kendallSeasonalTrendTest function

Kendall seasonal trend test

Nonparametric test for monotonic trend Within each season based on Kendall's Tau statistic

kendallSeasonalTrendTest(y, ...)

## Default S3 method:
kendallSeasonalTrendTest(
  y,
  season,
  year,
  alternative = "two.sided",
  correct = TRUE,
  ci.slope = TRUE,
  conf.level = 0.95,
  independent.obs = TRUE,
  data.name = NULL,
  season.name = NULL,
  year.name = NULL,
  parent.of.data = NULL,
  subset.expression = NULL,
  ...
)

## S3 method for class 'data.frame'
kendallSeasonalTrendTest(y, ...)

## S3 method for class 'formula'
kendallSeasonalTrendTest(y, data = NULL, subset, na.action = na.pass, ...)

## S3 method for class 'matrix'
kendallSeasonalTrendTest(y, ...)

Arguments

• y: an object containing data for the trend test. In the default method, the argument y must be numeric vector of observations. When y is a data frame, all columns must be numeric. When y is a matrix, it must be a numeric matrix. In the formula method, y must be a formula of the form y ~ season + year, where y, season, and year specify what variables to use for the these arguments in the call to kendallSeasonalTrendTest.default. Missing (NA), undefined (NaN), and infinite (Inf, -Inf) values are allowed but will be removed.
• ...: methods passed to or from other methods
• season: numeric or character vector or a factor indicating the seasons in which the observations in y were taken. The length of season must equal the length of y.
• year: numeric vector indicating the years in which the observations in y were taken. The length of year must equal the length of y.
• alternative: character string indicating the kind of alternative hypothesis. The possible values are "two.sided" (tau not equal to 0; the default), "less" (tau less than 0), and "greater" (tau greater than 0).
• correct: logical scalar indicating whether to use the correction for continuity in computing the z-statistic that is based on the test statistic S'. The default value is TRUE.
• ci.slope: logical scalar indicating whether to compute a confidence interval for the slope. The default value is TRUE.
• conf.level: numeric scalar between 0 and 1 indicating the confidence level associated with the confidence interval for the slope. The default value is 0.95.
• independent.obs: logical scalar indicating whether to assume the observations in y are seially independent. The default value is TRUE.
• data.name: character string indicating the name of the data used for the trend test. The default value is deparse(substitute(y)).
• season.name: character string indicating the name of the data used for the season. The default value is deparse(substitute(season)).
• year.name: character string indicating the name of the data used for the year. The default value is deparse(substitute(year)).
• parent.of.data: character string indicating the source of the data used for the trend test.
• subset.expression: character string indicating the expression used to subset the data.
• data: specifies an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which kendallTrendTest is called.
• subset: specifies an optional vector specifying a subset of observations to be used.
• na.action: specifies a function which indicates what should happen when the data contain NAs. The default is na.pass.

Returns

A list object with elements for results of the test

Details

Perform a nonparametric test for a monotonic trend within each season based on Kendall's tau statistic, and optionally compute a confidence interval for the slope across all seasons.

Examples

kendallSeasonalTrendTest(res ~ month + year, tidfitmean)

References

Hirsch, R.M., Slack, J.R., Smith, R.A. 1982. Techniques of trend analysis for monthly water quality data. Water Resources Research, 18:107-121.

Millard, S. P. 2013. EnvStats: An R Package for Environmental Statistics. Springer, New York.