Construct test objects in a unified way.
Usage
pmt(key, ...)
pmts(
which = c("all", "onesample", "twosample", "distribution", "association", "paired",
"ksample", "multcomp", "rcbd", "table")
)
define_pmt(
method = c("twosample", "distribution", "association", "paired", "ksample", "rcbd",
"table"),
statistic,
rejection = c("<>", "<", ">"),
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.
- method
a character string specifying the permutation scheme.
- statistic
definition of the test statistic. See details.
- rejection
a character string specifying the rejection region relative to the test statistic.
- 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, alternative
character strings specifying the name of the test and the alternative hypothesis, used for printing purposes only.
- 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, and returns a double.
The purpose of this design is to pre-calculate certain constants that remain invariant during permutation.
When using Rcpp, the parameters for different method are listed as follows. Note that the names can be customized, and the types can be replaced with auto (thanks to the support for generic lambdas in C++14). See examples.
method | Parameter 1 | Parameter 2 |
"twosample" | const NumericVector& sample_1 | const NumericVector& sample_2 |
"distribution" | const NumericVector& cumulative_prob_1 | const NumericVector& cumulative_prob_2 |
"association" | const NumericVector& sample_1 | const NumericVector& sample_2 |
"paired" | const NumericVector& sample_1 | const NumericVector& sample_2 |
"ksample" | const NumericVector& combined_sample | const IntegerVector& one_based_group_index |
"rcbd" | const NumericMatrix& block_as_column_data | |
"table" | const IntegerMatrix& contingency_table |
When using R, the parameters should be the R equivalents of these.
Note
To improve performance when calling R functions from C++, this package repeatedly evaluates the function body of the inner closure in the same environment, where formal arguments are pre-assigned to the data and the enclosing environment is that of the closure. This imposes the following restrictions on the inner closure when statistic is written in R:
Do not reassign the inner closure’s formal arguments or any pre-computed symbols in the outer closure.
Do not use default arguments or variadic arguments.
It's also worth noting that the data is permuted in-place. Therefore, modifications to the data within statistic may lead to incorrect results. It is recommended to avoid modifying the data when using R and pass const references as in the table above when using Rcpp.
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: 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(
method = "twosample", rejection = "<",
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: twosample
#> statistic = 29, p-value = 0.6564 (± 0.009308055 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: twosample
#> statistic = 0.3487557, p-value = 0.5975 (± 0.009611695 at 95% confidence)
t$n_permu <- 0
t$print()
#>
#> User-Defined Permutation Test
#>
#> scoring: custom type: permu(252) method: twosample
#> statistic = 0.3487557, p-value = 0.6031746
# \donttest{
r <- define_pmt(
method = "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(
method = "twosample", n_permu = 1e5,
statistic = "[](const auto& x, const auto& y) {
auto m = x.length();
auto n = y.length();
return [=](const auto& x, const auto& y) {
return sum(x) / m - sum(y) / n;
};
}"
)
# equivalent
# rcpp <- define_pmt(
# method = "twosample", n_permu = 1e5,
# statistic = "[](const NumericVector& x, const NumericVector& y) {
# R_xlen_t m = x.length();
# R_xlen_t n = y.length();
# return [m, n](const NumericVector& x, const NumericVector& y) -> double {
# return sum(x) / m - sum(y) / n;
# };
# }"
# )
options(LearnNonparam.pmt_progress = FALSE)
system.time(r$test(x, y))
#> user system elapsed
#> 0.084 0.000 0.084
system.time(rcpp$test(x, y))
#> user system elapsed
#> 0.008 0.000 0.008
# }