Hi Niko,
The fitglm function in MATLAB is designed to work with univariate response variables. For multivariate response variable (like a time series), you would typically need to fit a separate model for each time point, as done in the loop.
One way to achieve faster computations is to use parallel computing tools provided by MATLAB, such as parfor instead of for. This allows you to perform the iterations in parallel, which can significantly speed up the computation if you have a multi-core processor.
Here is how the code can be modified:
if isempty(gcp('nocreate'))
parpool
end
tic
coeffs = zeros(2,npoints);
parfor i = 1:npoints
tbl = table(cond, data_all(:,i));
mdl = fitglm(tbl,modelspec,'Distribution','normal');
coeffs(:,i) = mdl.Coefficients.Estimate;
end
toc
Please note that we need to start a parallel pool using parpool before using parfor. Also, the Parallel Computing Toolbox is required for this.
Even though this approach speeds up the computation, it is still fitting a separate GLM for each time point. For large number of time points, consider a different modeling approach that can handle multivariate time series data directly. However, this would likely involve moving away from the GLM framework.
Hope this helps!
