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Merge branch 'release/v0.2'

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Thomas A. Christensen II 2021-06-18 13:27:18 -05:00
commit 487f1c30df
9 changed files with 337 additions and 689 deletions
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Excel/Master BLUP Worksheet.xlsx
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Excel/van der Werf.xlsx
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13
Julia/install.jl
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@ -1,13 +0,0 @@
# beefblup install
# Prepares the Julia environment for using beefblup by installing the requisite
# packages
# Usage: julia install.jl
# (C) 2020 Thomas A. Christensen II
# Licensed under BSD-3-Clause License
# Import the package manager
using Pkg
# Install requisite packages
Pkg.add("XLSX")
Pkg.add("Gtk")

341
MATLAB/beefblup.m
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@ -1,341 +0,0 @@
% beefblup
% Main script for performing single-variate BLUP to find beef cattle
% breeding values
% Usage: beefblup
% (C) 2020 Thomas A. Christensen II
% Licensed under BSD-3-Clause License
% Prepare the workspace for computation
clear
clc
close all
%% Display stuff
disp('beefblup v. 0.1')
disp('(C) 2020 Thomas A. Christensen II')
disp('https://github.com/millironx/beefblup')
disp(' ')
%% Prompt User
% Ask for an input spreadsheet
[name, path] = uigetfile('*.xlsx','Select a beefblup worksheet');
% Ask for an ouput text file
[savename, savepath, ~] = uiputfile('*.txt', 'Save your beefblup results', 'results');
% Ask for heritability
h2 = input('What is the heritability for this trait? >> ');
%% Import input file
tic
disp(' ')
disp('Importing Excel file...')
% Import data from a suitable spreadsheet
fullname = [path name];
clear name path
[~, ~, data] = xlsread(fullname);
disp('Importing Excel file... Done!')
toc
disp(' ')
%% Process input file
tic
disp(' ')
disp('Processing and formatting data...')
disp(' ')
% Extract the headers into a separate array
headers = data(1,:);
data(1,:) = [];
% Convert the string dates to numbers
data(:,2) = num2cell(datenum(data(:,2)));
% Sort the array by date
data = sortrows(data,2);
% Coerce all id fields to string format
strings = [1 3 4];
data(:,strings) = cellfun(@num2str, data(:,strings), 'UniformOutput', false);
% Define fields to hold id values for animals and their parents
ids = char(data{:,1});
damids = char(data{:,3});
sireids = char(data{:,4});
numanimals = length(data(:,1));
% Define fields to hold the index values for animals and their parents
dam = zeros(numanimals,1);
sire = zeros(numanimals,1);
% Find all row numbers where an animal was a parent
for i=1:numanimals
% Find all animals that this animal birthed
dammatch = ismember(damids, ids(i,:), 'rows');
damindexes = find(dammatch == 1);
dam(damindexes) = i;
% Find all animals that this animal sired
sirematch = ismember(sireids, ids(i,:), 'rows');
sireindexes = find(sirematch == 1);
sire(sireindexes) = i;
end
% Store column numbers that need to be deleted
% Column 6 contains an intermediate Excel calculation and always needs to
% be deleted
colstodelete = 6;
% Coerce each group to string format
for i = 7:length(headers)
data(:,i) = cellfun(@num2str, data(:,i), 'UniformOutput', false);
end
% Find any columns that need to be deleted
for i = 7:length(headers)
if length(uniquecell(data(:,i))) <= 1
colname = headers{i};
disp(['Column "' colname '" does not have any unique animals and will be removed'])
disp('from this analysis');
colstodelete = [colstodelete i];
end
end
% Delete the appropriate columns from the datasheet and the headers
data(:,colstodelete) = [];
headers(colstodelete) = [];
% Determine how many contemporary groups there are
numgroups = ones(1, length(headers)-5);
for i = 6:length(headers)
numgroups(i-5) = length(uniquecell(data(:,i)));
end
% If there are more groups than animals, then the analysis cannot continue
if sum(numgroups) >= numanimals
disp('There are more contemporary groups than animals. The analysis will now abort.');
return
end
% Define a "normal" animal as one of the last in the groups, provided that
% all traits do not have null values
normal = cell([1 length(headers)-5]);
for i = 6:length(headers)
for j = numanimals:-1:1
if not(cellfun(@isempty, data(j,i)))
normal(i - 5) = data(j,i);
break
end
end
end
% Print the results of the "normal" definition
disp(' ')
disp('For the purposes of this analysis, a "normal" animal will be defined')
disp('by the following traits:')
for i = 6:length(headers)
disp([headers{i} ': ' normal{i-5}])
end
disp(' ')
disp('If no animal matching this description exists, the results may appear')
disp('outlandish, but are still as correct as the accuracy suggests')
disp(' ')
disp('Processing and formatting data... Done!')
