I am often disappointed with PCA plots in the scientific literature. Typically PCA plots do not provide a breakdown of the variables and their weights, just something like PCA1 (70% variance explained), PCA2 (10% variance explained). How could one tell which variables are strongly loaded into a component?
Are there PCA visualizations that can provide more insight into the data?
In my humble opinion, it depends on what you want out of the PCA, but that there are two simple plots that are quite common and might be helpful:
To know which variables have high loadings in which principal component, a simple barplot of loadings (as small multiples) will display this pretty clearly.
To look for patterns between samples a scatterplot of scores can sometimes help (e.g. in genetics when you've genotyped a bunch of individuals, a scatterplot of PC1 and PC2 is usually used to look for population patterns).
If you know variable or sample groupings a priori, colour the dots and bars.
ps. I hope it's not bad form to include links, but I've written a small post about these plots and making them in my favourite software. http://martinsbioblogg.wordpress.com/2013/06/26/using-r-two-plots-of-principal-component-analysis/
- Solved – proportion of variance explained in PCA?
- Solved – The sum of squared loadings in PCA summing to 1
- Solved – The unique variance in Factor analysis
- Solved – How to interpret the loadings of the *second* principal component
- Solved – Can PCA explained variance be computed from the components (or from SVD matrices)