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rsa_testModelCompare.m
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function varargout=rsa_testModelCompare(what,varargin)
% Testing different model RSA comparision methods as studied in Diedrichsen
% et al. (2017).
% Running simulations and producing Figures.
% Setting basedir for data files: change.
baseDir = '/Users/jdiedrichsen/Dropbox (Diedrichsenlab)/Projects/modelCompare';
% Use develop branch of the RSA matlab toolbox at https://github.com/rsagroup/rsatoolbox_matlab
import rsa.*;
import rsa.util.*;
import rsa.stat.*;
import rsa.rdm.*;
% Make the representational model matrices from features
switch (what)
% Prepare 3 different models and bring them into the correct format
case 'prep5FingerModel' % Prepare models for Experiment 1 (done)
M(1)=load(fullfile(baseDir,'Muscle_model.mat'));
M(2)=load(fullfile(baseDir,'Naturalstats_model.mat'));
for i=1:length(M)
M(i).RDM=M(i).RDM./sqrt(sum(M(i).RDM.^2));
end;
save(fullfile(baseDir,'Model_fiveFinger.mat'),'M');
varargout={M};
case 'prepChord' % Prepare models for Experiment 2 (done)
% Standardisation, but no absolute value
D=load(fullfile(chordDir,'distance_emg.mat'));
M(1).RDM=mean(D.dist(D.method==6,:));
M(1).name='Musclemodel';
D=naturalglove_analyze('naturalstats_distance_direct','Velocity_Cov_MCP_chords_01.mat');
M(2).RDM=mean(D.dist);
M(2).name='Naturalstats';
for i=1:length(M)
M(i).RDM=M(i).RDM./sqrt(sum(M(i).RDM.^2));
end;
save(fullfile(baseDir,'Model_chords.mat'),'M');
varargout={M};
case 'prepStartA' % Prepare models for Experiment 3 (done)
load(fullfile(baseDir,'START_B_visualiseRDMs','modelRDMs_A2.mat'));
M=modelRDMs_struct;
indx=[];
for i=1:length(M)
if ~isempty(strfind(M(i).name,'compl'))
indx=[indx i];
end;
end;
M=M(indx(1:end-1));
for i=1:length(M)
M(i).RDM=M(i).RDM./sqrt(sum(M(i).RDM.^2));
end;
save(fullfile(baseDir,'START_compl.mat'),'M');
case 'evaluateSubspace' % Looks at the subspace overlap of models in terms of their principal components
M=varargin{1};
for m=1:2
% Prep variance components
RDM{m}=rsa.rdm.squareRDM(M{m}.RDM);
numCat=size(RDM{m},1);
H = eye(numCat)-ones(numCat)/numCat; % Centering matrix
G{m}=-0.5*H*RDM{m}*H;
% Get the design matrix for encoding models
[V,Lam]=eig(G{m});
[lambda{m},i]=sort(diag(Lam),'descend');
X{m}=bsxfun(@times,V(:,i),sqrt(lambda{m}'));
end;
for k=1:numCat-1
R2(k)=subspace_overlap(X{1}(:,1:k),X{2}(:,1:k));
end
subplot(2,1,1); % Plot the amount of variance captured
cs1 =cumsum(lambda{1})./sum(lambda{1});
cs2= cumsum(lambda{2})./sum(lambda{2});
plot([1:numCat]',cs1,'b',[1:numCat]',cs2,'r');
subplot(2,1,2);
plot([1:k],R2);
case 'simulate_data' % Generate data from model structure: Generate activities and then distances
% 1. model distance by d = X*omega
% 2. get G from d by G = (H(-0.5*d)H'), H: centering matrix (I-1/N)
% 3. get trueU (u=cholcov(G)*normrand(0,1))
% 4. get Y (= Za*u + noise)
% 5. get d_hat and sigma_hat by crossvalidation
%
% Note that G=UU'= P * sum(omega*Gc);
% Noise*Noise' = P * var_e
% This for a SNR of 1:1 omega needs to be same size as var_e
Model = varargin{1}; % Input Model RDM
% Bring into Model Data frame structure if not already
if length(Model)>1
Model = struct2dataframe(Model);
end;
% Get info of RDMs
D.numComp = size(Model.RDM,1); % Number of components
D.numCond = size(squareform(Model.RDM(1,:)),1); % Number of conditions
% (Simulated) Experimental parameters: default
D.numPart = 9; % Number of runs
D.numVox = 160; % Number of voxels
D.omega = zeros(1,D.numComp); % hyperparamter on distance model
D.var_e = 1; % Noise variance: Like omega, this is cumulative over voxels
D.Sigma_P = []; % Noise-covariance matrix across voxels - if empty assumes identity
D.Sigma_K = []; % Noise-covariance matrix axross conditions- if empty assumes identity
D.numSim = 1000; % Number of simulation
%- allow to get user options
D = rsa_getUserOptions({varargin{2:end}},D);
D.N = D.numPart*D.numCond; % Number of trials
part = kron([1:D.numPart]',ones(D.numCond,1)); % Partitions
conditions = kron(ones(D.numPart,1),[1:D.numCond]'); % Conditions
% Get design matrix
Za = kron(ones(D.numPart,1),eye(D.numCond));
X = double(Model.RDM');
% Make true distances
D.d_true = double(D.omega*X');
% Now make true covariance matrix: infer from distances if not
% given
if (isfield(Model,'IPM')); % See if there is a inner product matrix
G = D.omega*Model.IPM;
