function [pval, t_orig, tmx_ptile, seed_state, est_alpha]=mxwt_perm2(dataA,dataB,n_perm,alph,tail,verblevel,seed_state) mxwt_perm2-Two sample permutation test based on Welch's t-statistic and null hypothesis of a mean difference of zero. For one-tailed tests, the null distribution is derived from the most extreme Welch's t-score of all permutations. For two-tailed tests, the null distribution is derived from the maximum absolute Welch's t-score of all permutations. This function can handle multiple electrodes and time points. Based on simulated ERP data sets, Welch's t appears to be less sensitive to differences in variance between groups than the standard independent samples t-statistic (esp. when group sizes are unequal). However, it is probably less powerful than the standard t-statistic. Thus you should probably use this function if the numbers of participants in each group are not equal and you suspect that one group is more variable (from trial to trail or participant to participant) than the other. Note, "Welch's t" is also referred to as the "Aspin-Welch t". See Murphy (1967) or Zar (1999) for the formula. Inputs: dataA - 3D matrix of ERPs from Group A (Channel x Time x Participant) dataB - 3D matrix of ERPs from Group B (Channel x Time x Participant) n_perm - number of permutations {default=1000}. Manly (1997) suggests using at least 1000 permutations for an alpha level of 0.05 and at least 5000 permutations for an alpha level of 0.01. alph - test alpha level {default=.05} tail - [1 | 0 | -1] If tail=1, the alternative hypothesis is that the mean of data is greater than 0 (upper tailed test). If tail=0, the alternative hypothesis is that the mean of data is different than 0 (two tailed test). If tail=-1, the alternative hypothesis is that the mean of the data is less than 0 (lower tailed test). {default: 0} Optional Inputs: verblevel - An integer specifiying the amount of information you want this function to provide about what it is doing during runtime. Options are: 0 - quiet, only show errors, warnings, and EEGLAB reports 1 - stuff anyone should probably know 2 - stuff you should know the first time you start working with a data set {default value} 3 - stuff that might help you debug (show all reports) seed_state - The initial state of the random number generating stream (see MATLAB documentation for "randstream"). If you pass a value from a previous run of this function, it should reproduce the exact same values. Outputs: pval - p-value at each time point and electrode (corrected for multiple comparisons via permutation test) t_orig - Welch's t-score at each time point and electrode tmx_ptile - critical t-scores for given alpha level seed_state - The initial state of the random number generating stream (see MATLAB documentation for "randstream") used to generate the permutations. You can use this to reproduce the output of this function. est_alpha - The estimated family-wise alpha level of the test. With permutation tests, a finite number of p-values are possible. This function tries to use an alpha level that is as close as possible to the desired alpha level. However, if the sample size is small, a very limited number of p-values are possible and the desired family-wise alpha level may be impossible to acheive. Notes: -The null hypothesis of this test is that the data in the two groups are "exchangeable" (i.e., the data from a participant in Group A was just as likely to have come from Group B). If the distributions of the two populations you're comparing differ in *any* way (i.e., not just in central tendency), the null hypothesis will be violated. For example, if the ERPs of the two groups differ in their variability, but not their average value (e.g., one set of ERPs is from a group of children and the other adults), you'll be more likely than alpha to reject the null hypothesis. Using Welch's t ameliorates the test's sensitivity to between-group differences in variance, but it does not completely remove it. -Unlike a parametric test (e.g., an ANOVA), a discrete set of p-values are possible (at most the number of possible permutations). Since the number of possible permutations grows rapdily with the number of participants, this is only an issue for small sample sizes (e.g., 3 participants in each group). When you have such a small sample size, the limited number of p-values may make the test overly conservative (e.g., you might be forced to use an alpha level of .0286 since it is the biggest possible alpha level less than .05). Author: David Groppe July, 2009 Kutaslab, San Diego References: Murphy, B. P. (1967). Some two-sample tests when the variances are unequal: A simulation study. Biometrika, 54(3), 679-683. Zar, J. H. (1999). Biostatistical analysis (fourth ed.). Upper Saddle River, New Jersey: Prentice Hall.

- VerbReport VerbReport() - Outputs messages if they exceed a desired level of importance.
