Multiple Figure Output from if/then - how to get onto one subplot?

This code fits a line to some data. When it fits certain perameters we set (r, b), it graphs the output.

Each iteration of a for loop (defined) has multiple graphs that fit these perameters. Right now, the code is opening a new window for each new figure. I would like it to instead arrange all the graphs into one subplot.

 fit_thresh = .25 ; %set a threshold for the fit of the linear regression, just randomly picked this number, could be more stringent
    %could change the fit_thresh
    for c = 1:size(mean_fr,1)
        

        %Then do a simple linear regression, look for a good fit and a
        %positive slope
        x = 1:numel(smoothdata(bin_spikes(c,:),'movmean',5));x=x'; %X in this case is just time (so 1 : number of bins)
        X = [ones(length(x),1) x];
        y = smoothdata(bin_spikes(c,:),'movmean',5); y=y';
        b = X\y ; % can use "mldividde" instead of "\"
        yfit = X *b ; %the line that is fit
        %assess fit using an R^2 measure
        r = 1 - sum((y - yfit).^2)/sum((y - mean(y)).^2) ; %goes from 0 to 1, with 1 being perfect fit

        if r > fit_thresh
            if b > 0 %looking for positive slope, could eventually put a threshold here
                thisfig = figure();
                plot(smoothdata(bin_spikes(c,:),'movmean',5)) %look for positive correlation, on the smoothened, binned data
                title(['Channel ' num2str(recording_info.channel_numbers(c)) ' Area ' recording_info.area{c} ])
                fig = gca;
                fig.XTickLabel = num2cell(fig.XTick *10);
                fig.XLabel.String = 'Time (ms) after sample offset';
                fig.YLabel.String = 'FR (spikes/s)';
                hold on
                plot (x, yfit, 'r-.');
                hold off
                end

It probably requires some sort of for loop with the subplot function, but how should it be structured? Where should I put it?

Answer 1

[Jyotsna Talluri]

Specify the subplot and hold on at the starting of for loop and hold off at the end of for loop.If size(mean_fr,1) is assumed to be 10,consider subplot(2,5,c);

 fit_thresh = .25 ; %set a threshold for the fit of the linear regression, just randomly picked this number, could be more stringent
    %could change the fit_thresh
    for c = 1:size(mean_fr,1)
        subplot(2,5,c);
        hold on;
        %Then do a simple linear regression, look for a good fit and a
        %positive slope
        x = 1:numel(smoothdata(bin_spikes(c,:),'movmean',5));x=x'; %X in this case is just time (so 1 : number of bins)
        X = [ones(length(x),1) x];
        y = smoothdata(bin_spikes(c,:),'movmean',5); y=y';
        b = X\y ; % can use "mldividde" instead of "\"
        yfit = X *b ; %the line that is fit
        %assess fit using an R^2 measure
        r = 1 - sum((y - yfit).^2)/sum((y - mean(y)).^2) ; %goes from 0 to 1, with 1 being perfect fit

        if r > fit_thresh
            if b > 0 %looking for positive slope, could eventually put a threshold here
                thisfig = figure();
                plot(smoothdata(bin_spikes(c,:),'movmean',5)) %look for positive correlation, on the smoothened, binned data
                title(['Channel ' num2str(recording_info.channel_numbers(c)) ' Area ' recording_info.area{c} ])
                fig = gca;
                fig.XTickLabel = num2cell(fig.XTick *10);
                fig.XLabel.String = 'Time (ms) after sample offset';
                fig.YLabel.String = 'FR (spikes/s)';
             
                plot (x, yfit, 'r-.');
              
                end

I hope this works fine.