NULL
#> .param_name: location shift
#> .plot: function (...)
#> .preprocess: function ()
#> .print: function ()
#> .raw_data: NULL
#> .scoring: rank
#> .side: NULL
#> .statistic: NULL
#> .statistic_func: NULL
#> .type: permu
pmts("ksample")
#> key class test
#> 1 ksample.oneway OneWay One-Way Test for Equal Means
#> 2 ksample.kw KruskalWallis Kruskal-Wallis Test
#> 3 ksample.jt JonckheereTerpstra Jonckheere-Terpstra Test
x <- rnorm(5)
y <- rnorm(5, 1)
t <- define_pmt(
inherit = "twosample",
scoring = base::rank, # equivalent to "rank"
statistic = function(...) function(x, y) sum(x)
)$test(x, y)$print()
#>
#> User-Defined Permutation Test
#>
#> scoring: custom type: permu(10000) method: default
#> statistic = 19, p-value = 0.09 (± 0.005609059 at 95% confidence)
t$scoring <- function(x) qnorm(rank(x) / (length(x) + 1)) # equivalent to "vw"
t$print()
#>
#> User-Defined Permutation Test
#>
#> scoring: custom type: permu(10000) method: default
#> statistic = -2.38528, p-value = 0.0654 (± 0.004845626 at 95% confidence)
t$n_permu <- 0
t$print()
#>
#> User-Defined Permutation Test
#>
#> scoring: custom type: permu(252) method: default
#> statistic = -2.38528, p-value = 0.06349206
# \donttest{
r <- define_pmt(
inherit = "twosample",
n_permu = 1e5,
statistic = function(x, y) {
m <- length(x)
n <- length(y)
function(x, y) sum(x) / m - sum(y) / n
}
)
rcpp <- define_pmt(
inherit = "twosample",
n_permu = 1e5,
statistic = "[](NumericVector x, NumericVector y) {
R_len_t m = x.size();
R_len_t n = y.size();
return [=](const NumericVector& x, const NumericVector& y) -> double {
return sum(x) / m - sum(y) / n;
};
}"
)
options(LearnNonparam.pmt_progress = FALSE)
system.time(r$test(x, y))
#> user system elapsed
#> 0.143 0.000 0.143
system.time(rcpp$test(x, y))
#> user system elapsed
#> 0.009 0.000 0.008
# }