Package 'runDRT'

Run Doubly Ranked Tests

Description

Performs two (or more) sample doubly ranked tests on pre-processed functional data, formatted as either a matrix (for functions) or an array (for surfaces).

Usage

run_drt(X, G, method = c("suff.rank", "avg.rank"), data.names = NULL)

## Default S3 method:
run_drt(X, G, method = c("suff.rank", "avg.rank"), data.names = NULL)

## S3 method for class 'formula'
run_drt(formula, ...)

Arguments

X	an n by T matrix or an S by T by n array containing the functions (or surfaces) to analyze.
G	a vector of length n containing the grouping variable.
method	statistic for summarizing the ranks: 'suff.rank' for sufficient statistic (the default) or 'avg.rank' for arithmetic average.
data.names	a vector of length two containing names that describe X and G.
formula	a formula of the form X ~ G.
...	additional arguments to pass to run_drt.default(), e.g. method.

Details

Doubly ranked tests are non-parametric tests that first rank functions (or surfaces) by time (or location). Next, the procedure summarizes the observed ranks using a statistic. The summarized ranks are then analyzed using either a Wilcoxon rank sum test or a Kruskal-Wallis test. To perform a doubly ranked test, realizations of functions must be stored in an n by T matrix where n is the total number of observed functions and T is the number of realizations per function (commonly time points or locations). Surface data in an S by T by n array can be analyzed as well, although currently this feature has under gone only limited testing.

By default, run_drt() implements a sufficient statistic when summarizing the ranks of each observed function across T, i.e. the argument method defaults to method = suff.rank. This statistic has the form

$t(z) = \frac{1}{T}\sum_{t=1}^T\log\left[ \left(\frac{z_t}{n}- \frac{1}{2n}\right)\bigg/\left(1-\frac{z_t}{n} + \frac{1}{2n}\right) \right],$

where $z_t$ is the observed rank at time $t$. See Meyer (2024) for additional details. The average rank may also be used by setting method = 'avg.rank', although this summary has not undergone testing in the doubly ranked context.

Regardless of the statistic used, the summarized ranks are the analyzed using either wilcox.test() or kruskal.test(), depending on the number of groups in G.

For functional data, Meyer (2024) suggests using refund::fpca.face() for pre-processing the data, but X can be pre-processed using any functional data approach or it can just be the raw data. run_drt() itself performs no pre-processing and takes X as inputted.

Value

A list with class "htest" containing the following components:

statistic	the value of the test statistic with a name describing it.
parameter	the parameter(s) for the exact distribution of the test statistic.
p.value	the p-value for the test.
null.value	the location parameter.
alternative	a character string describing the alternative hypothesis.
data.name	a character string giving the names of the data.
test_details	the output from the internally run Wilcoxon rank sum or Kruskal-Wallis test.
method	character string giving the type of doubly ranked test performed.
ranks	a list containing the ranks by column (if X is a matrix) and the summarized ranks.
data	a list containing X and G.

References

Meyer, MJ (2024). Doubly ranked tests for grouped functional data. Available on arXiv at https://arxiv.org/abs/2306.14761.

Examples

#### Two Sample Problem: Resin Viscosity ####
library(FDboost)
data("viscosity")

Xv    <- matrix(viscosity$visAll, nrow = nrow(viscosity$visAll), ncol = ncol(viscosity$visAll))
fXv   <- refund::fpca.face(Xv)
Yvis  <- fXv$Yhat
TR  <- viscosity$T_A

run_drt(Yvis ~ TR)

#### Four Sample Problem: Canadian Weather ####
R     <- fda::CanadianWeather$region
XT    <- t(fda::CanadianWeather$dailyAv[,,'Temperature.C'])
fXT   <- refund::fpca.face(XT)
YT    <- fXT$Yhat

run_drt(YT ~ R)

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