biplot diagram of PCA analysis differs from plot of variables
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@juditsessler-8275
Last seen 7.3 years ago
Hungary

I made a PCA analysis of mz 94x20 matrix:

pca_<-dudi.pca(df = rg, scannf = F, nf = 2)

and displayed the result with scatter(pca_, posieig = "bottomright").

I wanted to see only the variables, so I wrote

plot(pca_$co) text(pca_$co,row.names(pca_$co)) The variables were grouped differently on the first plot then on the second. Why? r pca biplot • 1.6k views ADD COMMENT 2 Entering edit mode @james-w-macdonald-5106 Last seen 5 hours ago United States This question is only tangential to Bioconductor, as the made4 package uses the CRAN ade4 package internally. So one could argue that it is better asked on R-help. That said, you need to read and understand the help page for the functions you are using. In the help page for dudi.pca() it says: Value: Returns a list of classes ‘pca’ and ‘dudi’ (see dudi) containing the used information for computing the principal component analysis : tab: the data frame to be analyzed depending of the transformation arguments (center and scale) cw: the column weights lw: the row weights eig: the eigenvalues rank: the rank of the analyzed matrice nf: the number of kept factors c1: the column normed scores i.e. the principal axes l1: the row normed scores co: the column coordinates li: the row coordinates i.e. the principal components So if you use scatterplot(), you get a PCA plot. If you directly plot pca_$co, you do not because you aren't plotting the principal components.