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fix Int, minor changes #41

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Dec 5, 2023
Merged

fix Int, minor changes #41

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9726893
fix Int, minor changes
Dec 4, 2023
fac0571
Merge branch 'master-test' of https://github.com/PharmCat/Metida.jl i…
PharmCat Dec 4, 2023
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2 changes: 1 addition & 1 deletion src/fit.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -132,7 +132,7 @@ function fit!(lmm::LMM{T}; kwargs...) where T
error("init length $(length(init)) != θ length $(length(θ))")
end
else
initθ = sqrt(initvar(lmm.data.yv, lmm.mm.m)[1])/(length(lmm.covstr.random)+1)
initθ = sqrt(initvar(lmm.data.yv, lmm.data.xv)[1])/(length(lmm.covstr.random)+1)
for i = 1:length(θ)
if lmm.covstr.ct[i] == :var
θ[i] = initθ
Expand Down
12 changes: 7 additions & 5 deletions src/lmm.jl
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Expand Up @@ -26,7 +26,7 @@ struct LMM{T<:AbstractFloat} <: MetidaModel
model::FormulaTerm
mf::ModelFrame
mm::ModelMatrix
covstr::CovStructure{T}
covstr::CovStructure
data::LMMData{T}
dv::LMMDataViews{T}
nfixed::Int
Expand All @@ -38,15 +38,15 @@ struct LMM{T<:AbstractFloat} <: MetidaModel
function LMM(model::FormulaTerm,
mf::ModelFrame,
mm::ModelMatrix,
covstr::CovStructure{T},
covstr::CovStructure,
data::LMMData{T},
dv::LMMDataViews{T},
nfixed::Int,
rankx::Int,
result::ModelResult,
maxvcbl::Int,
log::Vector{LMMLogMsg}) where T
new{eltype(mm.m)}(model, mf, mm, covstr, data, dv, nfixed, rankx, result, maxvcbl, log)
new{T}(model, mf, mm, covstr, data, dv, nfixed, rankx, result, maxvcbl, log)
end
function LMM(model, data; contrasts=Dict{Symbol,Any}(), random::Union{Nothing, VarEffect, Vector{VarEffect}} = nothing, repeated::Union{Nothing, VarEffect} = nothing)
#need check responce - Float
Expand Down Expand Up @@ -86,11 +86,13 @@ struct LMM{T<:AbstractFloat} <: MetidaModel
end
lmmdata = LMMData(modelmatrix(mf), response(mf))
covstr = CovStructure(random, repeated, data)
coefn = size(lmmdata.xv, 2)
rankx = rank(lmmdata.xv)
if rankx != size(lmmdata.xv, 2)
if rankx != coefn
@warn "Fixed-effect matrix not full-rank!"
lmmlog!(lmmlog, 1, LMMLogMsg(:WARN, "Fixed-effect matrix not full-rank!"))
end
mres = ModelResult(false, nothing, fill(NaN, covstr.tl), NaN, fill(NaN, rankx), nothing, fill(NaN, rankx, rankx), fill(NaN, rankx), nothing, false)
mres = ModelResult(false, nothing, fill(NaN, covstr.tl), NaN, fill(NaN, coefn), nothing, fill(NaN, coefn, coefn), fill(NaN, coefn), nothing, false)
LMM(model, mf, mm, covstr, lmmdata, LMMDataViews(lmmdata.xv, lmmdata.yv, covstr.vcovblock), nfixed, rankx, mres, findmax(length, covstr.vcovblock)[1], lmmlog)
end
function LMM(f::LMMformula, data; contrasts=Dict{Symbol,Any}(), kwargs...)
Expand Down
5 changes: 4 additions & 1 deletion src/lmmdata.jl
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Expand Up @@ -5,9 +5,12 @@ struct LMMData{T}
xv::Matrix{Float64}
# Responce vector
yv::Vector{T}
function LMMData(xa::Matrix{Float64}, ya::Vector{T}) where T
function LMMData(xa::AbstractMatrix{Float64}, ya::AbstractVector{T}) where T
new{T}(xa, ya)
end
function LMMData(xa::AbstractMatrix{Float64}, ya::AbstractVector{Int})
LMMData(xa, float.(ya))
end
end

