MEMOIR/logL_SCTL.m at main · ICB-DCM/MEMOIR · GitHub

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function [P,logL_sc,dlogL_scdxi] = logL_SCTL(xi, model, data, s, options, P)

persistent fp
persistent fl


%% Construct fixed effects and covariance matrix
beta = model.beta(xi);
delta = model.delta(xi);

[D,~,~,~,~,~] = xi2D(delta,options.type_D);

% Initialization measurements
Sim_SCTL.Y = NaN(size(data.SCTL.Y));
% events
if(~isfield(data.SCTL,'T'))
    data.SCTL.T = zeros(0,1,size(data.SCTL.Y,3));
end
Sim_SCTL.T = NaN(size(data.SCTL.T));
Sim_SCTL.R = NaN(size(data.SCTL.T));

% set default scaling
if(~isfield(model,'SCTLscale'))
    model.SCTLscale = 1;
end

% Loop: Indiviudal cells

logLi_D = zeros(size(data.SCTL.Y,3),1);
logLi_T = zeros(size(data.SCTL.Y,3),1);
logLi_b = zeros(size(data.SCTL.Y,3),1);
logLi_I = zeros(size(data.SCTL.Y,3),1);
logL_sc = zeros(size(data.SCTL.Y,3),1);

if options.nderiv >= 1
    dlogL_scdxi = zeros(size(data.SCTL.Y,3),length(xi));
end

tmp = arrayfun(@(x) any(~isnan(data.SCTL.Y(:,:,x)),2),1:size(data.SCTL.Y,3),'UniformOutput',false);
data.SCTL.ind_y = false(size(data.SCTL.Y,1),size(data.SCTL.Y,3));
data.SCTL.ind_y(:,:) = [tmp{:}];
tmp = arrayfun(@(x) any(~isnan(data.SCTL.T(:,:,x)),2),1:size(data.SCTL.T,3),'UniformOutput',false);
data.SCTL.ind_t = false(size(data.SCTL.T,1),size(data.SCTL.T,3));
data.SCTL.ind_t(:,:) = [tmp{:}];

Sim_SCTL_Y = zeros(size(data.SCTL.Y));
Sim_SCTL_SIGMAY = zeros(size(data.SCTL.Y));
Sim_SCTL_T = zeros(size(data.SCTL.T));
Sim_SCTL_SIGMAT = zeros(size(data.SCTL.T));
Sim_SCTL_R = zeros(size(data.SCTL.T));
% check wether there is a parallel pool available
try
    p = gcp('nocreate');
catch
    p = [];
end
if isempty(p)
    for i = 1:size(data.SCTL.Y,3)
        YY = zeros(size(data.SCTL.Y(:,:,i)));
        SY = zeros(size(data.SCTL.Y(:,:,i)));
        TT = zeros(size(data.SCTL.T(:,:,i)));
        ST = zeros(size(data.SCTL.T(:,:,i)));
        RR = zeros(size(data.SCTL.T(:,:,i)));
        [ logL, bhat, Sim ] = logL_SCTL_si(xi, model, data, s, options, P, i);
        logLi_D(i,1) = logL.D;
        logLi_T(i,1) = logL.T;
        logLi_b(i,1) = logL.b;
        logL_sc(i,1) = logL.val;
        if(options.integration)
            logLi_I(i,1) = logL.I;
        end
        b(:,i) = bhat.val;
        if(options.nderiv>0)
            dbdxi(i,:,:) = bhat.dxi;
            dlogL_scdxi(i,:) = logL.dxi;
        end
        YY(data.SCTL.ind_y(:,i),:) = reshape(Sim.SCTL_Y,size(YY(data.SCTL.ind_y(:,i),:)));
        SY(data.SCTL.ind_y(:,i),:) = reshape(Sim.SCTL_Sigma_Y,size(SY(data.SCTL.ind_y(:,i),:)));
        TT(data.SCTL.ind_t(:,i),:) = reshape(Sim.SCTL_T,size(TT(data.SCTL.ind_t(:,i),:)));
        ST(data.SCTL.ind_t(:,i),:) = reshape(Sim.SCTL_Sigma_T,size(ST(data.SCTL.ind_t(:,i),:)));
        RR(data.SCTL.ind_t(:,i),:) = reshape(Sim.SCTL_R,size(RR(data.SCTL.ind_t(:,i),:)));
        Sim_SCTL_Y(:,:,i) = YY;
        Sim_SCTL_SIGMAY(:,:,i) = SY;
        Sim_SCTL_T(:,:,i) = TT;
        Sim_SCTL_SIGMAT(:,:,i) = ST;
        Sim_SCTL_R(:,:,i) = RR;
    end
else
    parfor i = 1:size(data.SCTL.Y,3)
        YY = zeros(size(data.SCTL.Y(:,:,i)));
        SY = zeros(size(data.SCTL.Y(:,:,i)));
        TT = zeros(size(data.SCTL.T(:,:,i)));
        ST = zeros(size(data.SCTL.T(:,:,i)));
        RR = zeros(size(data.SCTL.T(:,:,i)));
        [ logL, bhat, Sim ] = logL_SCTL_si(xi, model, data, s, options, P, i);
        logLi_D(i,1) = logL.D;
        logLi_T(i,1) = logL.T;
        logLi_b(i,1) = logL.b;
        logL_sc(i,1) = logL.val;
        if(options.integration)
            logLi_I(i,1) = logL.I;
        end
        b(:,i) = bhat.val;
        if(options.nderiv>0)
            dbdxi(i,:,:) = bhat.dxi;
            dlogL_scdxi(i,:) = logL.dxi;
        end
        YY(data.SCTL.ind_y(:,i),:) = reshape(Sim.SCTL_Y,size(YY(data.SCTL.ind_y(:,i),:)));
        SY(data.SCTL.ind_y(:,i),:) = reshape(Sim.SCTL_Sigma_Y,size(SY(data.SCTL.ind_y(:,i),:)));
        TT(data.SCTL.ind_t(:,i),:) = reshape(Sim.SCTL_T,size(TT(data.SCTL.ind_t(:,i),:)));
        ST(data.SCTL.ind_t(:,i),:) = reshape(Sim.SCTL_Sigma_T,size(ST(data.SCTL.ind_t(:,i),:)));
        RR(data.SCTL.ind_t(:,i),:) = reshape(Sim.SCTL_R,size(RR(data.SCTL.ind_t(:,i),:)));
        Sim_SCTL_Y(:,:,i) = YY;
        Sim_SCTL_SIGMAY(:,:,i) = SY;
        Sim_SCTL_T(:,:,i) = TT;
        Sim_SCTL_SIGMAT(:,:,i) = ST;
        Sim_SCTL_R(:,:,i) = RR;
    end
end