G = rsa_squareIPM(G);
else
H = eye(D.numCond)-ones(D.numCond)/D.numCond;
G = -0.5 * H * squareform(D.d_true) * H'; % mean subtracted
end;
S = [];
% Get cholesky decomposition of the covariances -
% mean(trace(sigma)) is assumed to be 1
if(~isempty(D.Sigma_P))
cholcov_P = cholcov(D.Sigma_P);
else
cholcov_P = eye(D.numVox);
end
if(~isempty(D.Sigma_K))
cholcov_K = cholcov(D.Sigma_K);
else
cholcov_K = eye(D.numCond);
end
% Now run the simulations
for n = 1:D.numSim
% Generate true pattern from predicted distances
trueU = mvnrnd_exact(G,D.numVox);
% Generate artificial data
Noise = [];
for p = [1:D.numPart]
Noise = [Noise;sqrt(D.var_e) * cholcov_K'*randn(D.numCond,D.numVox)*cholcov_P]; % Strech the variance of the noise over voxels, just as omega
end
Y(:,:,n) = Za*trueU + Noise;
% Calc cross-validated distance and noise estimate
% try
[d_hat,Sig_hat] = rsa.distanceLDC(Y(:,:,n),part,conditions);
Xcon = indicatorMatrix('identity',conditions);
d_1 = pdist(pinv(Xcon)*Y(:,:,n),'euclidean');
% catch
%
% tmp2 = indicatorMatrix('allpairs',unique(conditions)');
% [d_hat,Sig_hat] = distance_ldc_sigma(Y(:,:,n),tmp1,tmp2,part);
% end
S.RDM(n,:) = d_hat';
S.RDMn(n,:)= (d_1.*d_1)';
S.Sig_hat(n,:)= Sig_hat(tril(true(D.numCond),0))'; % Get vectorized version of the variance-covariance matrix
S.sig_hat(n,1)= mean(diag(Sig_hat)); % Noise variance estimate
Sigma(:,:,n) = Sig_hat;
end;
S.numVox = ones(size(S.RDM,1),1)*D.numVox;
varargout = {S, Y, D, Sigma,conditions,part};
case 'modelCompare'
% RSA_methods={'spearman','pearson','fixed','loglikIRLS'};
% RSA simulations
% rsa_testModelCompare('modelCompare','model','Model_fiveFinger.mat','numSim',1000,'outfile','sim_rsa_Exp1.mat','methods',RSA_methods,'Omega',[0:0.1:0.8]);
% rsa_testModelCompare('modelCompare','model','Model_chords.mat','numSim',100,'outfile','sim_rsa_Exp2.mat','methods',RSA_methods,'Omega',[0:0.05:0.3]);
% rsa_testModelCompare('modelCompare','model','Model_START.mat','numSim',1,'outfile','sim_rsa_Exp3.mat','methods',RSA_methods,'Omega',[0:0.1:0.6]);
%
% NEW RSA simulation (for Diedrichsen et al. 2020)
% RSA_methods={'spearman','pearson','pearsonNc','pearsonSq','pearsonNcSq','pearsonWNc','cosine','cosineWNull'};
% rsa_testModelCompare('modelCompare','model','Model_fiveFinger.mat','numSim',1000,'outfile','sim_rsan_Exp1a.mat','methods',RSA_methods,'Omega',[0:0.1:0.8]);
% rsa_testModelCompare('modelCompare','model','Model_chords.mat','numSim',1000,'outfile','sim_rsan_Exp2a.mat','methods',RSA_methods,'Omega',[0:0.05:0.3]);
% rsa_testModelCompare('modelCompare','model','Model_START.mat','numSim',200,'outfile','sim_rsan_Exp3a.mat','methods',RSA_methods,'Omega',[0:0.1:0.6]);
% rsa_testModelCompare('modelCompare','model','Model_START.mat','numSim',200,'outfile','sim_rsan_Exp3b.mat','methods',RSA_methods,'Omega',[0:0.1:0.6]);
%
% Probabilistic RSA simlation
% RSA_methods={'spearman','pearson','cosine','cosineWNull','cosineWData'};
% rsa_testModelCompare('modelCompare','model','Model_fiveFinger.mat','numSim',1000,'outfile','sim_rsaProb_Exp1.mat','methods',RSA_methods,'Omega',[0:0.1:0.8]);
% rsa_testModelCompare('modelCompare','model','Model_chords.mat','numSim',1000,'outfile','sim_rsaProb_Exp2a.mat','methods',RSA_methods,'Omega',[0:0.05:0.3]);
% rsa_testModelCompare('modelCompare','model','Model_START.mat','numSim',50,'outfile','sim_rsaProb_Exp3a.mat','methods',RSA_methods,'Omega',[0:0.1:0.6]);
%
%
% PCM simulations
% rsa_testModelCompare('modelCompare','model','Model_fiveFinger.mat','numSim',500,'outfile','sim_pcm_Exp1.mat','methods',{'loglikPCM'},'Omega',[0:0.1:0.8]);
% rsa_testModelCompare('modelCompare','model','Model_chords.mat','numSim',100,'outfile','sim_pcm_Exp2.mat','methods',{'loglikPCM'},'Omega',[0:0.05:0.3]);
% rsa_testModelCompare('modelCompare','model','Model_START.mat','numSim',100,'outfile','sim_pcm_Exp3.mat','methods',{'loglikPCM'},'Omega',[0:0.1:0.6]);
%
% OPT_methods={'encodePCM','loglikIRLS','loglikPCM'};
% rsa_testModelCompare('modelCompare','model','Model_fiveFinger.mat','numSim',1000,'outfile','sim_opt_Exp1a.mat','methods',OPT_methods,'Omega',0.3);
% rsa_testModelCompare('modelCompare','model','Model_chords.mat','numSim',1000,'outfile','sim_opt_Exp2a.mat','methods',OPT_methods,'Omega',0.15);
% rsa_testModelCompare('modelCompare','model','Model_START.mat','numSim',500,'outfile','sim_opt_Exp3a.mat','methods',OPT_methods,'Omega',0.6);
%
% Encode simulations