- watchit function watchit(msg)

0001 %function [pval, t_orig, tmx_ptile, seed_state, est_alpha]=mxwt_perm2(dataA,dataB,n_perm,alph,tail,verblevel,seed_state) 0002 % 0003 % mxwt_perm2-Two sample permutation test based on Welch's t-statistic and null 0004 % hypothesis of a mean difference of zero. For one-tailed tests, the null 0005 % distribution is derived from the most extreme Welch's t-score of all 0006 % permutations. For two-tailed tests, the null distribution is derived from 0007 % the maximum absolute Welch's t-score of all permutations. This function 0008 % can handle multiple electrodes and time points. Based on simulated ERP 0009 % data sets, Welch's t appears to be less sensitive to differences in 0010 % variance between groups than the standard independent samples t-statistic 0011 % (esp. when group sizes are unequal). However, it is probably less powerful 0012 % than the standard t-statistic. Thus you should probably use this function 0013 % if the numbers of participants in each group are not equal and you suspect 0014 % that one group is more variable (from trial to trail or participant to 0015 % participant) than the other. Note, "Welch's t" is also referred to as the 0016 % "Aspin-Welch t". See Murphy (1967) or Zar (1999) for the formula. 0017 % 0018 % Inputs: 0019 % dataA - 3D matrix of ERPs from Group A (Channel x Time x Participant) 0020 % dataB - 3D matrix of ERPs from Group B (Channel x Time x Participant) 0021 % n_perm - number of permutations {default=1000}. Manly (1997) suggests 0022 % using at least 1000 permutations for an alpha level of 0.05 and 0023 % at least 5000 permutations for an alpha level of 0.01. 0024 % alph - test alpha level {default=.05} 0025 % tail - [1 | 0 | -1] If tail=1, the alternative hypothesis is that the 0026 % mean of data is greater than 0 (upper tailed test). If tail=0, 0027 % the alternative hypothesis is that the mean of data is different 0028 % than 0 (two tailed test). If tail=-1, the alternative hypothesis 0029 % is that the mean of the data is less than 0 (lower tailed test). 0030 % {default: 0} 0031 % 0032 % Optional Inputs: 0033 % verblevel - An integer specifiying the amount of information you want 0034 % this function to provide about what it is doing during runtime. 0035 % Options are: 0036 % 0 - quiet, only show errors, warnings, and EEGLAB reports 0037 % 1 - stuff anyone should probably know 0038 % 2 - stuff you should know the first time you start working 0039 % with a data set {default value} 0040 % 3 - stuff that might help you debug (show all 0041 % reports) 0042 % seed_state - The initial state of the random number generating stream 0043 % (see MATLAB documentation for "randstream"). If you pass 0044 % a value from a previous run of this function, it should 0045 % reproduce the exact same values. 0046 % 0047 % Outputs: 0048 % pval - p-value at each time point and electrode (corrected for 0049 % multiple comparisons via permutation test) 0050 % t_orig - Welch's t-score at each time point and electrode 0051 % tmx_ptile - critical t-scores for given alpha level 0052 % seed_state - The initial state of the random number generating stream 0053 % (see MATLAB documentation for "randstream") used to 0054 % generate the permutations. You can use this to reproduce 0055 % the output of this function. 0056 % est_alpha - The estimated family-wise alpha level of the test. With 0057 % permutation tests, a finite number of p-values are possible. 0058 % This function tries to use an alpha level that is as close 0059 % as possible to the desired alpha level. However, if the 0060 % sample size is small, a very limited number of p-values are 0061 % possible and the desired family-wise alpha level may be 0062 % impossible to acheive. 