P{s}.SCTL.bhat = b;
if(options.nderiv>0)
    P{s}.SCTL.dbhatdxi = dbdxi;
end
Sim_SCTL.Y = Sim_SCTL_Y;
Sim_SCTL.Sigma_Y = Sim_SCTL_SIGMAY;
Sim_SCTL.T = Sim_SCTL_T;
Sim_SCTL.Sigma_T = Sim_SCTL_SIGMAT;
Sim_SCTL.R = Sim_SCTL_R;


%% Visulization
if options.plot

    % Visualisation of single cell parameters
    if(isempty(fp))
        if(isfield(model,'title'))
            if(ischar(model.title))
                fp(s) = figure('Name',model.title);
            else
                fp(s) = figure;
            end
        else
            fp(s) = figure;
        end
    else
        if(length(fp)<s)
            if(isfield(model,'title'))
                if(ischar(model.title))
                    fp(s) = figure('Name',model.title);
                else
                    fp(s) = figure;
                end
            else
                fp(s) = figure;
            end
        elseif(isempty(fp(s)))
            if(isfield(model,'title'))
                if(ischar(model.title))
                    fp(s) = figure('Name',model.title);
                else
                    fp(s) = figure;
                end
            else
                fp(s) = figure;
            end
        end
    end
    figure(fp(s))
    clf
    b_s = P{s}.SCTL.bhat;
    n_b = size(b_s,1);

    for j = 1:n_b
        subplot(ceil((n_b+1)/4),4,j+1)
        xx = linspace(-5*sqrt(D(j,j)),5*sqrt(D(j,j)),100);
        %nhist(P{s}.SCTL.bhat(j,:),'pdf','noerror');
        hold on
        plot(xx,normcdf(xx,0,sqrt(D(j,j))),'.-b','LineWidth',2)
        ecdf = zeros(length(xx),1);
        for k = 1:length(xx)
            ecdf(k) = sum(b_s(j,:)<xx(k))/length(b_s(j,:));
        end
        plot(xx,ecdf,'--r','LineWidth',2)


        if(j==1)

        end
        xlim([-5*sqrt(D(j,j)),5*sqrt(D(j,j))])
        ylim([0,1.1])
        %xlabel(char(model.sym.b(model.ind_b(j))));
        ylabel('cdf')
        box on
    end
    subplot(ceil(n_b+1/4),4,1,'Visible','off')
    hold on
    plot(xx,normcdf(xx,0,sqrt(D(j,j))),'.-b','Visible','off')
    plot(xx,ecdf,'--r','LineWidth',2,'Visible','off')


    legend('cdf of single cell Parameters','cdf of populaton Parameters')

    % Visualisation of likelihood contribution
    if(isempty(fl))
        if(isfield(model,'title'))
            if(ischar(model.title))
                fl(s) = figure('Name',model.title);
            else
                fl(s) = figure;
            end
        else
            fl(s) = figure;
        end
    else
        if(length(fl)<s)
            if(isfield(model,'title'))
                if(ischar(model.title))
                    fl(s) = figure('Name',model.title);
                else
                    fl(s) = figure;
                end
            else
                fl(s) = figure;
            end
        elseif(isempty(fl(s)))
            if(isfield(model,'title'))
                if(ischar(model.title))
                    fl(s) = figure('Name',model.title);
                else
                    fl(s) = figure;
                end
            else
                fl(s) = figure;
            end
        end
    end
    figure(fl(s))
    clf
    if(options.integration)
        bar(transpose([logLi_D,logLi_T,logLi_b,logLi_I]),'stacked')
        set(gca,'XTickLabel',{'data','event','par','int'})
    else
        bar(transpose([logLi_D,logLi_T,logLi_b]),'stacked')
        set(gca,'XTickLabel',{'data','event','par'})
    end
    ylabel('log-likelihood')
    title('likelihood contribution')


    % Visualisation of data and fit
    model.plot(data,Sim_SCTL,s);
end

end