From a2357cfeca83d531f51e21a87ef410beccae0d8f Mon Sep 17 00:00:00 2001
From: Giuseppe Ragusa <giuseppe.ragusa@gmail.com>
Date: Wed, 11 Dec 2024 13:21:09 +0100
Subject: [PATCH] Fix problem with docstrings

---
 src/linpred.jl | 19 +++++++++----------
 src/lm.jl      | 12 ++++++------
 2 files changed, 15 insertions(+), 16 deletions(-)

diff --git a/src/linpred.jl b/src/linpred.jl
index 9a978626..cf4e856b 100644
--- a/src/linpred.jl
+++ b/src/linpred.jl
@@ -31,7 +31,6 @@ A `LinPred` type with a dense QR decomposition of `X`
 - `qr`: either a `QRCompactWY` or `QRPivoted` object created from `X`, with optional row weights.
 - `scratchm1`: scratch Matrix{T} of the same size as `X`
 """
-
 mutable struct DensePredQR{T<:BlasReal, Q<:Union{QRCompactWY, QRPivoted}, W<:AbstractWeights} <: DensePred
     X::Matrix{T}                  # model matrix
     beta0::Vector{T}              # base coefficient vector
@@ -454,18 +453,18 @@ function leverage(pp::DensePredChol{T, C, W}) where {T, C<:Cholesky, W}
 end
 
 function leverage(pp::DensePredQR{T, C, W}) where {T, C<:QRPivoted, W}
-  X = modelmatrix(pp; weighted=isweighted(pp))
-  _, k = size(X)
-  ch = pp.qr
-  rnk = length(ch.p)
-  p = ch.p
-  idx = invperm(p)[1:rnk]
-  sum(x -> x^2, view(X, :, 1:rnk)/ch.R[1:rnk, idx], dims=2)
+    X = modelmatrix(pp; weighted=isweighted(pp))
+    _, k = size(X)
+    ch = pp.qr
+    rnk = length(ch.p)
+    p = ch.p
+    idx = invperm(p)[1:rnk]
+    sum(x -> x^2, view(X, :, 1:rnk)/ch.R[1:rnk, idx], dims=2)
 end
 
 function leverage(pp::DensePredQR{T, C, W}) where {T, C<:Cholesky, W}
-  X = modelmatrix(pp; weighted=isweighted(pp))
-  sum(x -> x^2, X/pp.qr.R, dims=2)
+    X = modelmatrix(pp; weighted=isweighted(pp))
+    sum(x -> x^2, X/pp.qr.R, dims=2)
 end
 
 response(obj::LinPredModel) = obj.rr.y
diff --git a/src/lm.jl b/src/lm.jl
index ec00b72f..f1d55572 100644
--- a/src/lm.jl
+++ b/src/lm.jl
@@ -404,12 +404,6 @@ Compute [Cook's distance](https://en.wikipedia.org/wiki/Cook%27s_distance)
 for each observation in linear model `obj`, giving an estimate of the influence
 of each data point.
 """
-## To remove when https://github.com/JuliaStats/StatsAPI.jl/pull/16 is merged
-function crossmodelmatrix(model::RegressionModel; weighted::Bool=false)
-    x = weighted ? modelmatrix(model; weighted=weighted) : modelmatrix(model)
-    return Symmetric(x' * x)
-end
-
 function StatsBase.cooksdistance(obj::LinearModel)
     u = residuals(obj; weighted=isweighted(obj))
     mse = GLM.dispersion(obj,true)
@@ -418,3 +412,9 @@ function StatsBase.cooksdistance(obj::LinearModel)
     D = @. u^2 * (hii / (1 - hii)^2) / (k*mse)
     return D
 end
+
+## To remove when https://github.com/JuliaStats/StatsAPI.jl/pull/16 is merged
+function crossmodelmatrix(model::RegressionModel; weighted::Bool=false)
+    x = weighted ? modelmatrix(model; weighted=weighted) : modelmatrix(model)
+    return Symmetric(x' * x)
+end