# pcaplot: Plot Function for PCA with Grouped Values In mt: Metabolomics Data Analysis Toolbox

## Description

Plot function for PCA with grouped values.

## Usage

 ```1 2 3 4 5``` ```pcaplot(x, y, scale = TRUE, pcs = 1:2, ...) pca.plot(x, y, scale=TRUE, abbrev = FALSE, ep.plot=FALSE,...) pca.comp(x, scale=FALSE, pcs=1:2,...) ```

## Arguments

 `x` A matrix or data frame to be plotted. `y` A factor or vector giving group information of columns of `x`. `scale` A logical value indicating whether the data set `x` should be scaled. `pcs` A vector of index of PCs to be plotted. `ep.plot` A logical value indicating whether the ellipse should be plotted. `abbrev` Whether the group labels are abbreviated on the plots. If `abbrev > 0` this gives `minlength` in the call to `abbreviate`. `...` Further arguments to `prcomp` or `lattice`. See corresponding entry in `xyplot` for non-trivial details of `lattice`. For `pcaplot`, one argument is `ep`: an integer for plotting ellipse. `1` and `2` for plotting overall and group ellipse, respectively. Otherwise, none. For details, see `panel.elli.1`.

## Value

`pcaplot` returns an object of class `"trellis"`.

`pca.comp` returns a list with components:

 `scores` PCA scores `vars` Proportion of variance `varsn` A vector of string indicating the percentage of variance.

## Note

Number of columns of `x` must be larger than 1. `pcaplot` uses `lattice` to plot PCA while `pca.plot` uses the basic graphics to do so. `pca.plot` plots PC1 and PC2 only.

## Author(s)

Wanchang Lin

`grpplot`, `panel.elli.1`, `pca.plot.wrap`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24``` ```## examples of 'pcaplot' data(iris) pcaplot(iris[,1:4], iris[,5],pcs=c(2,1),ep=2) ## change confidence interval (see 'panel.elli.1') pcaplot(iris[,1:4], iris[,5],pcs=c(1,2),ep=2, conf.level = 0.9) pcaplot(iris[,1:4], iris[,5],pcs=c(2,1),ep=1, auto.key=list(space="top", columns=3)) pcaplot(iris[,1:4], iris[,5],pcs=c(1,3,4)) tmp <- pcaplot(iris[,1:4], iris[,5],pcs=1:3,ep=2) tmp ## change symbol's color, type and size pcaplot(iris[,1:4], iris[,5],pcs=c(2,1),main="IRIS DATA", cex=1.2, auto.key=list(space="right", col=c("black","blue","red"), cex=1.2), par.settings = list(superpose.symbol = list(col=c("black","blue","red"), pch=c(1:3)))) ## compare pcaplot and pca.plot. pcaplot(iris[,1:4], iris[,5],pcs=c(1,2),ep=2) pca.plot(iris[,1:4], iris[,5], ep.plot = TRUE) ## an example of 'pca.comp' pca.comp(iris[,1:4], scale = TRUE, pcs=1:3) ```