Merge branch 'master' of https://github.com/ICB-DCM/PESTO

Author: LoosC

examples/conversion_reaction/mainConversionReaction.m

@@ -261,12 +261,12 @@
 
 %% Confidence interval evaluation -- Parameters
 % Confidence intervals to the confidence levels fixed in the array
-% alpha
+% confLevels
 % are computed based on local approximations from the Hessian matrix at the
 % optimum, based on the profile likelihoods and on the parameter sampling.
 
-alpha = [0.9,0.95,0.99];
-parameters = getParameterConfidenceIntervals(parameters, alpha, optionsPesto);
+confLevels = [0.9,0.95,0.99];
+parameters = getParameterConfidenceIntervals(parameters, confLevels, optionsPesto);
 
 
 %% Evaluation of properties for multi-start local optimization results -- Properties
@@ -298,7 +298,7 @@
 % properties in different fashion, based on local approximations, profile
 % likelihoods and samples.
 
-properties = getPropertyConfidenceIntervals(properties, alpha, optionsProperties);
+properties = getPropertyConfidenceIntervals(properties, confLevels, optionsProperties);
 
 
 %% Comparison of calculated parameter profiles

examples/enzymatic_catalysis/mainEnzymaticCatalysis.m

@@ -125,11 +125,11 @@
 % Confidence Intervals for the Parameters are inferred from the local 
 % optimization and the sampling information.
 
-% Set alpha levels
-alphaLevels = [0.8, 0.9, 0.95, 0.99];
+% Set confidence levels
+confLevels = [0.8, 0.9, 0.95, 0.99];
 
 display(' Computing confidence intervals...');
-parameters = getParameterConfidenceIntervals(parameters, alphaLevels, optionsPesto);
+parameters = getParameterConfidenceIntervals(parameters, confLevels, optionsPesto);
 
 
 %% Perform Multistart optimization
@@ -166,7 +166,7 @@
 % Confidence Intervals for the Parameters are inferred from the local 
 % optimization and the sampling information.
 display(' Computing confidence intervals...');
-parameters = getParameterConfidenceIntervals(parameters, alphaLevels, optionsPesto);
+parameters = getParameterConfidenceIntervals(parameters, confLevels, optionsPesto);
 
 
 %% Calculate Profile Likelihoods
@@ -209,4 +209,4 @@
 %% Calculate Confidence Intervals
 % Confidence Intervals for the Parameters are inferred from the local 
 % optimization, the sampling and the profile information.
-parameters = getParameterConfidenceIntervals(parametersNew, alphaLevels, optionsPesto);
+parameters = getParameterConfidenceIntervals(parametersNew, confLevels, optionsPesto);

examples/mRNA_transfection/mainTransfection.m

@@ -311,21 +311,21 @@
 
 
 %% Confidence interval evaluation -- Parameters
-% Confidence intervals to the confidence levels fixed in the array alpha
+% Confidence intervals to the confidence levels fixed in the array confLevels
 % are computed based on local approximations from the Hessian matrix at the
 % optimum, based on the profile likelihoods and on the parameter sampling.
 
-alpha = [0.9,0.95,0.99];
+confLevels = [0.9,0.95,0.99];
 
 % Computation for the first mode
 optionsPesto.MAP_index = 1;
 optionsPesto.parameter_index = 1 : parameters.number;
-parameters = getParameterConfidenceIntervals(parameters, alpha, optionsPesto);
+parameters = getParameterConfidenceIntervals(parameters, confLevels, optionsPesto);
 
 % Computation for the second mode
 optionsPesto.MAP_index = MAP_index2;
 optionsPesto.parameter_index = [3, 4];
-parametersAlt = getParameterConfidenceIntervals(parametersAlt, alpha, optionsPesto);
+parametersAlt = getParameterConfidenceIntervals(parametersAlt, confLevels, optionsPesto);
 
 %% Evaluation of properties for multi-start local optimization results -- Properties
 % The values of the properties are evaluated at the end points of the
@@ -353,7 +353,7 @@
 % properties in different fashion, based on local approximations, profile
 % likelihoods and samples.
 
-properties = getPropertyConfidenceIntervals(properties, alpha);
+properties = getPropertyConfidenceIntervals(properties, confLevels);
 
 %% Comparison of calculated parameter profiles
 

getConfidenceIntervals.m

@@ -1,23 +1,23 @@
-function pStruct = getConfidenceIntervals(pStruct, alpha, type, varargin)
+function pStruct = getConfidenceIntervals(pStruct, confLevels, type, varargin)
     % getConfidenceIntervals() calculates the confidence intervals for the 
     % model parameters or properties. This is done by four approaches:
     % The values of CI.local_PL and CI.PL are determined by the point on which 
-    % a threshold according to the confidence level alpha (calculated by a 
+    % a threshold according to the confidence level (calculated by a 
     % chi2-distribution) is reached. local_PL computes this point by a local
     % approximation around the MAP estimate using the Hessian matrix, PL uses 
     % the profile likelihoods instead.
     % The value of CI.local_B is computed by using the cummulative distribution
     % function of a local approximation of the profile based on the Hessian
     % matrix at the MAP estimate.
     % The value of CI.S is calculated using samples for the model parameters
-    % and the according percentiles based on the confidence levels alpha.
+    % and the according percentiles based on the confidence levels.
     %
     % USAGE:
-    % * pStruct = getConfidenceIntervals(pStruct, alpha)
+    % * pStruct = getConfidenceIntervals(pStruct, confLevels)
     %
     % Parameters:
     %   pStruct: parameter or properties struct
-    %   alpha: vector with desired confidence levels for the intervals
+    %   confLevels: vector with desired confidence levels for the intervals
     %   varargin: 
     %    options: A PestoOptions instance
     %
@@ -64,32 +64,32 @@
     end
 
