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Remove Matlab script files
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2 changed files with 0 additions and 350 deletions
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% beefblup
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% Main script for performing single-variate BLUP to find beef cattle
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% breeding values
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% Usage: beefblup
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% (C) 2020 Thomas A. Christensen II
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% Licensed under BSD-3-Clause License
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% Prepare the workspace for computation
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clear
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clc
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close all
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%% Display stuff
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disp('beefblup v. 0.1')
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disp('(C) 2020 Thomas A. Christensen II')
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disp('https://github.com/millironx/beefblup')
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disp(' ')
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%% Prompt User
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% Ask for an input spreadsheet
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[name, path] = uigetfile('*.xlsx','Select a beefblup worksheet');
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% Ask for an ouput text file
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[savename, savepath, ~] = uiputfile('*.txt', 'Save your beefblup results', 'results');
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% Ask for heritability
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h2 = input('What is the heritability for this trait? >> ');
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%% Import input file
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tic
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disp(' ')
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disp('Importing Excel file...')
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% Import data from a suitable spreadsheet
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fullname = [path name];
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clear name path
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[~, ~, data] = xlsread(fullname);
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disp('Importing Excel file... Done!')
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toc
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disp(' ')
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%% Process input file
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tic
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disp(' ')
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disp('Processing and formatting data...')
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disp(' ')
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% Extract the headers into a separate array
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headers = data(1,:);
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data(1,:) = [];
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% Convert the string dates to numbers
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data(:,2) = num2cell(datenum(data(:,2)));
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% Sort the array by date
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data = sortrows(data,2);
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% Coerce all id fields to string format
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strings = [1 3 4];
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data(:,strings) = cellfun(@num2str, data(:,strings), 'UniformOutput', false);
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% Define fields to hold id values for animals and their parents
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ids = char(data{:,1});
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damids = char(data{:,3});
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sireids = char(data{:,4});
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numanimals = length(data(:,1));
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% Define fields to hold the index values for animals and their parents
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dam = zeros(numanimals,1);
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sire = zeros(numanimals,1);
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% Find all row numbers where an animal was a parent
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for i=1:numanimals
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% Find all animals that this animal birthed
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dammatch = ismember(damids, ids(i,:), 'rows');
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damindexes = find(dammatch == 1);
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dam(damindexes) = i;
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% Find all animals that this animal sired
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sirematch = ismember(sireids, ids(i,:), 'rows');
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sireindexes = find(sirematch == 1);
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sire(sireindexes) = i;
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end
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% Store column numbers that need to be deleted
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% Column 6 contains an intermediate Excel calculation and always needs to
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% be deleted
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colstodelete = 6;
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% Coerce each group to string format
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for i = 7:length(headers)
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data(:,i) = cellfun(@num2str, data(:,i), 'UniformOutput', false);
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end
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% Find any columns that need to be deleted
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for i = 7:length(headers)
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if length(uniquecell(data(:,i))) <= 1
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colname = headers{i};
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disp(['Column "' colname '" does not have any unique animals and will be removed'])
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disp('from this analysis');
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colstodelete = [colstodelete i];
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end
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end
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% Delete the appropriate columns from the datasheet and the headers
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data(:,colstodelete) = [];
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headers(colstodelete) = [];
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% Determine how many contemporary groups there are
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numgroups = ones(1, length(headers)-5);
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for i = 6:length(headers)
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numgroups(i-5) = length(uniquecell(data(:,i)));
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end
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% If there are more groups than animals, then the analysis cannot continue
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if sum(numgroups) >= numanimals
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disp('There are more contemporary groups than animals. The analysis will now abort.');
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return
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end
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% Define a "normal" animal as one of the last in the groups, provided that
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% all traits do not have null values
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normal = cell([1 length(headers)-5]);
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for i = 6:length(headers)
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for j = numanimals:-1:1
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if not(cellfun(@isempty, data(j,i)))
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normal(i - 5) = data(j,i);
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break
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end
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end
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end
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% Print the results of the "normal" definition
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disp(' ')
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disp('For the purposes of this analysis, a "normal" animal will be defined')
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disp('by the following traits:')
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for i = 6:length(headers)
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disp([headers{i} ': ' normal{i-5}])
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end
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disp(' ')
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disp('If no animal matching this description exists, the results may appear')
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disp('outlandish, but are still as correct as the accuracy suggests')
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disp(' ')
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disp('Processing and formatting data... Done!')
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toc
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disp(' ')
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%% Create the fixed-effect matrix
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tic
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disp(' ')
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disp('Creating the fixed-effect matrix...')
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% Form the fixed effect matrix
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X = zeros(numanimals, sum(numgroups)-length(numgroups)+1);
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X(:,1) = ones(1, numanimals);
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% Create an external counter that will increment through both loops
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I = 2;
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% Store the traits in a string cell array
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adjustedtraits = cell(1, sum(numgroups)-length(numgroups));
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% Iterate through each group
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for i = 1:length(normal)
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% Find the traits that are present in this trait
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traits = uniquecell(data(:,i+5));
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% Remove the "normal" version from the analysis
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normalindex = find(strcmp(traits, normal{i}));
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traits(normalindex) = [];
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% Iterate inside of the group
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for j = 1:length(traits)
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matchedindex = find(strcmp(data(:,i+5), traits{j}));
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X(matchedindex, I) = 1;
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% Add this trait to the string
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adjustedtraits(I - 1) = traits(j);
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% Increment the big counter
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I = I + 1;
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end
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end
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disp('Creating the fixed-effect matrix... Done!')
