plotPI: Creates Scatterplot Matrices for Probabilistic Indices.

Description

This function creates scatterplot matrices for the different probabilistic indices (PI) $P_{t}$ , $P_{t t^{'}}$ and $P_{t t^{'} t^{″}}$ computed for different variables.

Usage

plotPI(X,g,type="pair",goi=NULL,mc=1,alg="Cnaive",col="black",
           highlight=NULL,hlCol="red",pch=20,zoom=FALSE,order=NULL,...)

Value

A plot of probalistic indices

Arguments

X: Matrix or vector with observations. Each column is a variable, each row an individual.
g: Vector of group labels for observations in X. Has to be the same length as X has observations.
type: Type of probabilistic index, see details.
goi: Groups of Interest, see details.
mc: Set the amount of cores to use for parallel calculation (only available for Linux).
order: Boolean, calculate PI only for natural order or for all combinations.
alg: Internal function, which implementation should be used to calculate the PI.
col: Vector of colors of the scatterplot.
highlight: Vector with positions, which are marked in special color.
hlCol: Color of highlighted spots.
pch: Dot type of the plot.
zoom: Logical, shall the plots be zoomed to interesting areas?
...: Additional plotting arguments.

Author

Daniel Fischer

Details

This function creates the scatterplot matrices for the PI, in case that they haven't been calculated previoulsy. This means that all arguments of the estPI are valid here, since this function is called first and the results will then be passed to the plot function of the estPI object.

See also plot.estPI for further details on the specific plot parameters.

References

Fischer, D., Oja, H., Schleutker, J., Sen, P.K., Wahlfors, T. (2013): Generalized Mann-Whitney Type Tests for Microarray Experiments, Scandinavian Journal of Statistic, to appear.

Fischer, D., Oja, H. (2013): Mann-Whitney Type Tests for Microarray Experiments: The R Package gMWT, submitted article.

Examples

Run this code


X <- c(sample(15))
g <- c(1,1,1,2,2,2,2,3,3,3,4,4,4,4,4)
estPI(X,g,type="single")

X <- matrix(c(rnorm(5000,1.5,2),rnorm(6000,2,2),rnorm(4000,3.5,1)),byrow=TRUE, ncol=10)
colnames(X) <- letters[1:10]
g <- c(rep(1,500),rep(2,600),rep(3,400))

plotPI(X,g,type="single",mc=1)

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