pyamr.core.stats.kendall
Functions
- pyamr.core.stats.kendall.kendall(x)[source]
Computes the kendall statistical test
- Parameters:
x (a vector of data)
alpha (significance level (0.05 default))
- Returns:
trend (tells the trend (increasing, decreasing or no trend))
h (True (if trend is present) or False (if trend is absence))
p (p value of the significance test)
z (normalized test statistics)
Examples using pyamr.core.stats.kendall.kendall
Classes
- class pyamr.core.stats.kendall.KendallWrapper(estimator=None, evaluate=True)[source]
The Kendall statistical test, also known as Kendall’s rank correlation test or Kendall’s tau test, is a nonparametric statistical test used to assess the strength and direction of association between two variables. It is particularly suited for analyzing ranked or ordinal data, where the values of the variables are ranked or ordered but not necessarily quantitatively measurable. The coefficient ranges from -1 to +1, where a value of +1 indicates a perfect positive rank correlation, -1 indicates a perfect negative rank correlation, and 0 indicates no rank correlation.
Methods:
as_summary([alpha])This method displays the summary.
evaluate([alpha])Evaluates the model for the specified alpha.
trend_direction(alpha)This method returns the trend direction.
trend_exists(alpha)This method returns a boolean with the stationarity outocme.