0063 % 0064 % Notes: 0065 % -The null hypothesis of this test is that the data in the two groups are 0066 % "exchangeable" (i.e., the data from a participant in Group A was just as 0067 % likely to have come from Group B). If the distributions of the two 0068 % populations you're comparing differ in *any* way (i.e., not just in 0069 % central tendency), the null hypothesis will be violated. For example, if 0070 % the ERPs of the two groups differ in their variability, but not their 0071 % average value (e.g., one set of ERPs is from a group of children and the 0072 % other adults), you'll be more likely than alpha to reject the null 0073 % hypothesis. Using Welch's t ameliorates the test's sensitivity to 0074 % between-group differences in variance, but it does not completely remove 0075 % it. 0076 % 0077 % -Unlike a parametric test (e.g., an ANOVA), a discrete set of p-values 0078 % are possible (at most the number of possible permutations). Since the 0079 % number of possible permutations grows rapdily with the number of 0080 % participants, this is only an issue for small sample sizes (e.g., 3 0081 % participants in each group). When you have such a small sample size, the 0082 % limited number of p-values may make the test overly conservative (e.g., 0083 % you might be forced to use an alpha level of .0286 since it is the biggest 0084 % possible alpha level less than .05). 0085 % 0086 % Author: 0087 % David Groppe 0088 % July, 2009 0089 % Kutaslab, San Diego 0090 % 0091 % References: 0092 % 0093 % Murphy, B. P. (1967). Some two-sample tests when the variances are unequal: 0094 % A simulation study. Biometrika, 54(3), 679-683. 0095 % 0096 % Zar, J. H. (1999). Biostatistical analysis (fourth ed.). Upper Saddle 0097 % River, New Jersey: Prentice Hall. 0098 0099 %%%%%%%%%%%%%%%% REVISION LOG %%%%%%%%%%%%%%%%% 0100 % 0101 0102 0103 %%%%%%%%%%%%%%%% FUTURE IMPROVEMENTS %%%%%%%%%%%%%%%%% 0104 % 0105 % Test for equality of trials or variance? 0106 0107 function [pval, t_orig, tmx_ptile, seed_state, est_alpha]=mxwt_perm2(dataA,dataB,n_perm,alph,tail,verblevel,seed_state) 0108 0109 if nargin<2, 0110 error('You need to provide data for two groups of participants.'); 0111 end 0112 0113 if nargin<3, 0114 n_perm=2000; 0115 end 0116 0117 if nargin<4, 0118 alph=.05; 0119 elseif (alph>=1) || (alph<=0) 0120 error('Argument ''alph'' needs to be between 0 and 1.'); 0121 end 0122 0123 if alph<=.01 && n_perm<5000, 0124 watchit(sprintf('You are probably using too few permutations for an alpha level of %f. Type ">>help mxwt_perm2" for more info.',alph)); 0125 elseif alph<=.05 && n_perm<1000, 0126 watchit(sprintf('You are probably using too few permutations for an alpha level of %f. Type ">>help mxwt_perm2" for more info.',alph)); 0127 end 0128 0129 if nargin<5, 0130 tail=0; 0131 elseif (tail~=0) && (tail~=1) && (tail~=-1), 0132 error('Argument ''tail'' needs to be 0,1, or -1.'); 0133 end 0134 0135 if nargin<6, 0136 verblevel=2; 0137 end 0138 0139 defaultStream=RandStream.getDefaultStream; %random # generator state 0140 if (nargin<7) || isempty(seed_state), 0141 %Store state of random number generator 0142 seed_state=defaultStream.State; 0143 else 0144 defaultStream.State=seed_state; %reset random number generator to saved state 0145 end 0146 0147 0148 if length(size(dataA))~=3 || length(size(dataB))~=3 0149 error('dataA and dataB need to be three dimensional (chan x time x participant)') 0150 end 0151 [n_chan, n_tpt, n_subsA]=size(dataA); 0152 [n_chan2, n_tpt2, n_subsB]=size(dataB); 0153 0154 if verblevel, 0155 warning('off','all'); %for large # of subjects, nchoosek warns that its result is approximate 0156 n_psbl_prms=nchoosek(n_subsA+n_subsB,n_subsA); 0157 if n_psbl_prms<100, 0158 watchit(sprintf(['Due to the very limited number of participants in each group,' ... 