% rsa_testModelCompare('modelCompare','model','Model_fiveFinger.mat','numSim',1000,'outfile','sim_encPCMCorr_Exp1.mat','methods',{'encodePCMCorr'},'Omega',[0:0.1:0.8]);
% rsa_testModelCompare('modelCompare','model','Model_fiveFinger.mat','numSim',1000,'outfile','sim_encRidgeCorr_Exp1.mat','methods',{'encodeRidgeCorr'},'Omega',[0:0.1:0.8]);
% rsa_testModelCompare('modelCompare','model','Model_chords.mat','numSim',1000,'outfile','sim_encRidgeCorr_Exp2.mat','methods',{'encodeRidgeCorr'},'Omega',[0:0.05:0.3]);
% rsa_testModelCompare('modelCompare','model','START_compl.mat','numSim',500,'outfile','sim_encRidgeCorr_Exp3.mat','methods',{'encodeRidgeCorr'},'Omega',[0:0.1:0.8]);
Opt.methods ={'kendall','spearman','pearson','fixed','loglikIRLS','loglikPCM','encodeReg','encodePCM'};
Opt.methods ={'encodePCMcorr'};
Opt.model = 'Model_fiveFinger.mat';
Opt.numSim = 1000;
Opt.numPart = 8;
Opt.numVox = 160;
Opt.outfile = [];
Opt.Sigma_K = []; % Condiiton covariance of noise
Opt.Sigma_P = []; % Voxel covariance in noise
Opt.Omega = [0 0.3 0.6 0.9];
Opt=rsa.getUserOptions(varargin,Opt);
L=[]; % Summary stats
U=[]; % details on each simulation
numMethods = length(Opt.methods);
if (ischar(Opt.model))
load(fullfile(baseDir,Opt.model));
else
M=Opt.model;
end;
numModels = length(M);
% Experimental constants
test=rsa.rdm.squareRDM(M{1}.RDM);
D.numCat = size(test,1);
D.var_e = 1; % Noise variance
D.numPart = Opt.numPart;
D.numVox = Opt.numVox;
D.numSim = Opt.numSim;
D.Sigma_P = Opt.Sigma_P;
D.Sigma_K = Opt.Sigma_K;
% Prep some convenience matrices
H = eye(D.numCat)-ones(D.numCat)/D.numCat; % Centering matrix
part = kron([1:D.numPart]',ones(D.numCat,1));
cond = kron(ones(D.numPart,1),[1:D.numCat]');
Xpart = indicatorMatrix('identity',part);
Xcond = indicatorMatrix('identity',cond);
C=pcm_indicatorMatrix('allpairs',[1:D.numCat]); % Contrast vector for all pairs
% Prep the Variance components and regression matricies for each model
for m=1:numModels
% Prep variance components
RDM{m}=rsa.rdm.squareRDM(M{m}.RDM);
G{m}=-0.5*H*RDM{m}*H;
% Get the design matrix for encoding models
[V,Lam]=eig(G{m});
[lambda{m},i]=sort(diag(Lam),'descend');
X{m}=bsxfun(@times,V(:,i),sqrt(lambda{m}'));
X{m}=X{m}(:,lambda{m}>eps);
numReg(m) = size(X{m},2);
end;
% Loop over different signal levels
for om=Opt.Omega;
D.omega = om;
fprintf('%2.3f\n',om);
T=[];
% Generate data from the two models
Y=[];
for m=1:numModels
[S,y,D]=rsa_testModelCompare('simulate_data',M{m},D);
S.truemodel=ones(D.numSim,1)*m;
T=addstruct(T,S);
Y=cat(3,Y,y);
end;
T.omega = ones(D.numSim*numModels,1)*om;
for m=1:numModels
% Calculate the different forms of correlation,
for meth = 1:numMethods
tic;
switch(Opt.methods{meth})
case 'kendall'
T.kendall(:,m) = corr(T.RDMn',M{m}.RDM','type','Kendall');
case 'spearman'
T.spearman(:,m) = corr(T.RDMn',M{m}.RDM','type','Spearman');
case 'pearson'
T.pearson(:,m) = corr(T.RDM',M{m}.RDM');
case 'pearsonNc'
T.pearsonNc(:,m) = corr(T.RDMn',M{m}.RDM');
case 'pearsonWNc'
T.pearsonWNc(:,m) = rsa_covWeightCosine(T.RDMn,M{m}.RDM,1);
case 'pearsonNcSq'
T.pearsonNcSq(:,m) = corr(sqrt(T.RDMn'),sqrt(M{m}.RDM'));
case 'pearsonSq'
T.pearsonSq(:,m) = corr(sqrt(T.RDM'),sqrt(M{m}.RDM'));
case 'cosine'
nRDM=bsxfun(@rdivide,T.RDM,sqrt(sum(T.RDM.^2,2)));
mRDM=(M{m}.RDM')/sqrt(sum(M{m}.RDM.^2));
T.cosine(:,m) = nRDM*mRDM;
case 'cosineWNull' % Cosine angle weighted by the covariance structure under the Null-hypothesis
T.cosineWNull(:,m) = rsa_covWeightCosine(T.RDM,M{m}.RDM);
case 'cosineWData' % Cosine angle weighted by the covariance structure under the Full hypothesis
for n=1:size(T.RDM,1)
G=H*squareform(-0.5*T.RDM(n,:))*H';
G=pcm_makePD(G);
varD = rsa_varianceLDC(G,C,T.sig_hat(n,:),D.numPart,D.numVox);
T.cosineWData(n,m) = cosineW(T.RDM(n,:),M{m}.RDM,varD);
end;
case 'fixed'
nRDM=bsxfun(@rdivide,T.RDM,sqrt(sum(T.RDM.^2,2)));
[T.weight(:,m),T.fixed(:,m),T.loglikeFixed(:,m)] = ...
rsa.stat.fitModelOLS(M{m}.RDM',T.RDM);
case 'loglikIRLS'
% Likelihood under normal approximation with assumed Sigma
[T.weight(:,m),~,T.loglikIRLS(:,m)]=...
rsa_fitModelIRLS(M{m}.RDM',T.RDM,T.sig_hat,D.numPart,D.numVox);
case 'loglikIRLSsig'
% Likelihood under normal approximation with
% inferred structure, but open sigma for
% scaling
[T.weight(:,m),~,T.loglikIRLSsig(:,m)]=...
rsa_fitModelIRLS(M{m}.RDM',T.RDM,T.sig_hat,D.numPart,D.numVox,'assumeVoxelInd',0);
case 'loglikPCM'
% PCM-based likelihood.
