add pivot accessor

src/Xymat.jl

@@ -61,6 +61,15 @@ Base.copyto!(A::FeTerm{T}, src::AbstractVecOrMat{T}) where {T} = copyto!(A.x, sr
 
 Base.eltype(::FeTerm{T}) where {T} = T
 
+"""
+    pivot(m::MixedModel)
+    pivot(A::FeTerm)
+
+Return the pivot associated with the FeTerm.
+"""
+@inline pivot(m::MixedModel) = pivot(m.feterm)
+@inline pivot(A::FeTerm) = A.piv
+
 function fullrankx(A::FeTerm)
     x, rnk = A.x, A.rank
     return rnk == size(x, 2) ? x : view(x, :, 1:rnk)  # this handles the zero-columns case

src/generalizedlinearmixedmodel.jl

@@ -54,7 +54,7 @@ struct GeneralizedLinearMixedModel{T<:AbstractFloat,D<:Distribution} <: MixedMod
 end
 
 function StatsAPI.coef(m::GeneralizedLinearMixedModel{T}) where {T}
-    piv = m.LMM.feterm.piv
+    piv = pivot(m)
     return invpermute!(copyto!(fill(T(-0.0), length(piv)), m.β), piv)
 end
 

src/linearmixedmodel.jl

@@ -267,11 +267,11 @@ function StatsAPI.fit(
 end
 
 function StatsAPI.coef(m::LinearMixedModel{T}) where {T}
-    return coef!(Vector{T}(undef, length(m.feterm.piv)), m)
+    return coef!(Vector{T}(undef, length(pivot(m))), m)
 end
 
 function coef!(v::AbstractVector{Tv}, m::MixedModel{T}) where {Tv,T}
-    piv = m.feterm.piv
+    piv = pivot(m)
     return invpermute!(fixef!(v, m), piv)
 end
 
@@ -1243,12 +1243,12 @@ function stderror!(v::AbstractVector{Tv}, m::LinearMixedModel{T}) where {Tv,T}
         scr[i] = true
         v[i] = s * norm(ldiv!(L, scr))
     end
-    invpermute!(v, m.feterm.piv)
+    invpermute!(v, pivot(m))
     return v
 end
 
 function StatsAPI.stderror(m::LinearMixedModel{T}) where {T}
-    return stderror!(similar(m.feterm.piv, T), m)
+    return stderror!(similar(pivot(m), T), m)
 end
 
 """

src/predict.jl

@@ -70,7 +70,7 @@ Similarly, offsets are also not supported for `GeneralizedLinearMixedModel`.
 function StatsAPI.predict(
     m::LinearMixedModel, newdata::Tables.ColumnTable; new_re_levels=:missing
 )
-    return _predict(m, newdata, coef(m)[m.feterm.piv]; new_re_levels)
+    return _predict(m, newdata, coef(m)[pivot(m)]; new_re_levels)
 end
 
 function StatsAPI.predict(
@@ -81,7 +81,7 @@ function StatsAPI.predict(
 )
     type in (:linpred, :response) || throw(ArgumentError("Invalid value for type: $(type)"))
     # want pivoted but not truncated
-    y = _predict(m.LMM, newdata, coef(m)[m.feterm.piv]; new_re_levels)
+    y = _predict(m.LMM, newdata, coef(m)[pivot(m)]; new_re_levels)
 
     return type == :linpred ? y : broadcast!(Base.Fix1(linkinv, Link(m)), y, y)
 end
@@ -126,7 +126,7 @@ function _predict(m::MixedModel{T}, newdata, β; new_re_levels) where {T}
         ytemp, lmm
     end
 
-    pivotmatch = mnew.feterm.piv[m.feterm.piv]
+    pivotmatch = mnew.feterm.piv[pivot(m)]
     grps = fnames(m)
     mul!(y, view(mnew.X, :, pivotmatch), β)
     # mnew.reterms for the correct Z matrices

src/simulate.jl

@@ -149,16 +149,16 @@ end
 function simulate!(
     rng::AbstractRNG, y::AbstractVector, m::LinearMixedModel{T}; β=m.β, σ=m.σ, θ=m.θ
 ) where {T}
-    length(β) == length(m.feterm.piv) || length(β) == m.feterm.rank ||
+    length(β) == length(pivot(m)) || length(β) == m.feterm.rank ||
         throw(ArgumentError("You must specify all (non-singular) βs"))
 
     β = convert(Vector{T}, β)
     σ = T(σ)
     θ = convert(Vector{T}, θ)
     isempty(θ) || setθ!(m, θ)
 
-    if length(β) ≠ length(m.feterm.piv)
-        β = invpermute!(copyto!(fill(-0.0, length(m.feterm.piv)), β),
+    if length(β) ≠ length(pivot(m))
+        β = invpermute!(copyto!(fill(-0.0, length(pivot(m))), β),
             m.feterm.piv)
     end
 
@@ -228,7 +228,7 @@ function _simulate!(
     θ,
     resp=nothing,
 ) where {T}
-    length(β) == length(m.feterm.piv) || length(β) == m.feterm.rank ||
+    length(β) == length(pivot(m)) || length(β) == m.feterm.rank ||
         throw(ArgumentError("You must specify all (non-singular) βs"))
 
     dispersion_parameter(m) ||
@@ -249,7 +249,7 @@ function _simulate!(
 
     if length(β) == length(m.feterm.piv)
         # unlike LMM, GLMM stores the truncated, pivoted vector directly
-        β = β[view(m.feterm.piv, 1:(m.feterm.rank))]
+        β = β[view(pivot(m), 1:(m.feterm.rank))]
     end
     fast = (length(m.θ) == length(m.optsum.final))
     setpar! = fast ? setθ! : setβθ!