aPCoA {aPCoA} | R Documentation |

Adjusted confounding covariates to show the effect of the primary covariate in a PCoA plot. This method is designed for non-Euclidean distance. This function will plot the original PCoA plot along with the covariate adjusted PCoA plot.

```
aPCoA(formula,data,maincov,drawEllipse=TRUE,drawCenter=TRUE,
pch=19,cex=2,lwd=3,col=NULL,...)
```

`formula` |
A typical formula such as Y~ A, but here Y is a dissimilarity distance. The formula has the same requirements as in adonis function of the vegan package. |

`data` |
A dataset with the rownames the same as the rownames in distance. This dataset should include both the confounding covariate and the primary covariate. |

`maincov` |
the covariate of interest in the dataset, must be a factor |

`drawEllipse` |
Do you want to draw the 95% confidence elipse for each cluster? |

`drawCenter` |
Do you want to show the connection between cluster center (medoid) and cluster members? |

`pch` |
Point shapes |

`cex` |
Number indicating the amount by which plotting text and symbols should be scaled relative to the default. |

`lwd` |
Line width of the ellipses |

`col` |
Color for plot. If not provided by user, will use default distinct colors |

`...` |
Arguments passed to 'dataEllipse'. |

Two PCoA plots. One is the original one, while the other is the PCoA plot after adjusting for the confounding covariate.

`plotMatrix` |
The matrix for plotting the adjusted PCoA plot. |

Yushu Shi, Liangliang Zhang, Kim-Anh Do, Christine Peterson and Robert Jenq. (2020) aPCoA: Covariate Adjusted Principal Coordinates Analysis <arXiv:2003.09544>

```
library(vegan)
library(aPCoA)
data("Tasmania")
data<-data.frame(treatment=Tasmania$treatment,block=Tasmania$block)
bray<-vegdist(Tasmania$abund, method="bray")
rownames(data)<-rownames(as.matrix(bray))
opar<-par(mfrow=c(1,2),
mar=c(3.1, 3.1, 3.1, 5.1),
mgp=c(2, 0.5, 0),
oma=c(0, 0, 0, 4))
result<-aPCoA(bray~block,data,treatment)
par(opar)
```

[Package *aPCoA* version 1.3 Index]