toc
disp(' ')
%% Create the fixed-effect matrix
tic
disp(' ')
disp('Creating the fixed-effect matrix...')
% Form the fixed effect matrix
X = zeros(numanimals, sum(numgroups)-length(numgroups)+1);
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
traits = uniquecell(data(:,i+5));
% Remove the "normal" version from the analysis
normalindex = find(strcmp(traits, normal{i}));
traits(normalindex) = [];
% Iterate inside of the group
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
disp('Creating the fixed-effect matrix... Done!')
toc
disp(' ')
%% Additive relationship matrix
tic
disp(' ')
disp('Creating the additive relationship matrix...')
% Create an empty matrix for the additive relationship matrix
A = zeros(numanimals, numanimals);
% Create the additive relationship matrix by the FORTRAN method presented
% by Henderson
for i = 1:numanimals
if dam(i) ~= 0 && sire(i) ~= 0
for j = 1:(i-1)
A(j,i) = 0.5*(A(j,sire(i))+A(j,dam(i)));
A(i,j) = A(j,i);
end
A(i,i) = 1 + 0.5*A(sire(i),dam(i));
elseif dam(i) ~= 0 && sire(i) == 0
for j = 1:(i-1)
A(j,i) = 0.5*A(j,dam(i));
A(i,j) = A(j,i);
end
A(i,i) = 1;
elseif dam(i) == 0 && sire(i) ~=0
for j = 1:(i-1)
A(j,i) = 0.5*A(j,sire(i));
A(i,j) = A(j,i);
end
A(i,i) = 1;
else
for j = 1:(i-1)
A(j,i) = 0;
A(i,j) = 0;
end
A(i,i) = 1;
end
end
disp('Creating the additive relationship matrix... Done!')
toc
disp(' ')
%% Perform BLUP
tic
disp(' ')
disp('Solving the mixed-model equations')
% Extract the observed data
Y = cell2mat(data(:, 5));
% The identity matrix for random effects
Z = eye(numanimals, numanimals);
% Remove items where there is no data
nullobs = find(isnan(Y));
Z(nullobs, nullobs) = 0;
% Calculate heritability
lambda = (1-h2)/h2;
% Use the mixed-model equations
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;
disp('Solving the mixed-model equations... Done!')
toc
disp(' ')
%% Output the results
tic
disp(' ')
disp('Saving 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);
disp('Saving results... Done!')
