Hi Josh,

The continuous regressor is a linear combination of all other regressors. Note that the repeated values every two rows match the other rows. That final column can be produced by some (linear) combination of all others. You can safely remove that continuous regressor from your model, or model the data in a different manner to avoid the issue.

All the best,

Anderson

On Mon, 27 Jan 2020 at 17:17, Joshua Cain <[log in to unmask]> wrote:

Mathew, 

Thankyou for your response. Can I ask, what aspect of the design is it collinear with? The subject codes? Is there a way around this?

-Josh

On Mon, Jan 27, 2020 at 5:01 AM Matthew Webster <[log in to unmask]> wrote:

The covariate regressor is co-linear with the rest of the design and so cannot account for any extra variance.

Kind Regards

Matthew

--------------------------------
Dr Matthew Webster
FMRIB Centre 
John Radcliffe Hospital
University of Oxford

On 27 Jan 2020, at 04:13, Josh <[log in to unmask]> wrote:

I am doing a group-wide averaging in FEAT.  The only complexity is that I have regressed the data on subject of origin (2 data sets per individual).

This works fine. However, when adding a covariate regressor, FEAT is crashing (seemingly due to linear combinations found in my design). Am I setting up the design wrong or is my covariate regressor inherently confounded somehow?

Design Below::

See attached efficiency matrix.

Covariate has been mean-normalized.
Design:

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3258
1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3258
1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1.5604
1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1.5604
1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 -1.6807
1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 -1.6807
1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 -0.7545
1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 -0.7545
1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1.2794
1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1.2794
1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0.3258
1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0.3258
1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 -0.7545
1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 -0.7545
1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0.2063
1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0.2063
1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 -0.3112
1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 -0.3112
1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 -1.9168
1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 -1.9168
1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 -0.7545
1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 -0.7545
1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0.2063
1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0.2063
1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0.3258
1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0.3258
1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0.9822
1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0.9822
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1.5604
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1.5604
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.6002
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.6002

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