function [pM,Mp] = predobs(fitps,Mc,dbtype);

% function [pM,Mp] = predobs(fitps,Mc,dbtype);
% pM = predicted confusion probabilities
% Mp = observed confusion probabilities
% fitps = output from grtfit
% Mc = observed confusion frequencies
% dbtype = decision bound type; linear, piecewise, or maxpost

[nstim,nresp,nbr] = size(Mc);

m = [fitps(1).maa fitps(1).mab fitps(1).mba fitps(1).mbb];
c = zeros(2,2,4);
c(:,:,1) = fitps(1).caa;
c(:,:,2) = fitps(1).cab;
c(:,:,3) = fitps(1).cba;
c(:,:,4) = fitps(1).cbb;
if nbr==5
    if strcmp(dbtype,'maxpost')
        dbp(1,:) = [fitps.c1aa fitps.c1ab fitps.c1ba fitps.c1bb];
        dbp(2,:) = [fitps.c2aa fitps.c2ab fitps.c2ba fitps.c2bb];
        dbp(3,:) = [fitps.c3aa fitps.c3ab fitps.c3ba fitps.c3bb];
        dbp(4,:) = [fitps.c4aa fitps.c4ab fitps.c4ba fitps.c4bb];
        dbp(5,:) = [fitps.c5aa fitps.c5ab fitps.c5ba fitps.c5bb];
    else
        dbp(1,:) = [fitps.ax fitps.bx fitps.cx1 fitps.dx1 fitps.sx];
        dbp(2,:) = [fitps.ay fitps.by fitps.cy2 fitps.dy2 fitps.sy];
        dbp(3,:) = [fitps.ax fitps.bx fitps.cx3 fitps.dx3 fitps.sx];
        dbp(4,:) = [fitps.ay fitps.by fitps.cy4 fitps.dy4 fitps.sy];
        dbp(5,:) = [fitps.ax fitps.bx fitps.cx5 fitps.dx5 fitps.sx];
        dbp(6,:) = [fitps.ay fitps.by fitps.cy3 fitps.dy3 fitps.sy];
    end
elseif nbr==1
    if strcmp(dbtype,'maxpost')
        dbp(1,:) = [fitps.waa fitps.wab fitps.wba fitps.wbb];
    else
        dbp(1,:) = [0 0 fitps.cx fitps.dx 0];
        dbp(2,:) = [0 0 fitps.cy fitps.dy 0];
    end
end

% bin widths
bin = .05;

[dist,x,y] = bivar_norm(m,c,bin);

dec = logical(dec_reg(dbtype,dist,x,y,dbp,nbr));

pM = zeros(nstim,nresp,nbr);

binsize = bin^2;

for id = 1:nbr % base rate condition
    for s = 1:nstim % stimulus
        pdf = dist(:,:,s);
        for r = 1:nresp % response
            pM(s,r,id) = max(binsize*sum( sum( pdf( dec(:,:,r,id) ) ) ),eps);
        end
    end
end

for id = 1:nbr
    for s = 1:nstim
        pM(s,:,id) = pM(s,:,id)/sum(pM(s,:,id));
    end
end

Mp = zeros(nstim,nresp,nbr);
for ci = 1:nbr
    for si = 1:nstim
        Mp(si,:,ci) = Mc(si,:,ci)/sum(Mc(si,:,ci));
    end
end

pp = reshape(pM,nstim*nresp*nbr,1);
op = reshape(Mp,nstim*nresp*nbr,1);

%hfig = figure;
hold on
hax = gca;
plot(op,pp,'k.','LineWidth',2)
set(hax,'XLim',[0 1],'YLim',[0 1],'XTick',[0 1],'YTick',[0 1])
axis square
% xlabel('obs','FontSize',12)
% ylabel('pr','FontSize',12)
%xlabel('observed probability','FontSize',14)
%ylabel('predicted probability','FontSize',14)
plot([0 1],[0 1],'k:')
%text(.02,.96,label,'FontName','Times','FontSize',16);