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Complete port of beefblup to Julia
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GPG key ID:
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5 changed files with 194 additions and 202 deletions
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@ -1,9 +0,0 @@
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<w>beefblup</w>
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<w>blup</w>
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<w>matlab</w>
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BIN
Excel/van der Werf2.xlsx
Normal file
BIN
Excel/van der Werf2.xlsx
Normal file
Binary file not shown.
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@ -7,6 +7,9 @@
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# Import the required packages
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using XLSX
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using LinearAlgebra
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using Dates
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using Gtk
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# Display stuff
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println("beefblup v 0.0.0.1")
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@ -16,19 +19,23 @@ print("\n")
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### Prompt User
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# Ask for an input spreadsheet
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# print("Enter the full filename of a beefblup worksheet> ")
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# path = readline(stdin)
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path = "C:\\Users\\cclea\\source\\repos\\beefblup\\Excel\\Master BLUP Worksheet.xlsx"
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path = open_dialog_native(
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"Select a beefblup worksheet",
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GtkNullContainer(),
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("*.xlsx", GtkFileFilter("*.xlsx", name="beefblup worksheet"))
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)
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# Ask for an output text filename
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# print("Enter the full filename of your desired results> ")
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# savepath = readline(stdin)
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savepath = "C:\\Users\\cclea\\source\\repos\\beefblup\\results.txt"
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savepath = save_dialog_native(
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"Save your beefblup results",
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GtkNullContainer(),
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(GtkFileFilter("*.txt", name="Results file"),
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"*.txt")
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)
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# Ask for heritability
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# print("What is the heritability for this trait?> ")
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# h2 = parse(Float64, readline(stdin))
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h2 = 0.4
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print("What is the heritability for this trait?> ")
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h2 = parse(Float64, readline(stdin))
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### Import input filename
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print("[🐮]: Importing Excel file...")
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@ -39,7 +46,7 @@ data = XLSX.readxlsx(path)[1][:]
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print("Done!\n")
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### Process input file
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print("[🐮]: Processing and formatting data ...")
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print("[🐮]: Processing and formatting data...")
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# Extract the headers into a separate array
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headers = data[1,:]
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@ -49,9 +56,9 @@ data = data[2:end,:]
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data = sortslices(data, dims=1, lt=(x,y)->isless(x[2],y[2]))
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# Define fields to hold id values for animals and their parents
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ids = data[:,1]
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damids = data[:,3]
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sireids = data[:,4]
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ids = string.(data[:,1])
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damids = string.(data[:,3])
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sireids = string.(data[:,4])
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numanimals = length(ids)
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# Find the index values for animals and their parents
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@ -98,23 +105,183 @@ normal = Array{String}(undef,1,length(headers)-5)
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for i in 6:length(headers)
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for j in numanimals:-1:1
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if !ismissing(data[j,i])
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normal[i-5] = data[j,i]
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normal[i-5] = string(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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println(" ")
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println("For the purposes of this analysis, a 'normal' animal will be defined by")
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println("the following traits:")
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for i in 6:length(headers)
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print(headers[i])
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print(": ")
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print(normal[i-5])
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print("\n")
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print("Done!\n")
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### Create the fixed-effect matrix
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print("[🐮]: Creating the fixed-effect matrix...")
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# Form the fixed-effect matrix
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X = zeros(Int8, numanimals, floor(Int,sum(numgroups))-length(numgroups)+1)
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X[:,1] = ones(Int8, 1, numanimals)
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# Create an external counter that will increment through both loops
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counter = 2
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# Store the traits in a string array
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adjustedtraits =
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Array{String}(undef,floor(Int,sum(numgroups))-length(numgroups))
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# Iterate through each group
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for i in 1:length(normal)
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# Find the traits that are present in this trait
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localdata = string.(data[:,i+5])
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traits = unique(localdata)
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# Remove the normal version from the analysis
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effecttraits = traits[findall(x -> x != normal[i], traits)]
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# Iterate inside of the group
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for j in 1:length(effecttraits)
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matchedindex = findall(x -> x != effecttraits[j], localdata)
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X[matchedindex, counter] .= 1
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# Add this trait to the string
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adjustedtraits[counter - 1] = traits[j]
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# Increment the big counter
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global counter = counter + 1
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end
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end
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println("If no animal matching this description exists, the results may appear")
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println("outlandish, but are still as correct as the accuracy suggests")
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print("Done!\n")
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### Additive relationship matrix
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print("[🐮]: Creating 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 by
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# Henderson
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for i in 1:numanimals
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if !isnothing(dam[i]) && !isnothing(sire[i])
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for j in 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 !isnothing(dam[i]) && isnothing(sire[i])
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for j in 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 isnothing(dam[i]) && !isnothing(sire[i])
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for j in 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 in 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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print("Done!\n")
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### Perform BLUP
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print("[🐮]: Solving the mixed-model equations...")