OPT.fitScale = 1;
OPT.runEffect = [];
for n=1:size(T.RDM,1)
YY = Y(:,:,n)*Y(:,:,n)';
fcn = @(x) pcm_likelihoodIndivid(x,YY,M{m},Xcond,Xpart,Opt.numVox,OPT);
[theta,T.loglikPCM(n,m)] = pcm_NR(zeros(2,1),fcn);
T.PCMs(n,m)=theta(1);
T.PCMe(n,m)=theta(2);
end;
case 'encodeReg'
% Encoding model without regularisation
for n=1:size(T.RDM,1)
[T.encodeReg(n,m),~,~,T.encodeRegCorr(n,m)] = encode_crossval(Y(:,:,n),Xcond*X{m}(:,1:2),part,'linregress','X',Xpart);
end;
case 'encodePCM'
% Encoding model with regularisation
for n=1:size(T.RDM,1)
[T.encodePCM(n,m)] = encode_crossval(Y(:,:,n),Xcond*X{m},part,'pcm_NR_diag','X',Xpart,'Gd',ones(numReg(m),1));
end;
case 'encodePCMcorr'
% Encoding model with regularisation
for n=1:size(T.RDM,1)
[~,T.encodePCMcorr(n,m)] = encode_crossval(Y(:,:,n),Xcond*X{m},part,'pcm_NR_diag','X',Xpart,'Gd',ones(numReg(m),1));
end;
case 'encodeRidge'
% Encoding model with regularisation
for n=1:size(T.RDM,1)
[T.encodeRidge(n,m)] = encode_crossval(Y(:,:,n),Xcond*X{m},part,'ridgeFixed','X',Xpart,'G',eye(numReg(m))*om,'sigma2',1);
end;
case 'encodeRidgeCorr'
% Encoding model with regularisation
for n=1:size(T.RDM,1)
[~,T.encodeRidgeCorr(n,m)] = encode_crossval(Y(:,:,n),Xcond*X{m},part,'ridgeFixed','X',Xpart,'G',eye(numReg(m))*om,'sigma2',1);
end;
end;
exetime(m,meth) = toc;
end;
fprintf('model %d done\n',m);
end;
% Now calculate the proportion of cases the correct model wins
N = size(T.truemodel,1);
for m=1:numMethods
meancorrect = nan(N,1);
for n=1:N
val = T.(Opt.methods{m})(n,:);
trueval = val(T.truemodel(n));
val(T.truemodel(n))=[];
meancorrect(n,:) = (sum(trueval>val) + ...
sum(trueval==val)*0.5)./(numModels-1);
end;
K.propCorr(m,1) = mean(meancorrect);
K.method(m,1) = m;
K.methodStr{m,1} = Opt.methods{m};
K.avrgTime(m,1) = mean(exetime(:,m));
end;
K.omega=ones(numMethods,1)*om;
L=addstruct(L,K);
U=addstruct(U,T);
end;
% Plot the results
lineplot(L.omega,L.propCorr,'split',L.method,'style_thickline','leg',Opt.methods);
ylabel('Proportion correct');
drawline(0.5,'dir','horz');
U.methods = Opt.methods;
T=L;
if (~isempty(Opt.outfile))
save(fullfile(baseDir,Opt.outfile),'T','U');
end;
varargout={U,T};
case 'modelCompare_Sigma_P'
% Simulation with spatially correlated noise
methods={'pearsonNc','cosine','pearsonWNc','cosineWNull','loglikPCM'};
[X,Y,Z]=meshgrid([1:6],[1:6],[1:6]); % Make a cube of voxels
C = [X(:) Y(:) Z(:)]; % Get coordinates
D = (C(:,1)-C(:,1)').^2 + (C(:,2)-C(:,2)').^2 + (C(:,3)-C(:,3)').^2;
for s = [0:5]
Sigma_P = exp(-D/s);
Sigma_P(isnan(Sigma_P))=1; % Make sure s=0 leads to indentity matrix
%out = sprintf('%s/sim_sigP_%d_Exp2a.mat',baseDir,s);
out = sprintf('sim_sigP_%d_Exp2a.mat',s);
rsa_testModelCompare('modelCompare','model','Model_chords.mat','numSim',10000,'outfile',out,'methods',methods,'Omega',0.15,'Sigma_P',Sigma_P,'numVox',216);
end
case 'calc_mean_correct'
criterion = varargin{1};
truemodel = varargin{2};
[N,numModels]=size(criterion);
meancorrect = nan(N,1);
for n=1:N
val = criterion(n,:);
trueval = val (truemodel(n));
val(truemodel(n))=[];
meancorrect(n,:) = (sum(trueval>val) + ...
sum(trueval==val)*0.5)./(numModels-1);
end;
varargout = {mean(meancorrect),meancorrect};
case 'simple_modelfit_plot'
doplot=[1 3 5 6 7 8];
linecolor={'b','b','b','g','r','r'};
linestyle={':','--','-','-',':','-'};
D=load('simple_model.mat');
methods = D.methods;
D=rmfield(D,'methods');
numMethods = length(methods);
L=[];
for om=unique(D.omega)'
T=getrow(D,D.omega==om);
s=(T.truemodel-1)*2-1;
for m=1:length(methods)
K.propCorr(m,1) = (sum((s.*diff(T.(methods{m}),[],2))>0)+...
sum(diff(T.(methods{m}),[],2)==0)*0.5) ...
./size(T.omega,1);
end;
K.omega=ones(numMethods,1)*om;
K.method=[1:numMethods]';
L=addstruct(L,K);
end;
% Plot the results
lineplot(L.omega,L.propCorr,'split',L.method,'style_thickline','leg',{methods{doplot}},...