toc
disp(' ')

9
MATLAB/uniquecell.m
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@ -1,9 +0,0 @@
% uniquenan
% Serves the same purpose as UNIQUE, but ensures any empty cells are not
% counted
function y = uniquecell(x)
y = unique(x);
if any(cellfun(@isempty, y))
y(cellfun(@isempty, y)) = [];
end
end

6
Project.toml Normal file
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@ -0,0 +1,6 @@
[deps]
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
Gtk = "4c0ca9eb-093a-5379-98c5-f87ac0bbbf44"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"

77
README.md
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@ -13,35 +13,34 @@ Why? It's part of my effort to
## Installation ## Installation
1. [Download and install Julia](https://julialang.org/downloads/platform/) 1. [Download and install Julia](https://julialang.org/downloads/platform/)
2. Open a new Julia window and type the `]` key 2. Download the [beefblup ZIP
3. Type `add XLSX Gtk` and press **Enter** file](https://github.com/MillironX/beefblup/archive/refs/tags/v0.2.zip) and unzip it someplace memorable
3. In your file explorer, copy the address of the "beefblup" folder
Alternatively, you can run the [install 4. Launch Julia
script](https://github.com/MillironX/beefblup/raw/master/Julia/install.jl) from 5. Type `cd("<the address copied in step 5")` and press **Enter** (For example,
Julia. `cd("C:\Users\MillironX\Documents\beefblup")`)
6. Type the `]` key
7. Type `activate .` and press **Enter**
8. Type `instantiate` and press **Enter**
9. Installation is done: you can close the Julia window
## How to Use ## How to Use
> **Note:** beefblup and [Juno](https://junolab.org)/[Julia Pro](https://juliacomputing.com/products/juliapro.html) currently [don't get along](https://github.com/JunoLab/Juno.jl/issues/118). 1. Make a copy of the "sample.csv" spreadsheet and replace the data with your own
> Although it's tempting to just open up beefblup in Juno and press the big play 1. The trait you wish to calculate EBVs for always goes in the rightmost column
> button, it won't work. Follow these instructions until it's fixed. If you 2. If you wish to add more contemporary group traits to your analysis, include them before the rightmost column
> don't know what Juno is: ignore this message. 2. Save and close
3. In your file explorer, copy the address of the "beefblup" folder
1. Download the [beefblup ZIP 4. Launch Julia
file](https://github.com/MillironX/beefblup/archive/v0.1.zip) and unzip it 5. Type `cd("<the address copied in step 5")` and press **Enter** (For example,
someplace memorable `cd("C:\Users\MillironX\Documents\beefblup")`)
2. Make a copy of the "Master BLUP Worksheet" and replace the sample data with your own 6. Type the `]` key
3. If you wish to add more contemporary group traits to your analysis, replace 7. Type `activate .` and press **Enter**
or add them to the right of the Purple section 8. Press **Backspace**
4. Save and close 9. Type `include("src/beefblup.jl")` and press **Enter**
5. In your file explorer, copy the address of the "Julia" folder 10. Select the spreadsheet you created in steps 1-4
6. Launch Julia 11. Follow the on-screen prompts
7. Type `cd("<the address copied in step 5")` and press **Enter** (For example, 12. **#KeepEPDsReal!**
`cd("C:\Users\MillironX\Documents\beefblup\Julia")`)
8. Type `include("beefblup.jl")` and press **Enter**
9. Select the spreadsheet you created in steps 1-4
10. Follow the on-screen prompts
11. **#KeepEPDsReal!**
## For Programmers ## For Programmers
@ -51,19 +50,19 @@ Julia.
