pyamr.core.stats.correlation pyamr.core.stats.correlation ============================ ============================

Classes

class pyamr.core.stats.correlation.CorrelationWrapper(estimator=None, evaluate=True)

The Pearson Correlation Coefficient measures the linear correlation between two variables with a value within the range [-1,1]. Coefficient values of -1, 0 and 1 indicate total negative linear correlation, no inear correlation and total positive correlation respectively. In this study, the coefficient is used to assess whether or not there is a linear correlation between the number of observations (susceptibility test records) and the computed resistance index.

The Spearman Correlation Coefficient…

The Cross-Correlation ….

Methods:

as_summary([alpha])	This method displays the summary.
evaluate([alpha])	This method sets all the variables into this class.
fit(x1, x2, **kwargs)	This method computes kendall for monotonic increase

as_summary(alpha=0.05)

This method displays the summary.

evaluate(alpha=0.05)

This method sets all the variables into this class.

fit(x1, x2, **kwargs)

This method computes kendall for monotonic increase

Note

the pvalues produced by scipy are not reliable if less than 500 observations.

Parameters:

• x1 (np.array) – Variable x1

• y2 (np.array) – Variable y2

Returns:

object

Return type:

A CorrelationWrapper objects.

Examples using pyamr.core.stats.correlation.CorrelationWrapper

Step 03 - Time Series Analysis

Step 03 - Time Series Analysis

Correlation

Correlation