     % Initialization
-    pStruct.CI.alpha_levels = alpha;
+    pStruct.CI.confLevels = confLevels;
 
-    % Loop: alpha levels
-    for iConfLevel = 1:length(alpha)
+    % Loop: confidence levels
+    for iConfLevel = 1:length(confLevels)
         % Loop: Parameters
         for iP = options.(p_index)
             if isfield(pStruct,'MS')
-                pStruct = getCIfromOptimization(pStruct, alpha(iConfLevel), type, iMAP, iP, iConfLevel, options);
+                pStruct = getCIfromOptimization(pStruct, confLevels(iConfLevel), type, iMAP, iP, iConfLevel, options);
             end
 
             % Confidence intervals computed using profile likelihood
             if isfield(pStruct,'P')
-                pStruct = getCIfromProfiles(pStruct, alpha(iConfLevel), type, iMAP, iP, iConfLevel);
+                pStruct = getCIfromProfiles(pStruct, confLevels(iConfLevel), type, iMAP, iP, iConfLevel);
             end
 
             % Confidence intervals computed using sample
             if isfield(pStruct,'S')
-                pStruct.CI.S(iP,:,iConfLevel) = prctile(pStruct.S.(type)(iP,:,1),50 + 100*[-alpha(iConfLevel)/2, alpha(iConfLevel)/2]);
+                pStruct.CI.S(iP,:,iConfLevel) = prctile(pStruct.S.(type)(iP,:,1),50 + 100*[-confLevels(iConfLevel)/2, confLevels(iConfLevel)/2]);
             end
         end
     end
 
     %% Output
     switch options.mode
         case 'visual'
-            plotConfidenceIntervals(pStruct, alpha, [], options);
+            plotConfidenceIntervals(pStruct, [], options);
             disp('-> Calculation of confidence intervals for parameters FINISHED.');
         case 'text'
             disp('-> Calculation of confidence intervals for parameters FINISHED.');
@@ -99,7 +99,7 @@
 end
 
 
-function pStruct = getCIfromOptimization(pStruct, alpha, type, iMAP, iP, iConfLevel, options)
+function pStruct = getCIfromOptimization(pStruct, confLevel, type, iMAP, iP, iConfLevel, options)
 
     if strcmp(type, 'par')
         % Inversion of Hessian
@@ -116,36 +116,36 @@
 
     % Confidence intervals computed using local approximation and a
     % threshold (-> similar to PL-based confidence intervals)
-    pStruct.CI.local_PL(iP,1,iConfLevel) = pStruct.MS.(type)(iP,iMAP) - sqrt(icdf('chi2',alpha,1)*Sigma(iP,iP));
-    pStruct.CI.local_PL(iP,2,iConfLevel) = pStruct.MS.(type)(iP,iMAP) + sqrt(icdf('chi2',alpha,1)*Sigma(iP,iP));
+    pStruct.CI.local_PL(iP,1,iConfLevel) = pStruct.MS.(type)(iP,iMAP) - sqrt(icdf('chi2',confLevel,1)*Sigma(iP,iP));
+    pStruct.CI.local_PL(iP,2,iConfLevel) = pStruct.MS.(type)(iP,iMAP) + sqrt(icdf('chi2',confLevel,1)*Sigma(iP,iP));
 
     % Confidence intervals computed using local approximation and the
     % probability mass (-> similar to Bayesian confidence intervals)
-    pStruct.CI.local_B(iP,1,iConfLevel)  = icdf('norm',  (1-alpha)/2,pStruct.MS.(type)(iP,iMAP),sqrt(Sigma(iP,iP)));
-    pStruct.CI.local_B(iP,2,iConfLevel)  = icdf('norm',1-(1-alpha)/2,pStruct.MS.(type)(iP,iMAP),sqrt(Sigma(iP,iP)));
+    pStruct.CI.local_B(iP,1,iConfLevel)  = icdf('norm',  (1-confLevel)/2,pStruct.MS.(type)(iP,iMAP),sqrt(Sigma(iP,iP)));
+    pStruct.CI.local_B(iP,2,iConfLevel)  = icdf('norm',1-(1-confLevel)/2,pStruct.MS.(type)(iP,iMAP),sqrt(Sigma(iP,iP)));
 
 end
 
 
-function pStruct = getCIfromProfiles(pStruct, alpha, type, iMAP, iP, iConfLevel)
+function pStruct = getCIfromProfiles(pStruct, confLevel, type, iMAP, iP, iConfLevel)
 
