covar.plot | R Documentation |

Visualising components of a `dispRity`

object with covar.

```
covar.plot(
data,
n,
points = TRUE,
major.axes = FALSE,
ellipses = FALSE,
level = 0.95,
dimensions = c(1, 2),
centres = colMeans,
scale,
transparent.scale,
add = FALSE,
apply.to.VCV = FALSE,
...
)
```

`data` |
an |

`n` |
optional, a number of random posteriors to use. |

`points` |
logical, whether to plot the observed elements ( |

`major.axes` |
can be either logical for plotting all (or |

`ellipses` |
can be either logical for plotting all (or |

`level` |
the confidence interval level of the major axes and ellipses (default is |

`dimensions` |
which dimensions (default is |

`centres` |
optional, a way to determine ellipses or major axes positions. Can be either a |

`scale` |
optional, the name of a group from |

`transparent.scale` |
optional, if multiple major axes and/or ellipses are plotted, a scaling factor for the transparency. If left empty, the transparency is set to |

`add` |
logical, whether to add the plot to an existing plot ( |

`apply.to.VCV` |
logical, if |

`...` |
any graphical options to be passed to |

When specifying optional arguments with `...`

in a graph with multiple elements (e.g. `points`

, `lines`

, etc...) you can specify which specific element to affect using the syntax `<element>.<argument>`

. For example if you want everything in the plot to be in blue at the exception of the points to be red, you can use `covar.plot(..., col = "blue", points.col = "red")`

.

The arguments `major.axes`

and `ellipses`

can intake a `function`

for summarising the display of multiple variance covariance matrices (if `n`

is missing or greater than one). This can be any central tendency function such as `mean`

, `median`

or `mode.val`

.

The argument `centres`

allows to determine how to calculate the centre of each ellipses or major axes. The argument can be either:

A

`function`

to calculate the centre from a group like the default`colMeans`

function that calculates the centroid coordinates of each group;A

`numeric`

value to be replicated as the coordinates for the centre of each group (e.g.`centres = 0`

sets all the centres at the coordinates`c(0,0,0,...)`

); or a vector of numeric values to be directly used as the coordinates for each group (e.g.`centres = c(1,2,3)`

sets all the centres at the coordinates`c(1,2,3)`

); or a list of numeric values or numeric vectors to be used as the coordinates for the centres of each group;-
`"intercept"`

for using the estimated posterior intercept for each sample.

*NOTE* that if the input contains more dimensions than the visualised dimensions (by default `dimensions = c(1,2)`

) the ellipses and major axes are projected from an n-dimensional space onto a 2D space which might make them look incorrect.
*NOTE* also that the ellipses and major axes are measured independently, when summarising both parameters (e.g. by using `ellipses = mean`

and `major.axes = mean`

), the displayed summarised major axes is not calculated from the summarised ellipse but from the coordinates of all major axes (and therefore might not match the coordinates of the ellipse).

Thomas Guillerme

`MCMCglmm.subsets`

`covar.utilities`

```
data(charadriiformes)
## Creating a dispRity object from the charadriiformes model
covar <- MCMCglmm.subsets(data = charadriiformes$data,
posteriors = charadriiformes$posteriors,
group = MCMCglmm.levels(
charadriiformes$posteriors)[1:4],
rename.groups = c("gulls", "plovers",
"sandpipers", "phylogeny"))
## Default plot
covar.plot(covar)
## Same plot with more options
covar.plot(covar, n = 50, ellipses = mean, major.axes = TRUE,
col = c("orange", "blue", "darkgreen", "grey", "grey"),
legend = TRUE, points = TRUE, points.cex = 0.2,
main = "Charadriiformes shapespace")
```

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