# PCA plot

### Description

Generates a Principle Component plot for data.frames, matrices,
or a pre-made `prcomp`

object.

### Usage

1 2 |

### Arguments

`object` |
data.frame, matrix or |

`pc_x` |
integer, principle component for the plot x dimension. |

`pc_y` |
integer, principle component for the plot y dimension. |

`scale` |
logical, whether to scale to unit variance before PCA. |

`colFactor` |
factor or vector, colour the points by this factor,
default is |

`pchFactor` |
factor or vector, point-type by this factor,
default is |

`palette` |
string, the function to call to create a vector of
contiguous colours with |

`legend` |
logical, whether to display a legend on the plot. |

`...` |
further arguments passed to or from other methods. |

### Details

A data.frame object will be coerced internally to a matrix.
Matrices must be of type `double`

or `integer`

. The
`prcompPlot`

function will then perform a principle component analysis
on the data prior to plotting. The function is call
is `prcomp(t(object), retx=TRUE, center=TRUE, scale.=scale)`

.
Instead of specifying a data.frame or matrix, a pre-made `prcomp`

object
can be given to `prcompPlot`

. In this case, care should be taken in
setting the appropriate value of `scale.`

. If a vector is given to
`colFactor`

or `pchFactor`

, they will be coerced internally to
factors.

For the default `NULL`

values of `colFactor`

and `pchFactor`

,
all colours will be black and circles the point type, respectively.

### Value

None

### See Also

`prcomp`

`rainbow`

### Examples

1 2 3 4 5 6 7 8 | ```
library(HarmanData)
data(IMR90)
expt <- imr90.info$Treatment
batch <- imr90.info$Batch
prcompPlot(imr90.data, colFactor=expt)
pca <- prcomp(t(imr90.data), scale.=TRUE)
prcompPlot(pca, 1, 3, colFactor=batch, pchFactor=expt, palette='topo.colors',
main='IMR90 PCA plot of Dim 1 and 3')
``` |