     if ~isempty(pStruct.P(iP).(type))
         % left bound
         if strcmp(type, 'par')
             ind  = find(pStruct.P(iP).(type)(iP,:) <= pStruct.MS.(type)(iP,iMAP));
-            j = find(pStruct.P(iP).R(ind) <= exp(-icdf('chi2',alpha,1)/2),1,'last');
+            j = find(pStruct.P(iP).R(ind) <= exp(-icdf('chi2',confLevel,1)/2),1,'last');
             if ~isempty(j)
                 pStruct.CI.PL(iP,1,iConfLevel) = interp1(pStruct.P(iP).R(ind([j,j+1])),...
-                    pStruct.P(iP).(type)(iP,ind([j,j+1])),exp(-icdf('chi2',alpha,1)/2));
+                    pStruct.P(iP).(type)(iP,ind([j,j+1])),exp(-icdf('chi2',confLevel,1)/2));
             else
                 pStruct.CI.PL(iP,1,iConfLevel) = -inf;
             end
         else
             ind  = find(pStruct.P(iP).(type) <= pStruct.MS.(type)(iP,1));
-            j = find(pStruct.P(iP).R(ind) <= exp(-icdf('chi2',alpha,1)/2),1,'last');
+            j = find(pStruct.P(iP).R(ind) <= exp(-icdf('chi2',confLevel,1)/2),1,'last');
             if ~isempty(j)
                 pStruct.CI.PL(iP,1,iConfLevel) = interp1(pStruct.P(iP).R(ind([j,j+1])),...
-                    pStruct.P(iP).(type)(ind([j,j+1])),exp(-icdf('chi2',alpha,1)/2));
+                    pStruct.P(iP).(type)(ind([j,j+1])),exp(-icdf('chi2',confLevel,1)/2));
             else
                 pStruct.CI.PL(iP,1,iConfLevel) = -inf;
             end
@@ -154,19 +154,19 @@
         % right bound
         if strcmp(type, 'par')
             ind  = find(pStruct.P(iP).(type)(iP,:) >= pStruct.MS.(type)(iP,iMAP));
-            j = find(pStruct.P(iP).R(ind) <= exp(-icdf('chi2',alpha,1)/2),1,'first');
+            j = find(pStruct.P(iP).R(ind) <= exp(-icdf('chi2',confLevel,1)/2),1,'first');
             if ~isempty(j)
                 pStruct.CI.PL(iP,2,iConfLevel) = interp1(pStruct.P(iP).R(ind([j-1,j])),...
-                    pStruct.P(iP).(type)(iP,ind([j-1,j])),exp(-icdf('chi2',alpha,1)/2));
+                    pStruct.P(iP).(type)(iP,ind([j-1,j])),exp(-icdf('chi2',confLevel,1)/2));
             else
                 pStruct.CI.PL(iP,2,iConfLevel) = inf;
             end
         else
             ind  = find(pStruct.P(iP).(type) >= pStruct.MS.(type)(iP,1));
-            j = find(pStruct.P(iP).R(ind) <= exp(-icdf('chi2',alpha,1)/2),1,'first');
+            j = find(pStruct.P(iP).R(ind) <= exp(-icdf('chi2',confLevel,1)/2),1,'first');
             if ~isempty(j)
                 pStruct.CI.PL(iP,2,iConfLevel) = interp1(pStruct.P(iP).R(ind([j-1,j])),...
-                    pStruct.P(iP).(type)(ind([j-1,j])),exp(-icdf('chi2',alpha,1)/2));
+                    pStruct.P(iP).(type)(ind([j-1,j])),exp(-icdf('chi2',confLevel,1)/2));
             else
                 pStruct.CI.PL(iP,2,iConfLevel) = inf;
             end

plotConfidenceIntervals.m

@@ -1,4 +1,4 @@
-function fh = plotConfidenceIntervals(pStruct, alpha, varargin)
+function fh = plotConfidenceIntervals(pStruct, varargin)
 % plotConfidenceIntervals.m visualizes confidence itervals stored in either
 % the parameters or properties struct .CI
 %
@@ -30,7 +30,6 @@
 %       information about parameters and results of optimization (.MS) 
 %       and uncertainty analysis (.P and .S). This structures is the output
 %       of plotMultiStarts.m, getProfiles.m or plotSamples.m.
-%  alpha: significance levels
 %   varargin:
 %     method: integer array, from which method confidence intervals 
 %         should be plotted: 
@@ -298,8 +297,8 @@
             methodsOut.name{iMeth} = tempMethName{j};
         end
     end
-    methodsOut.numLevels = length(pStruct.CI.alpha_levels);
-    methodsOut.levels = pStruct.CI.alpha_levels;
+    methodsOut.numLevels = length(pStruct.CI.confLevels);
+    methodsOut.levels = pStruct.CI.confLevels;
     methodsOut.num = length(methodsOut.name);
     methodsOut.bars = linspace(0.3, 0.15, methodsOut.numLevels);
     colors = nan(methodsOut.num, 3, methodsOut.numLevels);