aPCoA {aPCoA} R Documentation

## Covariate Adjusted PCoA Plot

### Description

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.

### Usage

```aPCoA(formula,data,maincov,drawEllipse=TRUE,drawCenter=TRUE)
```

### Arguments

 `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?

### Value

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.

### References

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

### Examples

```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 1.2 Index]