'linecolor',linecolor,'linestyle',linestyle,...
'markercolor',linecolor,'markerfill',linecolor,...
'markersize',5,'errorbars','none','subset',ismember(L.method,doplot));
ylabel('Proportion correct');
drawline(0.5,'dir','horz');
varargout={L,D,methods};
case 'test_scalefit'
% This tests for different ways of doing a combined model fit
% across a number of subjects, each having a different scale
% of distances
M1 = sh1_getRDMmodelTau1([-2 1.5 -0.5 0],1,[1 2 4 6],'sqEuclidean');
M1.X = M1.RDM; % Design matrix
D.numExp = 50; % 100 Experiments
D.numSubj = 14; % Partitipants / experiment
D.numSim = D.numExp * D.numSubj;
D.omega = [0 0 0.6 0];
D.var_e = 20;
D.numPart = 8;
% D.phi = chi2rnd(10,D.numSim,1)/10;
models={[1 2 3 4],[2 3 4],[1 3 4],[1 2 3]};
% figure(1);
% rsa.fig.imageRDMs(M1);
corrN(M1.X')
[S,Y,D]=rsa_testModelFit('simulate_data',M1,D);
S.exp = kron([1:D.numExp]',ones(D.numSubj,1));
S.subj = kron(ones(D.numExp,1),[1:D.numSubj]');
for i=1:D.numExp
indx = find(S.exp==i);
fprintf('%d\n',i);
% for m=1:4
% S.R2(indx,1)=rsa_crossvalModel(getrow(M1,models{m}),S.RDM(indx,:),'method','OLS');
S.R2(indx,1)=rsa_crossvalModel(M1,S.RDM(indx,:),'method','OLS');
S.R2(indx,2)=rsa_crossvalModel(M1,S.RDM(indx,:),'method','NLS');
% S.R2(indx,3)=rsa_crossvalModel(M1,S.RDM(indx,:),'method','ScaleOLS');
% end;
end;
% S.R2d = bsxfun(@minus,S.R2(:,2:4),S.R2(:,1));
barplot([],S.R2);
varargout={S};
case 'test_pcm_fixed' % Test PCM with a fixed block effect
Opt.model = 'Model_fiveFinger.mat';
Opt.numSim = 10;
Opt.numPart = 3;
Opt.numVox = 40;
Opt.outfile = [];
Opt.Omega = 0.2;
U=[];
Opt=rsa.getUserOptions(varargin,Opt);
if (ischar(Opt.model))
load(fullfile(baseDir,Opt.model))
else
M=Opt.model;
end;
numModels = length(M);
% Experimental constants
test=rsa.rdm.squareRDM(M(1).RDM);
D.numCat = size(test,1);
D.var_e = 1; % Noise variance
D.numPart = Opt.numPart;
D.numVox = Opt.numVox;
D.numSim = Opt.numSim;
% Prep some convenience matrices
H = eye(D.numCat)-ones(D.numCat)/D.numCat; % Centering matrix
part = kron([1:D.numPart]',ones(D.numCat,1));
cond = kron(ones(D.numPart,1),[1:D.numCat]');
Xpart = indicatorMatrix('identity',part);
Xcond = indicatorMatrix('identity',cond);
% Prep the Variance components and regression matricies for each model
for m=1:numModels
% Prep variance components
if isfield(M{m},'IPM')
G{m}=rsa_squareIPM(M{m}.IPM);
else
RDM{m}=rsa.rdm.squareRDM(M{m}.RDM);
G{m}=-0.5*H*RDM{m}*H;
end;
% Get the design matrix for encoding models
[V,Lam]=eig(G{m});
[lambda{m},i]=sort(diag(Lam),'descend');
X{m}=bsxfun(@times,V(:,i),sqrt(lambda{m}'));
X{m}=X{m}(:,lambda{m}>eps);
numReg(m) = size(X{m},2);
end;
% Loop over different signal levels
for om=Opt.Omega;
D.omega = om;
fprintf('%2.3f\n',om);
T=[];
% Generate data from the two models
Y=[];
for m=1:numModels
[S,y,D]=rsa_testModelCompare('simulate_data',M{m},D);
S.truemodel=ones(D.numSim,1)*m;
T=addstruct(T,S);
Y=cat(3,Y,y);
end;
T.omega = ones(D.numSim*numModels,1)*om;
for m=1:numModels
for n=1:size(Y,3)
[G1,theta,~,l]=pcm_NR_diag(Y(:,:,n),Xcond*X{m},'Gd',ones(numReg(m),1),'X',Xpart,'hP',0);
T.loglik_NRdiag(n,m) = l(1);
T.signal_NRdiag(n,m)=theta(1);
T.noise_NRdiag(n,m)=theta(2);
[G3,theta2,~,l]=pcm_minimize(Y(:,:,n),Xcond,'Gc',{G{m}},'X',Xpart,'MaxIteration',1000);
T.loglik_min(n,m) = l(1);
T.signal_min(n,m)=theta2(1);
T.noise_min(n,m)=theta2(2);
[G2,theta1,~,l]=pcm_NR(Y(:,:,n),Xcond,'Gc',{G{m}},'X',Xpart,'hP',0);
T.loglik_NR(n,m) = l(1);
T.signal_NR(n,m)=theta1(1);
T.noise_NR(n,m)=theta1(2);
end;
fprintf('model %d done\n',m);
end;
% Now calculate the proportion of cases the correct model wins
U=addstruct(U,T);
end;
varargout={U};
case 'test_pcm' % Test PCM for comparisons
M1 = sh1_getRDMmodelTau1([-2 1.5 -1 0],1,[1 2 4 6],'sqEuclidean');
M1.X = M1.RDM; % Design matrix
D.numExp = 1; % 100 Experiments
D.numSubj = 5; % 12 Partitipants
D.numSim = D.numExp * D.numSubj;
% D.omega = zeros(1,size(M1.RDM,1));
D.omega = [0 0 0.6 0];
