Help with PCA analysis

I have a set of measurements of wrist movements. It contains data about the movements in x,y,z translations and x,y,z rotations - so it has 6 dimensions.

I used:

[coeff,score,~,~,explained,~] = pca(E);

Explained gives me the following result:

[56,0982092469235;
26,0254466244066;
11,3132049655197;
5,01572264188474;
0,929970860888532;
0,617445660376905]

From this I see that 80% of the data is in the first two principle components.

I assume that these 2 principle components form a plane. How do I use coeff to see how this plane is orientated?

Thanks in advance for helping me out!

[the cyclist]

When you say "see how this plane is orientated", do you literally mean a visualization? It's a challenge to show a (2-D) plane embedded in a 6-D space.

[Elise]

Good question, visualizing would be preferable, but I can see that that would be difficult.

I quess what I want to know is how much of each original component can be found in the first two principal components. For this case: how much translation v.s. rotation can be found in the first to principle components.

I know this should be found in coeff/score, I just can not figure out how to interpret these.