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toc
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disp(' ')
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%% Additive relationship matrix
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tic
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disp(' ')
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disp('Creating the additive relationship matrix...')
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% Create an empty matrix for the additive relationship matrix
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A = zeros(numanimals, numanimals);
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% Create the additive relationship matrix by the FORTRAN method presented
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% by Henderson
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for i = 1:numanimals
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if dam(i) ~= 0 && sire(i) ~= 0
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for j = 1:(i-1)
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A(j,i) = 0.5*(A(j,sire(i))+A(j,dam(i)));
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A(i,j) = A(j,i);
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end
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A(i,i) = 1 + 0.5*A(sire(i),dam(i));
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elseif dam(i) ~= 0 && sire(i) == 0
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for j = 1:(i-1)
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A(j,i) = 0.5*A(j,dam(i));
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A(i,j) = A(j,i);
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end
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A(i,i) = 1;
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elseif dam(i) == 0 && sire(i) ~=0
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for j = 1:(i-1)
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A(j,i) = 0.5*A(j,sire(i));
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A(i,j) = A(j,i);
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end
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A(i,i) = 1;
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else
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for j = 1:(i-1)
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A(j,i) = 0;
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A(i,j) = 0;
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end
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A(i,i) = 1;
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end
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end
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disp('Creating the additive relationship matrix... Done!')
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toc
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disp(' ')
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%% Perform BLUP
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tic
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disp(' ')
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disp('Solving the mixed-model equations')
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% Extract the observed data
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Y = cell2mat(data(:, 5));
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% The identity matrix for random effects
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Z = eye(numanimals, numanimals);
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% Remove items where there is no data
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nullobs = find(isnan(Y));
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Z(nullobs, nullobs) = 0;
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% Calculate heritability
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lambda = (1-h2)/h2;
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% Use the mixed-model equations
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solutions = [X'*X X'*Z; Z'*X (Z'*Z)+(inv(A).*lambda)]\[X'*Y; Z'*Y];
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% Find the accuracies
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diaginv = diag(inv([X'*X X'*Z; Z'*X (Z'*Z)+(inv(A).*lambda)]));
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reliability = 1 - diaginv.*lambda;
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disp('Solving the mixed-model equations... Done!')
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toc
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disp(' ')
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%% Output the results
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tic
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disp(' ')
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disp('Saving results...')
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% Find how many traits we found BLUE for
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numgroups = numgroups - 1;
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% Start printing results to output
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fileID = fopen([savepath savename], 'w');
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fprintf(fileID, 'beefblup Results Report\n');
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fprintf(fileID, 'Produced using beefblup for MATLAB (');
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fprintf(fileID, '%s', 'https://github.com/millironx/beefblup');
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fprintf(fileID, ')\n\n');
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fprintf(fileID, 'Input:\t');
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fprintf(fileID, '%s', fullname);
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fprintf(fileID, '\nAnalysis performed:\t');
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fprintf(fileID, date);
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fprintf(fileID, '\nTrait examined:\t');
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fprintf(fileID, [headers{5}]);
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fprintf(fileID, '\n\n');
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% Print base population stats
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fprintf(fileID, 'Base Population:\n');
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for i = 1:length(numgroups)
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fprintf(fileID, '\t');
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fprintf(fileID, [headers{i+5}]);
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fprintf(fileID, ':\t');
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fprintf(fileID, [normal{i}]);
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fprintf(fileID, '\n');
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end
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fprintf(fileID, '\tMean ');
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fprintf(fileID, [headers{5}]);
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fprintf(fileID, ':\t');
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fprintf(fileID, num2str(solutions(1)));
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fprintf(fileID, '\n\n');
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I = 2;
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% Contemporary group adjustments
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fprintf(fileID, 'Contemporary Group Effects:\n');
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for i = 1:length(numgroups)
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fprintf(fileID, '\t');
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fprintf(fileID, [headers{i+5}]);
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fprintf(fileID, '\tEffect\tReliability\n');
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for j = 1:numgroups(i)
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fprintf(fileID, '\t');
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fprintf(fileID, [adjustedtraits{I-1}]);
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fprintf(fileID, '\t');
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fprintf(fileID, num2str(solutions(I)));
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fprintf(fileID, '\t');
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fprintf(fileID, num2str(reliability(I)));
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fprintf(fileID, '\n');
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I = I + 1;
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end
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fprintf(fileID, '\n');
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end
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fprintf(fileID, '\n');
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% Expected breeding values
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fprintf(fileID, 'Expected Breeding Values:\n');
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fprintf(fileID, '\tID\tEBV\tReliability\n');
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for i = 1:numanimals
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fprintf(fileID, '\t');
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fprintf(fileID, [data{i,1}]);
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fprintf(fileID, '\t');
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fprintf(fileID, num2str(solutions(i+I-1)));
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fprintf(fileID, '\t');
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fprintf(fileID, num2str(reliability(i+I-1)));
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fprintf(fileID, '\n');
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end
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fprintf(fileID, '\n - END REPORT -');
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fclose(fileID);
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disp('Saving results... Done!')
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toc
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disp(' ')
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@ -1,9 +0,0 @@
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% uniquenan
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% Serves the same purpose as UNIQUE, but ensures any empty cells are not
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% counted
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function y = uniquecell(x)
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y = unique(x);
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if any(cellfun(@isempty, y))
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y(cellfun(@isempty, y)) = [];
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end
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end
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