### Development Roadmap ### Development Roadmap
| Version | Feature | | Version | Feature | Status |
| ------- | ------------------------------------------------------------------- | | ------- | ----------------------------------------------------------------------------------- | ------------------ |
| v0.1 | Julia port of original MATLAB script | | v0.1 | Julia port of original MATLAB script | :heavy_check_mark: |
| v0.2 | Spreadsheet format redesign | | v0.2 | Spreadsheet format redesign | :heavy_check_mark: |
| v0.3 | API rewrite (change to function calls and package format instead of script running) | | v0.3 | API rewrite (change to function calls and package format instead of script running) | |
| v0.4 | Add GUI for all options | | v0.4 | Add GUI for all options | |
| v0.5 | Automatically calculated Age-Of-Dam, Year, and Season fixed-effects | | v0.5 | Automatically calculated Age-Of-Dam, Year, and Season fixed-effects | |
| v0.6 | Repeated measurement BLUP (aka dairyblup) | | v0.6 | Repeated measurement BLUP (aka dairyblup) | |
| v0.7 | Multiple trait BLUP | | v0.7 | Multiple trait BLUP | |
| v0.8 | Maternal effects BLUP | | v0.8 | Maternal effects BLUP | |
| v0.9 | Genomic BLUP | | v0.9 | Genomic BLUP | |
| v0.10 | beefblup binaries | | v0.10 | beefblup binaries | |
| v1.0 | [Finally, RELEASE!!!](https://youtu.be/1CBjxGdgC1w?t=282) | | v1.0 | [Finally, RELEASE!!!](https://youtu.be/1CBjxGdgC1w?t=282) | |
### Bug Reports ### Bug Reports

8
data/sample.csv Normal file
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@ -0,0 +1,8 @@
id,birthdate,dam,sire,year,sex,weaning_weight
1,1/1/1990,,,1990,male,354
2,1/1/1990,,,1990,female,251
3,1/1/1991,,1,1991,male,327
4,1/1/1991,,1,1991,female,328
5,1/1/1991,2,1,1991,male,301
6,1/1/1991,2,,1991,female,270
7,1/1/1992,,,1992,male,330
1 id birthdate dam sire year sex weaning_weight
2 1 1/1/1990 1990 male 354
3 2 1/1/1990 1990 female 251
4 3 1/1/1991 1 1991 male 327
5 4 1/1/1991 1 1991 female 328
6 5 1/1/1991 2 1 1991 male 301
7 6 1/1/1991 2 1991 female 270
8 7 1/1/1992 1992 male 330

96
Julia/beefblup.jl → src/beefblup.jl Normal file → Executable file
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@ -1,19 +1,26 @@
#!/bin/bash
#=
exec julia --project=$(realpath $(dirname $(dirname "${BASH_SOURCE[0]}"))) "${BASH_SOURCE[0]}" "$@"
=#
# beefblup # beefblup
# Main script for performing single-variate BLUP to find beef cattle # Main script for performing single-variate BLUP to find beef cattle
# breeding values # breeding values
# Usage: julia beefblup.jl # Usage: julia beefblup.jl
# (C) 2020 Thomas A. Christensen II # (C) 2021 Thomas A. Christensen II
# Licensed under BSD-3-Clause License # Licensed under BSD-3-Clause License
# cSpell:includeRegExp #.*
# cSpell:includeRegExp ("""|''')[^\1]*\1
# Import the required packages # Import the required packages
using XLSX using CSV
using DataFrames
using LinearAlgebra using LinearAlgebra
using Dates using Dates
using Gtk using Gtk
# Display stuff # Display stuff
println("beefblup v 0.1") println("beefblup v 0.2")
println("(C) 2020 Thomas A. Christensen II") println("(C) 2021 Thomas A. Christensen II")
println("https://github.com/millironx/beefblup") println("https://github.com/millironx/beefblup")
print("\n") print("\n")
@ -22,7 +29,7 @@ print("\n")
path = open_dialog_native( path = open_dialog_native(
"Select a beefblup worksheet", "Select a beefblup worksheet",
GtkNullContainer(), GtkNullContainer(),
("*.xlsx", GtkFileFilter("*.xlsx", name="beefblup worksheet")) ("*.csv", GtkFileFilter("*.csv", name="beefblup worksheet"))
) )
# Ask for an output text filename # Ask for an output text filename
@ -38,58 +45,45 @@ print("What is the heritability for this trait?> ")
h2 = parse(Float64, readline(stdin)) h2 = parse(Float64, readline(stdin))
### Import input filename ### Import input filename
print("[🐮]: Importing Excel file...") print("[🐮]: Importing data file...")
# Import data from a suitable spreadsheet # Import data from a suitable spreadsheet
data = XLSX.readxlsx(path)[1][:] data = DataFrame(CSV.File(path))
print("Done!\n") print("Done!\n")
### Process input file ### Process input file
print("[🐮]: Processing and formatting data...") print("[🐮]: Processing and formatting data...")
