Syntactic Sugar for Object Construction

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("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.
method: a character string specifying the permutation scheme.
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, 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

statistic should not cause errors or return missing values.
The data is permuted in-place. Therefore, modifications to the data within statistic may lead to incorrect results. Since R has copy-on-modify semantics but C++ does not, it is recommended to pass const references when using Rcpp in define_pmt, as shown in the table above.

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",
    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 = 22, p-value = 0.312 (± 0.009080706 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 = -1.398858, p-value = 0.2988 (± 0.008971382 at 95% confidence)

t$n_permu <- 0
t$print()
#> 
#>  	 User-Defined Permutation Test 
#> 
#> scoring: custom    type: permu(252)    method: twosample
#> statistic = -1.398858, p-value = 0.3015873

# \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$test(x, y))
#>    user  system elapsed 
#>   0.084   0.000   0.085 
system.time(rcpp$test(x, y))
#>    user  system elapsed 
#>   0.009   0.000   0.008 
# }