struct LMMDataViews{T} <: AbstractLMMDataBlocks
Expand Down
37 changes: 31 additions & 6 deletions src/reml.jl
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Expand Up @@ -64,7 +64,7 @@ function reml_sweep_β(lmm, data, θ::Vector{T}; maxthreads::Int = 16) where T #
@inbounds for j ∈ 1:d + (t ≤ r)
i = offset + j
q = length(lmm.covstr.vcovblock[i])
qswm = q + lmm.rankx
qswm = q + p
Vp = zeros(T, qswm, qswm)
V = view(Vp, 1:q, 1:q)
Vx = view(Vp, 1:q, q+1:qswm)
Expand Down Expand Up @@ -103,19 +103,43 @@ function reml_sweep_β(lmm, data, θ::Vector{T}; maxthreads::Int = 16) where T #
θ₃ += mulθ₃(data.yv[i], data.xv[i], β, V⁻¹[i])
end
else
#=
qrd = qr(θs₂)
vec = collect(1:length(βm))
dr = diag(qrd.R)
tval = mean(dr)*sqrt(eps())
inds = Int[]
for i = 1:length(βm)
if dr[i] > tval
push!(inds, i)
else
θ₂[:, i] .= zero(T)
θ₂[i, :] .= zero(T)
βm[i] = zero(T)
end
end
rθs₂ = θs₂[inds, inds]
mul!(view(β, inds), inv(rθs₂), view(βm, inds))
logdetθ₂ = logdet(rθs₂)
=#
β .= NaN
return Inf, β, Inf, Inf, false
end
# θ₃
#@inbounds @simd for i = 1:n
# θ₃ += mulθ₃(data.yv[i], data.xv[i], β, V⁻¹[i])
#end
return θ₁ + logdetθ₂ + θ₃ + c, β, θs₂, θ₃, noerror #REML, β, iC, θ₃, errors
end
# Using BLAS, LAPACK - non ForwardDiff, used by MetidaNLopt
function reml_sweep_β_nlopt(lmm, data, θ::Vector{T}; maxthreads::Int = 16) where T
n = length(lmm.covstr.vcovblock)
N = length(lmm.data.yv)
c = (N - lmm.rankx)*log(2π)
p = size(lmm.data.xv, 2)
#---------------------------------------------------------------------------
θ₁ = zero(T)
θ₂ = zeros(T, lmm.rankx, lmm.rankx)
θ₂ = zeros(T, p, p)
θ₃ = zero(T)
A = Vector{Matrix{T}}(undef, n)
logdetθ₂ = zero(T)
Expand All @@ -130,8 +154,8 @@ function reml_sweep_β_nlopt(lmm, data, θ::Vector{T}; maxthreads::Int = 16) whe
d, r = divrem(n, ncore)
Base.Threads.@threads for t = 1:ncore
offset = min(t-1, r) + (t-1)*d
accθ₂[t] = zeros(T, lmm.rankx, lmm.rankx)
accβm[t] = zeros(T, lmm.rankx)
accθ₂[t] = zeros(T, p, p)
accβm[t] = zeros(T, p)
@inbounds for j ∈ 1:d+(t ≤ r)
i = offset + j
q = length(lmm.covstr.vcovblock[i])
Expand Down Expand Up @@ -188,6 +212,7 @@ end
################################################################################
function core_sweep_β(lmm, data, θ::Vector{T}, β, n; maxthreads::Int = 16) where T
ncore = min(num_cores(), n, maxthreads)
p = size(lmm.data.xv, 2)
accθ₁ = zeros(T, ncore)
accθ₂ = Vector{Matrix{T}}(undef, ncore)
accθ₃ = zeros(T, ncore)
Expand All @@ -197,11 +222,11 @@ function core_sweep_β(lmm, data, θ::Vector{T}, β, n; maxthreads::Int = 16) wh
d, r = divrem(n, ncore)
Base.Threads.@threads for t = 1:ncore
offset = min(t-1, r) + (t-1)*d
accθ₂[t] = zeros(T, lmm.rankx, lmm.rankx)
accθ₂[t] = zeros(T, p, p)
@inbounds for j ∈ 1:d+(t ≤ r)
i = offset + j
q = length(lmm.covstr.vcovblock[i])
qswm = q + lmm.rankx
qswm = q + p
Vp = zeros(T, qswm, qswm)
V = view(Vp, 1:q, 1:q)
Vx = view(Vp, 1:q, q+1:qswm)
Expand Down
2 changes: 1 addition & 1 deletion src/utils.jl
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Expand Up @@ -51,7 +51,7 @@ L-contrast matrix for `i` fixed effect.
"""
function lcontrast(lmm::LMM, i::Int)
n = length(lmm.mf.f.rhs.terms)
p = size(lmm.mm.m, 2)
p = size(lmm.data.xv, 2)
if i > n || n < 1 error("Factor number out of range 1-$(n)") end
inds = findall(x -> x==i, lmm.mm.assign)
if typeof(lmm.mf.f.rhs.terms[i]) <: CategoricalTerm
Expand Down
7 changes: 7 additions & 0 deletions test/test.jl
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Expand Up @@ -169,6 +169,13 @@ include("testdata.jl")
@test tt.ndf[2] ≈ 3.0 atol=1E-5
@test tt.df[2] ≈ 3.39086 atol=1E-5
@test tt.pval[2] ≈ 0.900636 atol=1E-5

# Int dependent variable, function Term in random part
df0.varint = Int.(ceil.(df0.var2))
lmmint = Metida.fit(Metida.LMM, Metida.@lmmformula(varint~formulation,
random = 1+var^2|subject:Metida.SI), df0)
Metida.fit!(lmmint)
@test Metida.m2logreml(lmmint) ≈ 84.23373276096902 atol=1E-6
end
################################################################################
# df0
Expand Down
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