0159 ' the total number of possible permutations is small.\nThus only a limited number of p-values (at most %d) are possible and the test might be overly conservative.'], ... 0160 n_psbl_prms)); 0161 end 0162 warning('on','all'); 0163 end 0164 0165 if n_chan~=n_chan2, 0166 error('The number of channels in Group 1 (dataA) and Group 2 (dataB) need to be equal.'); 0167 elseif n_tpt~=n_tpt2, 0168 error('The number of time points in Group 1 (dataA) and Group 2 (dataB) need to be equal.'); 0169 end 0170 %combine data 0171 total_subs=n_subsA+n_subsB; 0172 data=dataA; 0173 data(:,:,(n_subsA+1):total_subs)=dataB; 0174 0175 if verblevel~=0, 0176 fprintf('mxt_perm2: Number of channels: %d\n',n_chan); 0177 fprintf('mxt_perm2: Number of time points: %d\n',n_tpt); 0178 fprintf('mxt_perm2: Total # of comparisons: %d\n',n_chan*n_tpt); 0179 fprintf('mxt_perm2: Number of participants in Group A: %d\n',n_subsA); 0180 fprintf('mxt_perm2: Number of participants in Group B: %d\n',n_subsB); 0181 %fprintf('t-score degrees of freedom: %d\n',total_subs-2); %edit for Welch's t? 0182 end 0183 VerbReport(sprintf('Executing permutation test with %d permutations...',n_perm),2,verblevel); 0184 if (verblevel>=2), 0185 fprintf('Permutations completed: '); 0186 end 0187 0188 mx_t=zeros(1,n_perm); 0189 for perm=1:n_perm 0190 if ~rem(perm,100) 0191 if (verblevel>=2), 0192 if ~rem(perm-100,1000) 0193 fprintf('%d',perm); 0194 else 0195 fprintf(', %d',perm); 0196 end 0197 if ~rem(perm,1000) 0198 fprintf('\n'); 0199 end 0200 end 0201 end 0202 %randomly assign participants to conditions 0203 r=randperm(total_subs); 0204 grp1=r(1:n_subsA); 0205 grp2=r((n_subsA+1):total_subs); 0206 %compute most extreme Welch's t-score 0207 mx_t(perm)=wtmax2(data,grp1,grp2,n_subsA,n_subsB); 0208 end 0209 0210 %End of permutations, print carriage return if it hasn't already been done 0211 %(i.e., perm is NOT a multiple of 1000) 0212 if (verblevel>=2) && rem(perm,1000) 0213 fprintf('\n'); 0214 end 0215 0216 %Compute critical Welch's t(s) 0217 if tail==0, 0218 %two-tailed, test statistic is biggest absolute value of all t's 0219 mx_t=abs(mx_t); 0220 tmx_ptile(2)=prctile(mx_t,100-100*alph); 0221 tmx_ptile(1)=-tmx_ptile(2); 0222 est_alpha=mean(mx_t>=tmx_ptile(2)); 0223 elseif tail==1, 0224 %upper tailed 0225 tmx_ptile=prctile(mx_t,100-100*alph); 0226 est_alpha=mean(mx_t>=tmx_ptile); 0227 else 0228 %tail=-1, lower tailed 0229 tmx_ptile=prctile(mx_t,alph*100); 0230 est_alpha=mean(mx_t<=tmx_ptile); 0231 end 0232 if verblevel~=0, 0233 fprintf('Desired family-wise alpha level: %f\n',alph); 0234 fprintf('Estimated actual family-wise alpha level: %f\n',est_alpha); 0235 end 0236 0237 %Compute t-scores of actual observations 0238 [dummy t_orig]=wtmax2(data,1:n_subsA,(n_subsA+1):total_subs,n_subsA,n_subsB); 0239 pval=zeros(n_chan,n_tpt); 0240 %compute p-values 0241 for t=1:n_tpt, 0242 for c=1:n_chan, 0243 if tail==0, 0244 pval(c,t)=mean(mx_t>=abs(t_orig(c,t))); %note mx_t are now all positive due to abs command above 0245 elseif tail==1, 0246 pval(c,t)=mean(mx_t>=t_orig(c,t)); 0247 elseif tail==-1, 0248 pval(c,t)=mean(mx_t<=t_orig(c,t)); 0249 end 0250 end 0251 end 0252 0253 0254 function [mx_t, all_t]=wtmax2(dat,grpA,grpB,n_subsA,n_subsB) 0255 % might make this faster by moving it into the code 0256 0257 xA=dat(:,:,grpA); 0258 xB=dat(:,:,grpB); 0259 0260 smA=sum(xA,3); 0261 mnA=smA/n_subsA; 0262 varA=(sum(xA.^2,3)-smA.^2/n_subsA)/(n_subsA-1); 0263 0264 smB=sum(xB,3); 0265 mnB=smB/n_subsB; 0266 varB=(sum(xB.^2,3)-smB.^2/n_subsB)/(n_subsB-1); 0267 0268 all_t=(mnA-mnB)./sqrt( (varA/n_subsA) + (varB/n_subsB)); 0269 [mx1 id_row]=max(abs(all_t)); 0270 [mx2 id_col]=max(mx1); 0271 mx_t=all_t(id_row(id_col),id_col); 0272

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