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| @@ -1,79 +1,79 @@ | ||
| function models = PlotGAM(models,prs) | ||
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| %% Description | ||
| % This function will generate three plots: | ||
| % 1) log likelihood ratios of each model variant (with standard errors), | ||
| % the ratios being taken with respect to a one-parameter null model (constant | ||
| % firing rate with no tuning). | ||
| % 2) Fraction of variance in neural response explained by each model variant. | ||
| % 3) Marginal tuning functions of the best model. | ||
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| %% | ||
| fprintf('...... Plotting results\n'); | ||
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| %% load analysis parameters | ||
| prs = struct2cell(prs); | ||
| [varname,vartype,~, ~,nfolds,~,~,~,~,~] = deal(prs{:}); | ||
| nvars = length(varname); | ||
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| % give each combination of variables a name | ||
| nModels = length(models.class); | ||
| varlabel = cell(1,nvars); modellabel = cell(1,nModels); | ||
| for i=1:nvars | ||
| if strcmp(vartype{i},'2D'), varlabel{i} = varname{i}{1}(1); % use first letter of the variable name to label | ||
| else, varlabel{i} = varname{i}(1); end | ||
| end | ||
| for i=1:nModels, modellabel{i} = cell2mat(varlabel(models.class{i})); end | ||
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| %% load model info | ||
| testFit = cell2mat(models.testFit); | ||
| nrows = size(testFit,1); | ||
| bestmodel = models.bestmodel; | ||
| LLvals = reshape(testFit(:,3),nfolds,nrows/nfolds); % 3rd column contains likelihood values | ||
| Vexp = reshape(testFit(:,1),nfolds,nrows/nfolds); % 1st column contains variance explained | ||
| xvals = models.x; | ||
| if ~isnan(bestmodel), fvals = models.marginaltunings{bestmodel}; end | ||
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| %% plot | ||
| SS_pix = get(0,'screensize'); | ||
| figure; set(gcf,'Position',SS_pix); hold on; | ||
| Nc = 4; % plot N x 4 panels | ||
| Nr = 1 + 1 + ceil(nvars/Nc); % plot log-likelihood , var explained , tuning to each variable | ||
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| % likelihoods | ||
| subplot(Nr,Nc,1:Nc); hold on; | ||
| errorbar(nanmean(LLvals),nanstd(LLvals)/sqrt(nfolds),'ok','linewidth',3); | ||
| plot(bestmodel,mean(LLvals(:,bestmodel)),'.r','markersize',20); | ||
| plot(0.5:nModels+0.5,zeros(nModels+1,1),'--k','linewidth',2); | ||
| set(gca,'fontsize',16); box off; | ||
| set(gca,'XLim',[0 nModels+1]); set(gca,'XTick',1:nModels); | ||
| set(gca,'XTickLabel',modellabel); | ||
| legend('Model performance','Selected model','Null model'); | ||
| ylabel('Log likelihood ratio (bits/spike)','Fontsize',12); | ||
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| % variance explained | ||
| subplot(Nr,Nc,Nc+(1:Nc)); hold on; | ||
| errorbar(nanmean(Vexp),nanstd(Vexp)/sqrt(nfolds),'ok','linewidth',3); | ||
| plot(bestmodel,mean(Vexp(:,bestmodel)),'.r','markersize',20); | ||
| plot(0.5:nModels+0.5,zeros(nModels+1,1),'--k','linewidth',2); | ||
| set(gca,'fontsize',16); box off; | ||
| set(gca,'XLim',[0 nModels+1]); set(gca,'XTick',1:nModels); | ||
| set(gca,'XTickLabel',modellabel); | ||
| ylabel('Fraction of variance explained','Fontsize',12); | ||
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| % plot tuning functions if the best model is better than the null model | ||
| if ~isnan(bestmodel) | ||
| for i=1:nvars | ||
| if strcmp(vartype{i},'2D') && ~isempty(fvals{i}) | ||
| subplot(Nr,Nc,2*Nc+i); | ||
| imagesc(xvals{i}{1},xvals{i}{2},fvals{i}); | ||
| xlabel(varname{i}{1}); ylabel(varname{i}{2}); | ||
| set(gca,'fontsize',16); box off; | ||
| elseif ~isempty(fvals{i}) | ||
| subplot(Nr,Nc,2*Nc+i); | ||