D.var_e = 80;
D.numPart = 8;
ModelComp={1,2,3,4,[1 2 3 4],[2 3 4],[1 3 4],[1 2 4],[1 2 3]};
% figure(1);
% rsa.fig.imageRDMs(M1);
corrN(M1.X');
[S,Y,D,~,cond,part]=rsa_testModelFit('simulate_data',M1,D);
S.exp = kron([1:D.numExp]',ones(D.numSubj,1));
S.subj = kron(ones(D.numExp,1),[1:D.numSubj]');
for i=1:D.numExp.*D.numSubj
for m=1:length(ModelComp)
M2=getrow(M1,ModelComp{m});
[S.logEv(i,m)]=rsa_fitModelPCM(M2,Y(:,:,i),cond,part);
end;
end;
keyboard;
varargout={S};
case 'encodeReg'
% Test different dimensionalties of encoding models
% rsa_testModelCompare('encodeReg','model','Model_fiveFinger.mat','numSim',1000,'outfile','sim_encodeReg_Exp1.mat');
% rsa_testModelCompare('encodeReg','model','Model_fiveFinger.mat','numSim',50,'method','encodePCM','outfile','sim_encodePCM_Exp1.mat');
% rsa_testModelCompare('encodeReg','model','Model_chords.mat','numSim',100,'outfile','sim_encodeReg_Exp2.mat','numReg',[1:8 10:2:20 25 30],'Omega',0.1);
% rsa_testModelCompare('encodeReg','model','Model_chords.mat','numSim',10,'method','encodePCM','outfile','sim_encodePCM_Exp2.mat','numReg',[1:8 10:2:20 25 30],'Omega',0.1);
% rsa_testModelCompare('encodeReg','model','Model_chords.mat','numSim',10,'method','encodeRidge','outfile','sim_encodeRidge_Exp2.mat','numReg',[1:8 10:2:20 25 30],'Omega',0.1);
% rsa_testModelCompare('encodeReg','model','START_compl.mat','numSim',10,'outfile','sim_encodeReg_Exp3.mat','numReg',[1 5:5:30 40:10:90]);
% rsa_testModelCompare('encodeReg','model','START_compl.mat','numSim',10,'method','encodePCM','outfile','sim_encodePCM_Exp3.mat','numReg',[1 5:5:30 40:10:90]);
Opt.model = 'Model_fiveFinger.mat';
% Opt.outfile = 'sim_encodeReg_Exp1.mat';
Opt.outfile = [];
Opt.numSim = 100;
Opt.numPart = 8;
Opt.numVox = 160;
Opt.Omega = 0.3;
Opt.numReg = []; % Number of regressors tested
Opt.method = 'encodeReg';
Opt=rsa.getUserOptions(varargin,Opt);
L=[]; % Summary stats
U=[]; % details on each simulation
if (ischar(Opt.model))
load(fullfile(baseDir,Opt.model))
else
M=Opt.model;
end;
numModels = length(M);
% Experimental constants
test=rsa.rdm.squareRDM(M(1).RDM);
D.numCat = size(test,1);
D.var_e = 1; % Noise variance
D.numPart = Opt.numPart;
D.numVox = Opt.numVox;
D.numSim = Opt.numSim;
% Prep some convenience matrices
H = eye(D.numCat)-ones(D.numCat)/D.numCat; % Centering matrix
part = kron([1:D.numPart]',ones(D.numCat,1));
cond = kron(ones(D.numPart,1),[1:D.numCat]');
Xpart = indicatorMatrix('identity',part);
Xcond = indicatorMatrix('identity',cond);
% Prep the Variance components and regression matricies for each model
for m=1:numModels
% Prep variance components
RDM{m}=rsa.rdm.squareRDM(M{m}.RDM);
G{m}=-0.5*H*RDM{m}*H;
% Get the design matrix for encoding models
[V,Lam]=eig(G{m});
[lambda{m},i]=sort(diag(Lam),'descend');
X{m}=bsxfun(@times,V(:,i),sqrt(lambda{m}'));
X{m}=X{m}(:,lambda{m}>eps);
end;
if (isempty(Opt.numReg))
Opt.numReg = [1:size(X{1},2)]; % Number of regressors
end;
% Loop over different signal levels
for om=Opt.Omega;
D.omega = om;
fprintf('%2.3f\n',om);
T=[];
% Generate data from the two models
Y=[];
for m=1:numModels
[S,y,D]=rsa_testModelFit('simulate_data',M{m},D);
S.truemodel=ones(D.numSim,1)*m;
T=addstruct(T,S);
Y=cat(3,Y,y);
end;
T.omega = ones(D.numSim*numModels,1)*om;
for m=1:numModels
% Calculate the different forms of correlation,
% Encoding model without regularisation
for n=1:size(T.RDM,1)
if (mod(n,ceil(Opt.numSim/20))==0);
fprintf('.');
end;
for v=Opt.numReg
switch(Opt.method)
case 'encodeReg'
[T.(sprintf('R2_%2.2d',v))(n,m),~,~,T.(sprintf('corr_%2.2d',v))(n,m)]= ...
encode_crossval(Y(:,:,n),Xcond*X{m}(:,1:v),part,'linregress','X',Xpart);
case 'encodePCM'
[T.(sprintf('R2_%2.2d',v))(n,m),~,~,T.(sprintf('corr_%2.2d',v))(n,m)]= ...
encode_crossval(Y(:,:,n),Xcond*X{m}(:,1:v),part,'pcm_NR_diag','X',Xpart,'Gd',ones(v,1));
case 'encodeRidge'
[T.(sprintf('R2_%2.2d',v))(n,m),~,~,T.(sprintf('corr_%2.2d',v))(n,m)]= ...
