What does a biplot tell you?

Biplots are a type of exploratory graph used in statistics, a generalization of the simple two-variable scatterplot. A biplot allows information on both samples and variables of a data matrix to be displayed graphically. A generalised biplot displays information on both continuous and categorical variables.

How do you explain PCA biplot?

The biplot is a very popular way for visualization of results from PCA, as it combines both the principal component scores and the loading vectors in a single biplot display. The plot shows the observations as points in the plane formed by two principal components (synthetic variables).

What do arrows mean in PCA?

Each variable that went into the PCA has an associated arrow. Arrows for each variable point in the direction of increasing values of that variable. If you look at the ‘Rating’ arrow, it points towards low values of PC1 – so we know the lower the value of PC1, the higher the Drinker Rating.

How do you describe a biplot?

A biplot overlays a score plot and a loadings plot in a single graph. An example is shown at the right. Points are the projected observations; vectors are the projected variables….The four types of biplots

1. When c=0, the vectors are represented faithfully.
2. When c=1, the observations are represented faithfully.

Which is the first arrow in biplot in R?

However, reading into the code of biplot in R. The line about the arrows is: Where y is the actually the loadings matrix, which is the eigenvector matrix. So it looks like the 1st arrow is actually pointing from (0, 0) to (y [1, 1], y [1, 2]).

Which is the loading matrix in biplot in R?

However, reading into the code of biplot in R. The line about the arrows is: Where y is the actually the loadings matrix, which is the eigenvector matrix. So it looks like the 1st arrow is actually pointing from (0, 0) to (y [1, 1], y [1, 2]). I understand that we are trying to plot a high dimensional arrow onto a 2D plane.

How to reproduce the arrows in PCA biplot?

The arrows can be reproduced as the correlation of the original variables with the scores generated by the first two principal components. or even yet… connecting with the geometric explanation of loadings by @ttnphns, or this other informative post also by @ttnphns.

Which is better ggplot or your biplot for PCA?

Produces a ggplot2 variant of a so-called biplot for PCA (principal component analysis), but is more flexible and more appealing than the base R biplot () function.