ComparePcaPlots: Compare PCA Plots
In NormalizeMets: Analysis of Metabolomics Data

Description Usage Arguments Author(s) Examples

Produces a comparison of principal component multiplots

ComparePcaPlots(lfeaturedata, lgroupdata, saveplot = FALSE, plotname = "",
  savetype = c("png", "bmp", "jpeg", "tiff", "pdf"), y.axis = 1,
  x.axis = 2, center = TRUE, scale = TRUE, lmain = NULL, n = 3,
  showlegend = TRUE, usercols = NULL, cex_val = 0.7, ...)

`lfeaturedata`	A list of data frames in the featuredata format. This is a dataframe with metabolites in columns and samples in rows. Unique sample names should be provided as row names. See NormalizeMets Vignette for details.
`lgroupdata`	A list of data frames or a vectors with group names.
`saveplot`	A logical indication whether to save the plot produced.
`plotname`	Name of the output file if the file is to be saved. This is the general name for all the graphs and the specific type prefix will be added automatically.
`savetype`	The required format for the plot to be saved in. Threre is a choice of `"png","bmp","jpeg","tiff","pdf"` type files.
`y.axis`	The principal component to be plotted on the y-axis.
`x.axis`	The principal component to be plotted on the x-axis.
`center`	A logical indicating whether the variables should be scaled to have zero mean.
`scale`	A logical indicating whether the variables should be scaled to have unit variance before the analysis takes place.
`lmain`	A list of plot titles
`n`	The number of principal components to be plotted. The default value is set to 3.
`showlegend`	A logical indication whether to print a legend for the plot.
`usercols`	A character string with colours to be used or TRUE for ColList to be used (ColList is the defualt set of colours used in other plots in this package). A value other then `NULL` will automatically set `showlegend` to `FALSE`.
`cex_val`	A numeric indicating the size of some text elements.
`...`	Arguments to be passed on to other methods.

Alysha M De Livera, Gavriel Olshansky.

1
2
3

    data(mixdata)
#     ComparePcaPlots(list(mixdata$featuredata,mixdata$featuredata*1.2),
#             list(mixdata$sampledata[,3],mixdata$sampledata[,3]))