encode_crossval(Y(:,:,n),Xcond*X{m}(:,1:v),part,'ridgeFixed','X',Xpart,'G',eye(v)*om,'sigma2',D.var_e);
end;
end;
end;
fprintf('model %d done\n',m);
end;
% Now calculate the proportion of cases the correct model wins
N = size(T.truemodel,1);
for i=1:length(Opt.numReg)
name1=sprintf('R2_%2.2d',Opt.numReg(i));
name2=sprintf('corr_%2.2d',Opt.numReg(i));
for n=1:N
val1 = T.(name1)(n,:);
trueval1 = val1(T.truemodel(n));
val1(T.truemodel(n))=[];
meancorrect1(n,:) = (sum(trueval1>val1) + ...
sum(trueval1==val1)*0.5)./(numModels-1);
val2 = T.(name2)(n,:);
trueval2 = val2(T.truemodel(n));
val2(T.truemodel(n))=[];
meancorrect2(n,:) = (sum(trueval2>val2) + ...
sum(trueval2==val2)*0.5)./(numModels-1);
trueVal(n,:) = [trueval1 trueval2];
end;
K.numReg(i,1) = Opt.numReg(i);
K.propCorrR2(i,1) = mean(meancorrect1);
K.propCorrCorr(i,1) = mean(meancorrect2);
K.meanR2(i,:) = mean(trueVal(:,1));
K.meanCorr(i,:) = mean(trueVal(:,2));
end;
K.omega=ones(length(Opt.numReg),1)*om;
L=addstruct(L,K);
U=addstruct(U,T);
end;
T=L;
subplot(3,1,1);
lineplot(T.numReg,T.meanR2,'style_thickline');
drawline(0,'dir','horz');
subplot(3,1,2);
lineplot(T.numReg,T.meanCorr,'style_thickline');
drawline(0,'dir','horz');
subplot(3,1,3);
lineplot(T.numReg,[T.propCorrR2 T.propCorrCorr],'style_thickline','leg',{'R2','Corr'});
drawline(0.5,'dir','horz');
varargout={T,U};
if (~isempty(Opt.outfile))
save(fullfile(baseDir,Opt.outfile),'T','U');
end;
case 'encodeRegularise'
% Test different dimensionalties of encoding models
% rsa_testModelCompare('encodeRegularise','numSim',10000,'outfile','sim_encodeRegularise_Exp2.mat');
Opt.model = 'Model_chords.mat';
Opt.outfile = [];
Opt.numSim = 30;
Opt.numPart = 8;
Opt.numVox = 160;
Opt.Omega = 0.2;
Opt.Omega_hat = [0.01 0.05:0.05:0.5];
Opt=rsa.getUserOptions(varargin,Opt);
L=[]; % Summary stats
U=[]; % details on each simulation
if (ischar(Opt.model))
load(fullfile(baseDir,Opt.model))
else
M=Opt.model;
end;
numModels = length(M);
% Experimental constants
test=rsa.rdm.squareRDM(M(1).RDM);
D.numCat = size(test,1);
D.var_e = 1; % Noise variance
D.numPart = Opt.numPart;
D.numVox = Opt.numVox;
D.numSim = Opt.numSim;
% Prep some convenience matrices
H = eye(D.numCat)-ones(D.numCat)/D.numCat; % Centering matrix
part = kron([1:D.numPart]',ones(D.numCat,1));
cond = kron(ones(D.numPart,1),[1:D.numCat]');
Xpart = indicatorMatrix('identity',part);
Xcond = indicatorMatrix('identity',cond);
% Prep the Variance components and regression matricies for each model
for m=1:numModels
% Prep variance components
RDM{m}=rsa.rdm.squareRDM(M{m}.RDM);
G{m}=-0.5*H*RDM{m}*H;
% Standarsize ???
% Get the design matrix for encoding models
[V,Lam]=eig(G{m});
[lambda{m},i]=sort(diag(Lam),'descend');
X{m}=bsxfun(@times,V(:,i),sqrt(lambda{m}'));
X{m}=X{m}(:,lambda{m}>eps);
end;
% Loop over different regularisation levels
D.omega = Opt.Omega;
for om=Opt.Omega_hat;
D.omega_hat=om;
fprintf('%2.3f\n',om);
T=[];
% Generate data from the two models
Y=[];
for m=1:numModels
[S,y,D]=rsa_testModelFit('simulate_data',M{m},D);
S.truemodel=ones(D.numSim,1)*m;
T=addstruct(T,S);
Y=cat(3,Y,y);
end;
T.omega_hat = ones(D.numSim*numModels,1)*om;
for m=1:numModels
% Calculate the different forms of correlation,
% Encoding model without regularisation
for n=1:size(T.RDM,1)
if (mod(n,ceil(Opt.numSim/20))==0);
fprintf('.');
end;
[T.R2(n,m),~,~,T.corr(n,m)]= encode_crossval(Y(:,:,n),Xcond*X{m},part,'ridgeFixed','X',Xpart,'G',eye(size(X{m},2))*D.omega_hat,'sigma2',D.var_e);
end;
fprintf('model %d done\n',m);
end;
% Now calculate the proportion of cases the correct model wins
N = size(T.truemodel,1);
for n=1:N
val1 = T.R2(n,:);
trueval1 = val1(T.truemodel(n));
val1(T.truemodel(n))=[];
meancorrect1(n,:) = (sum(trueval1>val1) + ...
sum(trueval1==val1)*0.5)./(numModels-1);
val2 = T.corr(n,:);
trueval2 = val2(T.truemodel(n));
val2(T.truemodel(n))=[];
meancorrect2(n,:) = (sum(trueval2>val2) + ...