# Extract the headers into a separate array
headers = data[1,:]
data = data[2:end,:]
# Sort the array by date # Sort the array by date
data = sortslices(data, dims=1, lt=(x,y)->isless(x[2],y[2])) sort!(data, :birthdate)
# Define fields to hold id values for animals and their parents # Define fields to hold id values for animals and their parents
ids = string.(data[:,1]) numanimals = length(data.id)
damids = string.(data[:,3])
sireids = string.(data[:,4])
numanimals = length(ids)
# Find the index values for animals and their parents # Find the index values for animals and their parents
dam = indexin(damids, ids) dam = indexin(data.dam, data.id)
sire = indexin(sireids, ids) sire = indexin(data.sire, data.id)
# Store column numbers that need to be deleted # Extract all of the fixed effects
# Column 6 contains an intermediate Excel calculation and always need to fixedfx = select(data, Not([:id, :birthdate, :sire, :dam]))[:,1:end-1]
# be deleted
colstokeep = [1, 2, 3, 4, 5]
# Find any columns that need to be deleted # Find any columns that need to be deleted
for i in 7:length(headers) for i in 1:ncol(fixedfx)
if length(unique(data[:,i])) <= 1 if length(unique(fixedfx[:,i])) <= 1
colname = headers[i] colname = names(fixedfx)[i]
print("Column '") print("Column '")
print(colname) print(colname)
print("' does not have any unique animals and will be removed from this analysis\n") print("' does not have any unique animals and will be removed from this analysis\n")
else deletecols!(fixedfx,i)
push!(colstokeep, i)
end end
end end
# Delete the appropriate columns from the datasheet and the headers
data = data[:, colstokeep]
headers = headers[colstokeep]
# Determine how many contemporary groups there are # Determine how many contemporary groups there are
numgroups = ones(1, length(headers)-5) numtraits = ncol(fixedfx)
for i in 6:length(headers) numgroups = ones(1, numtraits)
numgroups[i-5] = length(unique(data[:,i])) for i in 1:numtraits
numgroups[i] = length(unique(fixedfx[:,i]))
end end
# If there are more groups than animals, then the analysis cannot continue # If there are more groups than animals, then the analysis cannot continue
@ -101,11 +95,11 @@ end
# Define a "normal" animal as one of the last in the groups, provided that # Define a "normal" animal as one of the last in the groups, provided that
# all traits do not have null values # all traits do not have null values
normal = Array{String}(undef,1,length(headers)-5) normal = Array{String}(undef,1,numtraits)
for i in 6:length(headers) for i in 1:numtraits
for j in numanimals:-1:1 for j in numanimals:-1:1
if !ismissing(data[j,i]) if !ismissing(fixedfx[j,i])
normal[i-5] = string(data[j,i]) normal[i] = string(fixedfx[j,i])
break break
end end
end end
@ -129,13 +123,13 @@ Array{String}(undef,floor(Int,sum(numgroups))-length(numgroups))
# Iterate through each group # Iterate through each group
for i in 1:length(normal) for i in 1:length(normal)
# Find the traits that are present in this trait # Find the traits that are present in this trait
localdata = string.(data[:,i+5]) localdata = string.(fixedfx[:,i])
traits = unique(localdata) traits = unique(localdata)
# Remove the normal version from the analysis # Remove the normal version from the analysis
effecttraits = traits[findall(x -> x != normal[i], traits)] effecttraits = traits[findall(x -> x != normal[i], traits)]
# Iterate inside of the group # Iterate inside of the group
for j in 1:length(effecttraits) for j in 1:(length(effecttraits))
matchedindex = findall(x -> x != effecttraits[j], localdata) matchedindex = findall(x -> x == effecttraits[j], localdata)
X[matchedindex, counter] .= 1 X[matchedindex, counter] .= 1
# Add this trait to the string # Add this trait to the string
adjustedtraits[counter - 1] = traits[j] adjustedtraits[counter - 1] = traits[j]
@ -188,7 +182,7 @@ print("Done!\n")
print("[🐮]: Solving the mixed-model equations...") print("[🐮]: Solving the mixed-model equations...")
