runPCA: Perform an Principal Component Analysis
In EMA: Easy Microarray Data Analysis

Description Usage Arguments Value Author(s) See Also Examples

This function performs a Principal Component Analysis (PCA) and represents the samples or the variables of the analysis.

runPCA(X, ncp=5, scale=TRUE, ind.sup=NULL, quanti.sup=NULL,quali.sup=NULL,
sample.qual=TRUE, variable.qual=FALSE, sample.cont=TRUE,variable.cont=FALSE,
plotSample=TRUE, plotVariable=FALSE, plotInertia = TRUE, plotBiplot=FALSE,
lab.sample="quality", lab.var=NULL,palette="rainbow",
lim.cos2.sample=0, lim.cos2.var=0, pdf=FALSE, pdfname= NULL, verbose=FALSE, ...)

`X`	a data frame with n rows (samples) and p columns (variables)
`ncp`	number of dimensions kept in the results (by default 5)
`scale`	a boolean, if TRUE (value set by default) then data are scaled to unit variance
`ind.sup`	a vector indicating the indexes of the supplementary individuals
`quanti.sup`	a vector indicating the indexes of the quantitative supplementary variables
`quali.sup`	a vector indicating the indexes of the qualitative supplementary variables
`sample.qual`	a boolean, if TRUE quality sample is displayed, by default = TRUE
`variable.qual`	a boolean, if TRUE quality variable is displayed, by default = FALSE
`sample.cont`	a boolean, if TRUE sample contribution is displayed, by default = TRUE
`variable.cont`	a boolean, if TRUE variable contribution is displayed, by default = FALSE
`plotSample`	a boolean, if TRUE samples are displayed, by default = TRUE
`plotVariable`	a boolean, if TRUE variables are displayed, by default = FALSE
`plotInertia`	a boolean, if TRUE inertia percentage of components is displayed, by default = TRUE
`plotBiplot`	a boolean, if TRUE biplot is displayed, by default = FALSE
`lab.sample`	a vector, sample representation is colored by label.sample, by default = NULL
`lab.var`	a vector, variable representation is colored by label.var, by default = "quality"
`palette`	character, name of palette color, by default = "rainbow"
`lim.cos2.sample`	a numeric, for graphics, keep samples with cos2 >= lim.cos2.sample, by default = 0
`lim.cos2.var`	a numeric, for graphics, keep variables with cos2 >= lim.cos2.var, by default = 0
`pdf`	a boolean, if TRUE save all the graphics in a pdf file, by default = FALSE
`pdfname`	pdf file name for saving graphics
`verbose`	print results if verbose = TRUE, by default = FALSE
`...`	Arguments to be passed to methods, such as graphical parameters (see 'par').

`eig`	a matrix containing all the eigenvalues, the percentage of variance and the cumulative percentage of variance
`var`	a list of matrices containing all the results for the active variables (coordinates, correlation between variables and axes, square cosine, contributions
`ind`	a list of matrices containing all the results for the active individuals (coordinates, square cosine, contributions)

Returns the individuals factor map for axes 1 and 2, 1 and 3, 2 and 3 Returns the inertia percentage of components By default, print sample coordinates, sample quality and sample contribution

Nicolas Servant, Eleonore Gravier, Pierre Gestraud, Cecile Laurent, Caroline Paccard, Anne Biton, Jonas Mandel, Bernard Asselain, Emmanuel Barillot, Philippe Hupe

plotSample, plotVariable, plotInertia

data("marty")

## PCA on sample with inertia plot and sample plot colored by tumour type

example.subset <- marty[1:100,]
## Not run: 
pca <- runPCA(t(example.subset), verbose = FALSE, lab.sample = marty.type.cl)

## End(Not run)