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Finished outputting report

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This commit is contained in:
Thomas A. Christensen II 2018-09-21 22:58:47 -06:00
parent eb20b8b552
commit 88fbf16ed4
1 changed files with 83 additions and 1 deletions
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82
MATLAB/beefblup.m
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@ -103,6 +103,9 @@ X(:,1) = ones(1, numanimals);
% Create an external counter that will increment through both loops
I = 2;
% Store the traits in a string cell array
adjustedtraits = cell(1, sum(numgroups)-length(numgroups));
% Iterate through each group
for i = 1:length(normal)
% Find the traits that are present in this trait
@ -116,6 +119,11 @@ for i = 1:length(normal)
for j = 1:length(traits)
matchedindex = find(strcmp(data(:,i+5), traits{j}));
X(matchedindex, I) = 1;
% Add this trait to the string
adjustedtraits(I - 1) = traits(j);
% Increment the big counter
I = I + 1;
end
end
@ -181,3 +189,77 @@ solutions = [X'*X X'*Z; Z'*X (Z'*Z)+(inv(A).*lambda)]\[X'*Y; Z'*Y];
% Find the accuracies
diaginv = diag(inv([X'*X X'*Z; Z'*X (Z'*Z)+(inv(A).*lambda)]));
reliability = 1 - diaginv.*lambda;
% Ask the user for where they would like the file saved
[savename, savepath, ~] = uiputfile('*.txt', 'Save your beefblup results', 'results');
% Find how many traits we found BLUE for
numgroups = numgroups - 1;
% Start printing results to output
fileID = fopen([savepath savename], 'w');
fprintf(fileID, 'beefblup Results Report\n');
fprintf(fileID, 'Produced using beefblup for MATLAB (');
fprintf(fileID, '%s', 'https://github.com/millironx/beefblup');
fprintf(fileID, ')\n\n');
fprintf(fileID, 'Input:\t');
fprintf(fileID, '%s', fullname);
fprintf(fileID, '\nAnalysis performed:\t');
fprintf(fileID, date);
fprintf(fileID, '\nTrait examined:\t');
fprintf(fileID, [headers{5}]);
fprintf(fileID, '\n\n');
% Print base population stats
fprintf(fileID, 'Base Population:\n');
for i = 1:length(numgroups)
fprintf(fileID, '\t');
fprintf(fileID, [headers{i+5}]);
fprintf(fileID, ':\t');
fprintf(fileID, [normal{i}]);
fprintf(fileID, '\n');
end
fprintf(fileID, '\tMean ');
fprintf(fileID, [headers{5}]);
fprintf(fileID, ':\t');
fprintf(fileID, num2str(solutions(1)));
fprintf(fileID, '\n\n');
I = 2;
% Contemporary group adjustments
fprintf(fileID, 'Contemporary Group Effects:\n');
for i = 1:length(numgroups)
fprintf(fileID, '\t');
fprintf(fileID, [headers{i+5}]);
fprintf(fileID, '\tEffect\tReliability\n');
for j = 1:numgroups(i)
fprintf(fileID, '\t');
fprintf(fileID, [adjustedtraits{I-1}]);
fprintf(fileID, '\t');
fprintf(fileID, num2str(solutions(I)));
fprintf(fileID, '\t');
fprintf(fileID, num2str(reliability(I)));
fprintf(fileID, '\n');
I = I + 1;
end
fprintf(fileID, '\n');
end
fprintf(fileID, '\n');
% Expected breeding values
fprintf(fileID, 'Expected Breeding Values:\n');
fprintf(fileID, '\tID\tEBV\tReliability\n');
for i = 1:numanimals
fprintf(fileID, '\t');
fprintf(fileID, [data{i,1}]);
fprintf(fileID, '\t');
fprintf(fileID, num2str(solutions(i+I-1)));
fprintf(fileID, '\t');
fprintf(fileID, num2str(reliability(i+I-1)));
fprintf(fileID, '\n');
end
fprintf(fileID, '\n - END REPORT -');
fclose(fileID);