| plot(xvals{i},fvals{i},'Linewidth',2,'Color','k'); | ||
| xlabel(varname{i}); ylabel('Firing rate (spk/s)'); | ||
| set(gca,'fontsize',16); box off; | ||
| end | ||
| end | ||
| function models = PlotGAM(models,prs) | ||
|
|
||
| %% Description | ||
| % This function will generate three plots: | ||
| % 1) log likelihood ratios of each model variant (with standard errors), | ||
| % the ratios being taken with respect to a one-parameter null model (constant | ||
| % firing rate with no tuning). | ||
| % 2) Fraction of variance in neural response explained by each model variant. | ||
| % 3) Marginal tuning functions of the best model. | ||
|
|
||
| %% | ||
| fprintf('...... Plotting results\n'); | ||
|
|
||
| %% load analysis parameters | ||
| prs = struct2cell(prs); | ||
| [varname,vartype,~,~, ~,nfolds,~,~,~,~,~,~,~] = deal(prs{:}); | ||
| nvars = length(varname); | ||
|
|
||
| % give each combination of variables a name | ||
| nModels = length(models.class); | ||
| varlabel = cell(1,nvars); modellabel = cell(1,nModels); | ||
| for i=1:nvars | ||
| if strcmp(vartype{i},'2D'), varlabel{i} = varname{i}{1}(1); % use first letter of the variable name to label | ||
| else, varlabel{i} = varname{i}(1); end | ||
| end | ||
| for i=1:nModels, modellabel{i} = cell2mat(varlabel(models.class{i})); end | ||
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|
||
| %% load model info | ||
| testFit = cell2mat(models.testFit); | ||
| nrows = size(testFit,1); | ||
| bestmodel = models.bestmodel; | ||
| LLvals = reshape(testFit(:,3),nfolds,nrows/nfolds); % 3rd column contains likelihood values | ||
| Vexp = reshape(testFit(:,1),nfolds,nrows/nfolds); % 1st column contains variance explained | ||
| xvals = models.x; | ||
| if ~isnan(bestmodel), fvals = models.marginaltunings{bestmodel}; end | ||
|
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||
| %% plot | ||
| SS_pix = get(0,'screensize'); | ||
| figure; set(gcf,'Position',SS_pix); hold on; | ||
| Nc = 4; % plot N x 4 panels | ||
| Nr = 1 + 1 + ceil(nvars/Nc); % plot log-likelihood , var explained , tuning to each variable | ||
|
|
||
| % likelihoods | ||
| subplot(Nr,Nc,1:Nc); hold on; | ||
| errorbar(nanmean(LLvals),nanstd(LLvals)/sqrt(nfolds),'ok','linewidth',3); | ||
| plot(bestmodel,mean(LLvals(:,bestmodel)),'.r','markersize',20); | ||
| plot(0.5:nModels+0.5,zeros(nModels+1,1),'--k','linewidth',2); | ||
| set(gca,'fontsize',16); box off; | ||
| set(gca,'XLim',[0 nModels+1]); set(gca,'XTick',1:nModels); | ||
| set(gca,'XTickLabel',modellabel); | ||
| legend('Model performance','Selected model','Null model'); | ||
| ylabel('Log likelihood ratio (bits/spike)','Fontsize',12); | ||
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||
| % variance explained | ||
| subplot(Nr,Nc,Nc+(1:Nc)); hold on; | ||
| errorbar(nanmean(Vexp),nanstd(Vexp)/sqrt(nfolds),'ok','linewidth',3); | ||
| plot(bestmodel,mean(Vexp(:,bestmodel)),'.r','markersize',20); | ||
| plot(0.5:nModels+0.5,zeros(nModels+1,1),'--k','linewidth',2); | ||
| set(gca,'fontsize',16); box off; | ||
| set(gca,'XLim',[0 nModels+1]); set(gca,'XTick',1:nModels); | ||
| set(gca,'XTickLabel',modellabel); | ||
| ylabel('Fraction of variance explained','Fontsize',12); | ||
|
|
||
| % plot tuning functions if the best model is better than the null model | ||
| if ~isnan(bestmodel) | ||
| for i=1:nvars | ||
| if strcmp(vartype{i},'2D') && ~isempty(fvals{i}) | ||
| subplot(Nr,Nc,2*Nc+i); | ||
| imagesc(xvals{i}{1},xvals{i}{2},fvals{i}); | ||
| xlabel(varname{i}{1}); ylabel(varname{i}{2}); | ||
| set(gca,'fontsize',16); box off; | ||
| elseif ~isempty(fvals{i}) | ||
| subplot(Nr,Nc,2*Nc+i); | ||
| plot(xvals{i},fvals{i}.mean,'Linewidth',2,'Color','k'); | ||
| xlabel(varname{i}); ylabel('Firing rate (spk/s)'); | ||
| set(gca,'fontsize',16); box off; | ||
| end | ||
| end | ||
| end |