setupfitting_GLM.m

function [prs0,Xstruct] = setupfitting_GLM(gg, Stim)
%  [prs0,Xstruct] = setupfitting_GLM(gg, Stim)
%
%  Set initial parameters and build design matrix structure for GLM fitting
%
%  Inputs: 
%     gg = glm param structure
%     Stim = stimulus (time along columns, other dims along rows)
%     maxsize = maximum # floats to store in design matrix 
%                     (Affects # of chunks used to compute likelihood)
%  Output: 
%     prs0 = initial parameters extracted from gg
%     Xstruct = struct with design matrix for stim and spike-history terms


% Initialize optimization param structure

% ---- Create struct and make stimulus design matrix ---------------------
if strcmp(gg.ktype, 'linear') % standard GLM
    Xstruct = initfit_stimDesignMat(gg,Stim);  % create design matrix structure
    
    % extract parameter vector
    prs0 = [gg.kt(:); gg.dc; gg.ihw(:); gg.ihw2(:)];

elseif strcmp(gg.ktype, 'bilinear') % bilinearly-parametrized stim filter
    Xstruct = initfit_stimDesignMat_bi(gg,Stim); % create design matrix structure

    % extract parameter vector    
    prs0 = [gg.kt(:); gg.kx(:); gg.dc; gg.ihw(:); gg.ihw2(:)];

else
    error('unknown filter type (allowed types are ''linear'' or ''bilinear'')');
end

% ---- Make spike-history design matrix -----------------------------------
Xstruct = initfit_sphistDesignMat(gg,Xstruct); 

% set nonlinearity
Xstruct.nlfun = gg.nlfun;  

% compute mask (time bins to use for likelihood)
Xstruct.bmask = initfit_mask(gg.mask,Xstruct.dtSp,Xstruct.rlen);