Display Spearman Correlation Coefficients and their Significances
The function computes Spearman rank correlation coefficients and places them in the upper triangle of a printed matrix displayed on the current device, the probabilities that the coefficients are not due to chance (Ho: Coefficient = 0) are printed in the lower triangle. The diagonal is filled with NAs to visually split the two triangles.
a matrix of numeric data.
Any less than detection limit values represented by negative values, or zeros or other numeric codes representing blanks in the data, must be removed prior to executing this function, see
Any data vectors (rows) containing
NAs are removed prior to computation.
This function is not recommended for use with closed compositional data sets, i.e. geochemical analyses, unless correlations are sought between a non-compositional variable and individual compositional variables. If it is used with compositional data, it is highly recommended that
ifclr be set to
TRUE to remove the effects of closure and display the ‘true’ inter-element variability. However, different groups of elements, subsets, of a data set will yield different inter-element correlations for the same pair of elements due to the nature of the
clr transform. When carrying out a centred log-ratio transformation it is essential that the data are all in the same measurement units, and by default a reminder/warning is display if the data are centred log-ratio transformed, see
For working with compositional data sets functions
gx.sm are recommended. For visual displays see
Robert G. Garrett
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## Make test data available data(sind.mat2open) ## Compute Spearman correlation coefficients gx.spearman(sind.mat2open) ## Note, unlike gx.pearson there is no example with a log ## transformation. The log transformation is monotonic ## and does not change the ranks ## Compute Spearmann correlation coefficients following ## a centred log-ratio transformation gx.spearman(sind.mat2open, ifclr = TRUE)