Description Usage Arguments Note Author(s) See Also Examples

The function computes Pearson product moment 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.

1 | ```
gx.pearson(xx, log = FALSE, ifclr = FALSE, ifwarn = TRUE)
``` |

`xx` |
a matrix of numeric data. |

`log` |
if |

`ifclr` |
if |

`ifwarn` |
by default |

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 `ltdl.fix.df`

.

Any data vectors (rows) containing `NA`

s 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 `ifwarn`

above.

For working with compositional data sets functions `gx.symm.coords.r`

, `gx.vm`

and `gx.sm`

are recommended. For visual displays see `gx.pairs4parts`

and `gx.plot2parts`

.

When a centred log-ratio transformation is undertaken the `log`

‘switch’ is ignored.

Robert G. Garrett

`ltdl.fix.df`

, `remove.na`

, `clr`

, `sind.mat2open`

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
## Make test data available
data(sind.mat2open)
## Compute Pearson correlation coefficients
gx.pearson(sind.mat2open)
## Compute Pearson correlation coefficients following
## a logarithmic transformation
gx.pearson(sind.mat2open, log = TRUE)
## Compute Pearson correlation coefficients following
## a centred log-ratio transformation
gx.pearson(sind.mat2open, ifclr = TRUE)
``` |

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