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# Extract the observed data
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Y = convert(Array{Float64}, data[:,5])
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# The random effects matrix
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Z = Matrix{Int}(I, numanimals, numanimals)
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# Remove items where there is no data
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nullobs = findall(isnothing, Y)
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Z[nullobs, nullobs] .= 0
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# Calculate heritability
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λ = (1-h2)/h2
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# Use the mixed-model equations
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MME = [X'*X X'*Z; Z'*X (Z'*Z)+(inv(A).*λ)]
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MMY = [X'*Y; Z'*Y]
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solutions = MME\MMY
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# Find the accuracies
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diaginv = diag(inv(MME))
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reliability = ones(Float64, length(diaginv)) - diaginv.*λ
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print("Done!\n")
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### Output the results
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print("[🐮]: 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 = open(savepath, "w")
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write(fileID, "beefblup Results Report\n")
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write(fileID, "Produced using beefblup for Julia (")
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write(fileID, "https://github.com/millironx/beefblup")
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write(fileID, ")\n\n")
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write(fileID, "Input:\t")
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write(fileID, path)
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write(fileID, "\nAnalysis performed:\t")
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write(fileID, string(Dates.today()))
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write(fileID, "\nTrait examined:\t")
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write(fileID, headers[5])
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write(fileID, "\n\n")
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# Print base population stats
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write(fileID, "Base Population:\n")
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for i in 1:length(numgroups)
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write(fileID, "\t")
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write(fileID, headers[i+5])
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write(fileID, ":\t")
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write(fileID, normal[i])
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write(fileID, "\n")
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end
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write(fileID, "\tMean ")
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write(fileID, headers[5])
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write(fileID, ":\t")
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write(fileID, string(solutions[1]))
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write(fileID, "\n\n")
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# Contemporary group adjustments
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counter = 2
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write(fileID, "Contemporary Group Effects:\n")
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for i in 1:length(numgroups)
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write(fileID, "\t")
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write(fileID, headers[i+5])
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write(fileID, "\tEffect\tReliability\n")
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for j in 1:numgroups[i]
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write(fileID, "\t")
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write(fileID, adjustedtraits[counter - 1])
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write(fileID, "\t")
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write(fileID, string(solutions[counter]))
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write(fileID, "\t")
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write(fileID, string(reliability[counter]))
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write(fileID, "\n")
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global counter = counter + 1
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end
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||||
write(fileID, "\n")
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||||
end
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write(fileID, "\n")
|
||||
|
||||
# Expected breeding values
|
||||
write(fileID, "Expected Breeding Values:\n")
|
||||
write(fileID, "\tID\tEBV\tReliability\n")
|
||||
for i in 1:numanimals
|
||||
write(fileID, "\t")
|
||||
write(fileID, ids[i])
|
||||
write(fileID, "\t")
|
||||
write(fileID, string(solutions[i+counter-1]))
|
||||
write(fileID, "\t")
|
||||
write(fileID, string(reliability[i+counter-1]))
|
||||
write(fileID, "\n")
|
||||
end
|
||||
|
||||
write(fileID, "\n - END REPORT -")
|
||||
close(fileID)
|
||||
|
||||
print("Done!\n")
|
||||
|
|
|
|||
|
|
@ -10,3 +10,4 @@ using Pkg
|
|||
|
||||
# Install requisite packages
|
||||
Pkg.add("XLSX")
|
||||
Pkg.add("Gtk")
|
||||
Loading…
Reference in a new issue