showPCA: Principal Component Plots for JIVE Decomposition

Description

Display principal component plots of the joint and individual structure of a data set after a joint and individual variation explained (JIVE) decomposition.

Usage

showPCA(result, n_joint = 0, n_indiv = rep(0, length(result$data)), 
         Colors = "black", pch=1)

Arguments

result

An object of class "jive".

n_joint

The number of joint components to plot.

n_indiv

The vector of the number of individual components to plot for each data set.

Colors

The colors of the data points in the plot. Can be a vector specifying a different color for each sample.

pch

Character to use for plotting. Can be a vector specifying a different character for each sample.

Details

This shows the patterns in the column space that maximize variability of joint or individual structure, analogous to principal components. A multi-panel figure with aligned scatterplots for each pair of principal components, across both joint and individual structure, will be generated. Plotted points correspond to shared columns (e.g., samples).

References

Lock, E. F., Hoadley, K. A., Marron, J. S., & Nobel, A. B. (2013). Joint and individual variation explained (JIVE) for integrated analysis of multiple data types. The Annals of Applied Statistics, 7(1), 523-542.

Examples

Run this code

# NOT RUN {
##Load JIVE results (using default settings) for simulated data 
##as in Section 2.4 of Lock et al., 2013,
##with rank 1 joint structure, and rank 1 individual structure for each dataset
data(SimResults) 
# Visualize results
# Plot the three components, 1 joint and 1 individual from each source
showPCA(Results,1,c(1,1))
###This displays three scatterplots: 
#the first joint principal component vs. the first principal component individual to source 1,
#the first joint component vs.  the first component individual to source 2, and 
#the first component individual to source 1 vs. the first component individual to source 2.
# }