If negative or zero values exist, data will be shifted to 0 and incremented by
1 before log transformation.
Pearson correlation
(Pearson correlation evaluates the linear relationship between
two continuous variables. For the Pearson r correlation,
both variables should be normally distributed.
Other assumptions include linearity, homoscedasticity, and the absence of outliers.
Linearity assumes a straight line relationship between each of the two variables
and homoscedasticity assumes that data is equally distributed about
the regression line.)
Spearman correlation
(The Spearman correlation coefficient is based on the ranked
values for each variable rather than the raw data. The Spearman rank correlation
test does not carry any assumptions about
the distribution of the data and is the appropriate correlation analysis when
the variables are measured on a scale that
is at least ordinal.)
Kendall correlation
(The Kendall rank coefficient is non-parametric,
as it does not rely on any assumptions on the distributions)