sum(trueval2==val2)*0.5)./(numModels-1);
trueVal(n,:) = [trueval1 trueval2];
end;
K.omega_hat = om;
K.propCorrR2 = mean(meancorrect1);
K.propCorrCorr = mean(meancorrect2);
K.meanR2 = mean(trueVal(:,1));
K.meanCorr = mean(trueVal(:,2));
K.omega=Opt.Omega;
L=addstruct(L,K);
U=addstruct(U,T);
end;
T=L;
subplot(3,1,1);
lineplot(T.omega_hat,T.meanR2,'style_thickline');
drawline(0,'dir','horz');
subplot(3,1,2);
lineplot(T.omega_hat,T.meanCorr,'style_thickline');
drawline(0,'dir','horz');
subplot(3,1,3);
lineplot(T.omega_hat,[T.propCorrR2 T.propCorrCorr],'style_thickline','leg',{'R2','Corr'});
drawline(0.5,'dir','horz');
varargout={T,U};
if (~isempty(Opt.outfile))
save(fullfile(baseDir,Opt.outfile),'T','U');
end;
case 'use_intercept'
% M{1}.RDM = [1 2 2];
% M{2}.RDM = [1 4 4];
% M{1}.RDM = [1 2 1];
% M{2}.RDM = [1 4 1];
%M{1}.RDM = [1 2 2 2 2 1];
% M{2}.RDM = [1 4 4 4 4 1];
M{1}.RDM = [0 2 2 2 2 1];
M{2}.RDM = [0 4 4 4 4 1];
nC =4;
H=eye(nC)-ones(nC)/nC;
for i=1:2
M{i}.type = 'fixed';
M{i}.numGparams = 0;
M{i}.Gc = -0.5*H*squareform(M{i}.RDM)*H;
end;
case 'Util_summarizeFiles'
ex = varargin{1};
filesNames=varargin{2};
T=[];
S=[];
D=[];
% Load RSA files
for f=1:length(filesNames);
files=dir(sprintf('sim_%s_Exp%d*',filesNames{f},ex));
if (~isempty(files))
for i=1:length(files)
R=load(files(i).name);
[Num,om]=pivottable(R.U.omega,[],R.U.sig_hat,'length');
for o=1:length(om)
R.T.numSim(R.T.omega==om(o),1)=Num(o);
end;
T=addstruct(T,R.T);
end;
T.omega = round(T.omega,3);
S=tapply(T,{'method','methodStr','omega'},{T.propCorr.*T.numSim,'sum','name','numCorr'},{T.numSim,'sum','name','numSim'});
S.propCorr = S.numCorr ./ S.numSim;
D=addstruct(D,S);
end;
end;
varargout={D};
case 'Util_relabelMethods'
D=varargin{1};
methodStr=varargin{2};
D.method = zeros(size(D.method));
for i=1:length(methodStr)
a=strcmp(methodStr{i},D.methodStr);
D.method(a)=i;
end;
varargout={D};
case 'Figure_rsaVarianceLDC'
cd(baseDir);
load(fullfile(baseDir,'Model_fiveFinger.mat'));
subplot(3,2,1);
rsa.fig.imageRDMs(M{1},'transformfcn','ssqrt','singleRDM',1,'colourScheme',gray);
subplot(3,2,3);
rsa.fig.imageRDMs(M{2},'transformfcn','ssqrt','singleRDM',1,'colourScheme',gray);
load(fullfile(baseDir,'Model_chords.mat'))
subplot(3,2,2);
rsa.fig.imageRDMs(M{1},'transformfcn','ssqrt','singleRDM',1,'colourScheme',gray);
subplot(3,2,4);
rsa.fig.imageRDMs(M{2},'transformfcn','ssqrt','singleRDM',1,'colourScheme',gray);
for ex=1:2
T=[];
S=[];
% Load RSA files
files=dir(sprintf('sim_rsa_Exp%d*',ex));
for i=1:length(files)
R=load(files(i).name);
[Num,om]=pivottable(R.U.omega,[],R.U.sig_hat,'length');
for o=1:length(om)
R.T.numSim(R.T.omega==om(o),1)=Num(o);
end;
T=addstruct(T,R.T);
end;
T.omega = round(T.omega,3);
D=tapply(T,{'method','methodStr','omega'},...
{T.propCorr.*T.numSim,'sum','name','numCorr'},...
{T.numSim,'sum','name','numSim'},...
'subset',T.method~=2);
D.propCorr = D.numCorr ./ D.numSim;
subplot(3,2,ex+4);
color={[0.6 0.6 0.6],[0 0 0],[0 0 0]};
lineplot(D.omega,D.propCorr,'split',D.methodStr,'linecolor',color,...
'markercolor',color,'linestyle',{'-',':','-'},'linewidth',2,...
'leg','auto','errorfcn',[]);
set(gca,'YLim',[0.4 1]);
drawline(0.5,'dir','horz');
end;
set(gcf,'PaperPosition',[0 0 6 10]);
wysiwyg;
case 'Figure_rsa_pcm'
filesNames={'rsa','pcm'};
cd(baseDir);
for ex=1:3
T=[];
S=[];
% Load RSA files
for f=1:2
files=dir(sprintf('sim_%s_Exp%d*',filesNames{f},ex));
for i=1:length(files)
R=load(files(i).name);
[Num,om]=pivottable(R.U.omega,[],R.U.sig_hat,'length');
for o=1:length(om)
R.T.numSim(R.T.omega==om(o),1)=Num(o);
end;
T=addstruct(T,R.T);
end;
T.omega = round(T.omega,3);
D=tapply(T,{'method','methodStr','omega'},{T.propCorr.*T.numSim,'sum','name','numCorr'},{T.numSim,'sum','name','numSim'});
D.propCorr = D.numCorr ./ D.numSim;
S=addstruct(S,D);
end;
subplot(1,3,ex);
if (ex==1)
lineplot(D.omega,D.propCorr,'split',D.methodStr,'style_thickline','leg','auto','subset',~strcmp(D.methodStr,'loglikPCM'),'errorfcn',[]);
else
lineplot(D.omega,D.propCorr,'split',D.methodStr,'style_thickline','subset',~strcmp(D.methodStr,'loglikPCM'),'errorfcn',[]);
end;
hold on;
lineplot(D.omega,D.propCorr,'split',D.methodStr,'linestyle',':','linecolor','k','subset',strcmp(D.methodStr,'loglikPCM'),'errorfcn',[]);