Inner matrix dimensions must agree but how when adding four or more predictor variables?

I'm trying to see how joint angles of the right leg (predictor variables, X = x1, x2, x3 for the hip, knee, and ankle angles respectively each a [1 x 20] array) could potentially stablise the position of the right foot (outcome variables Y = x,y coordinate positions: both a [1 x 20] array) using multiple linear regression.

"dev" (a [1 x 20] array for each predictor variable) is the deviations of joint angles from the mean joint angle configuration at each trial and projected onto the null-space or null(J)(Z = null(J)) using the following code:

for i = 1:N

UCM(:,i) = (Z'*dev(:,i))*Z;

end

The UCM is used to look at the control of a movement and is approximated linearly using the null space (Z) of the J matrix.

This code works for one frame (i.e. a [1 x 20] array) and a whole normalised movement cycle (i.e. a [101 x 20] array) for 3 predictor varibles (PV's) and 2 output variables (OV's) from a [3 x 1] Z array.

However when I increase the number of PV's to 4 (resulting in a [4 x 2] Z array) and 5 (resulting in a [5 x 2] Z array) with 2OV's, I get the following warning:

???Error using mm>mtimes

Inner matrix dimensions must agree

Therefore, i'm unable to analyse any data above three PV's.

Any help would be appreciated.

Answer 1

[Matt J]

UCM=Z*(Z'*dev);

[Tim Bennett]

Thank you for your help. You've solved the problem.

Answer 2

[Matt J]

You're passing data to 'mm' that is incompatibly sized for matrix multiplication.

Use DBSTOP IF ERROR to pause program execution at the point where this occurs. Then inspect the sizes of the offending variables.