Hi All,
I submitted the function repanova.m (in statistics) a while back,
and have since found an error in it's calculation of statistical
significance. I have corrected that error and attached is a diff for
that file. Please let me know what I can do to help integrate this
correction.
Thanks,
Kyle
Index: repanova.m
===================================================================
--- repanova.m (revision 9052)
+++ repanova.m (working copy)
@@ -33,60 +33,68 @@
function [p, table, st] = repanova(varargin)
- if (nargin == 2)
- X = varargin{1};
- condition = varargin{2};
- option = 'cell';
- elseif (nargin == 3)
- X = varargin{1};
- condition = varargin{2};
- option = varargin{3};
- else
- print_usage;
- endif
+switch nargin
+ case 0
+ error('Too few inputs.');
+ case 1
+ X = varargin{1};
+ for c = 1:size(X, 2)
+ condition{c} = ['time', num2str(c)];
+ end
+ option = 'cell';
+ case 2
+ X = varargin{1};
+ condition = varargin{2};
+ option = 'cell';
+ case 3
+ X = varargin{1};
+ condition = varargin{2};
+ option = varargin{3};
+ otherwise
+ error('Too many inputs.');
+end
+ % Find the means of the subjects and measures, ignoring any NaNs
+ u_subjects = nanmean(X,2);
+ u_measures = nanmean(X,1);
+ u_grand = nansum(nansum(X)) / (size(X,1) * size(X,2));
+ % Differences between rows will be reflected in SS subjects, differences
+ % between columns will be reflected in SS_within subjects.
+ N = size(X,1); % number of subjects
+ J = size(X,2); % number of samples per subject
+ SS_measures = N * nansum((u_measures - u_grand).^2);
+ SS_subjects = J * nansum((u_subjects - u_grand).^2);
+ SS_total = nansum(nansum((X - u_grand).^2));
+ SS_error = SS_total - SS_measures - SS_subjects;
+ df_measures = J - 1;
+ df_subjects = N - 1;
+ df_grand = (N*J) - 1;
+ df_error = df_grand - df_measures - df_subjects;
+ MS_measures = SS_measures / df_measures;
+ MS_subjects = SS_subjects / df_subjects;
+ MS_error = SS_error / df_error; % variation expected as a result of
sampling error alone
+ F = MS_measures / MS_error;
+ p = 1 - fcdf(F, df_measures, df_error); % Probability of F given equal
means.
- % Find the means of the subjects and measures, ignoring any NaNs
- u_subjects = nanmean(X,2);
- u_measures = nanmean(X,1);
- u_grand = nansum(nansum(X)) / (size(X,1) * size(X,2));
- % Differences between rows will be reflected in SS subjects, differences
- % between columns will be reflected in SS_within subjects.
- N = size(X,1); % number of subjects
- J = size(X,2); % number of samples per subject
- SS_measures = N * nansum((u_measures - u_grand).^2);
- SS_subjects = J * nansum((u_subjects - u_grand).^2);
- SS_total = nansum(nansum((X - u_grand).^2));
- SS_error = SS_total - SS_measures - SS_subjects;
- df_measures = J - 1;
- df_subjects = N - 1;
- df_grand = (N*J) - 1;
- df_error = df_grand - df_measures - df_subjects;
- MS_measures = SS_measures / df_measures;
- MS_subjects = SS_subjects / df_subjects;
- MS_error = SS_error / df_error; % variation expected as a result of
sampling error alone
- F = MS_measures / MS_error;
- p = 1 - fcdf(F,df_measures,df_subjects); % Probability of F given equal
means.
+ if strcmp(option, 'string')
+ table = [sprintf('\nSource\tSS\tdf\tMS\tF\tProb > F'), ...
+ sprintf('\nSubject\t%g\t%i\t%g', SS_subjects, df_subjects,
MS_subjects), ...
+ sprintf('\nMeasure\t%g\t%i\t%g\t%g\t%g', SS_measures,
df_measures, MS_measures, F, p), ...
+ sprintf('\nError\t%g\t%i\t%g', SS_error, df_error,
MS_error), ...
+ sprintf('\n')];
+ else
+ table = {'Source', 'Partial SS', 'df', 'MS', 'F', 'Prob > F'; ...
+ 'Subject', SS_subjects, df_subjects, MS_subjects, '', '';
...
+ 'Measure', SS_measures, df_measures, MS_measures, F, p};
+ end
- if strcmp(option, 'string')
- table = [sprintf('\nSource\tSS\tdf\tMS\tF\tProb > F'), ...
- sprintf('\nSubject\t%g\t%i\t%g', SS_subjects, df_subjects,
MS_subjects), ...
- sprintf('\nMeasure\t%g\t%i\t%g\t%g\t%g', SS_measures,
df_measures, MS_measures, F, p), ...
- sprintf('\nError\t%g\t%i\t%g', SS_error, df_error,
MS_error), ...
- sprintf('\n')];
- else
- table = {'Source', 'Partial SS', 'df', 'MS', 'F', 'Prob > F'; ...
- 'Subject', SS_subjects, df_subjects, MS_subjects, '', ''; ...
- 'Measure', SS_measures, df_measures, MS_measures, F, p};
- end
+ st.gnames = condition'; % this is the same struct format used in anova1
+ st.n = repmat(N, 1, J);
+ st.source = 'anova1'; % it cannot be assumed that 'repanova' is a
supported source for multcompare
+ st.means = u_measures;
+ st.df = df_error;
+ st.s = sqrt(MS_error);
+end
- st.gnames = condition'; % this is the same struct format used in anova1
- st.n = repmat(N, 1, J);
- st.source = 'anova1'; % it cannot be assumed that 'repanova' is a
supported source for multcompare
- st.means = u_measures;
- st.df = df_error;
- st.s = sqrt(MS_error);
-endfunction
-
% This function was created with guidance from the following websites:
% http://courses.washington.edu/stat217/rmANOVA.html
%
http://grants.hhp.coe.uh.edu/doconnor/PEP6305/Topic%20010%20Repeated%20Measures.htm
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