# Extract the observed data # Extract the observed data
Y = convert(Array{Float64}, data[:,5]) Y = convert(Array{Float64}, data[:,end])
# The random effects matrix # The random effects matrix
Z = Matrix{Int}(I, numanimals, numanimals) Z = Matrix{Int}(I, numanimals, numanimals)
@ -217,10 +211,14 @@ print("[🐮]: Saving results...")
# Find how many traits we found BLUE for # Find how many traits we found BLUE for
numgroups = numgroups .- 1 numgroups = numgroups .- 1
# Extract the names of the traits
fixedfxnames = names(fixedfx)
traitname = names(data)[end]
# Start printing results to output # Start printing results to output
fileID = open(savepath, "w") fileID = open(savepath, "w")
write(fileID, "beefblup Results Report\n") write(fileID, "beefblup Results Report\n")
write(fileID, "Produced using beefblup for Julia (") write(fileID, "Produced using beefblup (")
write(fileID, "https://github.com/millironx/beefblup") write(fileID, "https://github.com/millironx/beefblup")
write(fileID, ")\n\n") write(fileID, ")\n\n")
write(fileID, "Input:\t") write(fileID, "Input:\t")
@ -228,20 +226,20 @@ write(fileID, path)
write(fileID, "\nAnalysis performed:\t") write(fileID, "\nAnalysis performed:\t")
write(fileID, string(Dates.today())) write(fileID, string(Dates.today()))
write(fileID, "\nTrait examined:\t") write(fileID, "\nTrait examined:\t")
write(fileID, headers[5]) write(fileID, traitname)
write(fileID, "\n\n") write(fileID, "\n\n")
# Print base population stats # Print base population stats
write(fileID, "Base Population:\n") write(fileID, "Base Population:\n")
for i in 1:length(numgroups) for i in 1:length(normal)
write(fileID, "\t") write(fileID, "\t")
write(fileID, headers[i+5]) write(fileID, fixedfxnames[i])
write(fileID, ":\t") write(fileID, ":\t")
write(fileID, normal[i]) write(fileID, normal[i])
write(fileID, "\n") write(fileID, "\n")
end end
write(fileID, "\tMean ") write(fileID, "\tMean ")
write(fileID, headers[5]) write(fileID, traitname)
write(fileID, ":\t") write(fileID, ":\t")
write(fileID, string(solutions[1])) write(fileID, string(solutions[1]))
write(fileID, "\n\n") write(fileID, "\n\n")
@ -251,7 +249,7 @@ counter = 2
write(fileID, "Contemporary Group Effects:\n") write(fileID, "Contemporary Group Effects:\n")
for i in 1:length(numgroups) for i in 1:length(numgroups)
write(fileID, "\t") write(fileID, "\t")
write(fileID, headers[i+5]) write(fileID, fixedfxnames[i])
write(fileID, "\tEffect\tReliability\n") write(fileID, "\tEffect\tReliability\n")
for j in 1:numgroups[i] for j in 1:numgroups[i]
write(fileID, "\t") write(fileID, "\t")
@ -273,7 +271,7 @@ write(fileID, "Expected Breeding Values:\n")
write(fileID, "\tID\tEBV\tReliability\n") write(fileID, "\tID\tEBV\tReliability\n")
for i in 1:numanimals for i in 1:numanimals
write(fileID, "\t") write(fileID, "\t")
write(fileID, ids[i]) write(fileID, string(data.id[i]))
write(fileID, "\t") write(fileID, "\t")
write(fileID, string(solutions[i+counter-1])) write(fileID, string(solutions[i+counter-1]))
write(fileID, "\t") write(fileID, "\t")