Remove white space

src/ftest.jl

@@ -189,7 +189,7 @@ function show(io::IO, ftr::FTestResult{N}) where N
 
  # get rid of negative zero -- doesn't matter mathematically,
  # but messes up doctests and various other things
- # cf. Issue #461 
+ # cf. Issue #461
  r2vals = [replace(@sprintf("%.4f", val), "-0.0000" => "0.0000") for val in ftr.r2]
 
  outrows[2, :] = ["[1]", @sprintf("%.0d", ftr.dof[1]), " ",

src/glmfit.jl

@@ -625,7 +625,7 @@ function fit(::Type{M},
  off = offset === nothing ? similar(y, 0) : offset
  wts = wts === nothing ? similar(y, 0) : wts
  rr = GlmResp(y, d, l, off, wts)
- 
+
  if method === :cholesky
  res = M(rr, cholpred(X, dropcollinear), f, false)
  elseif method === :qr

src/glmtools.jl

@@ -501,7 +501,7 @@ dispersion_parameter(::Union{Bernoulli, Binomial, Poisson}) = false
 
 """
  _safe_int(x::T)
- 
+
 Convert to Int, when `x` is within 1 eps of an integer.
 """
 function _safe_int(x::T) where {T<:AbstractFloat}
@@ -527,7 +527,7 @@ function loglik_obs end
 loglik_obs(::Bernoulli, y, μ, wt, ϕ) = wt*logpdf(Bernoulli(μ), y)
 loglik_obs(::Binomial, y, μ, wt, ϕ) = logpdf(Binomial(Int(wt), μ), _safe_int(y*wt))
 loglik_obs(::Gamma, y, μ, wt, ϕ) = wt*logpdf(Gamma(inv(ϕ), μ*ϕ), y)
-# In Distributions.jl, a Geometric distribution characterizes the number of failures before 
+# In Distributions.jl, a Geometric distribution characterizes the number of failures before
 # the first success in a sequence of independent Bernoulli trials with success rate p.
 # The mean of Geometric distribution is (1 - p) / p.
 # Hence, p = 1 / (1 + μ).

src/linpred.jl

@@ -38,7 +38,7 @@ mutable struct DensePredQR{T<:BlasReal,Q<:Union{QRCompactWY, QRPivoted}} <: Dens
  scratchbeta::Vector{T}
  qr::Q
  scratchm1::Matrix{T}
- 
+
  function DensePredQR(X::AbstractMatrix, pivot::Bool=false)
  n, p = size(X)
  T = typeof(float(zero(eltype(X))))
@@ -74,7 +74,7 @@ function delbeta!(p::DensePredQR{T,<:QRCompactWY}, r::Vector{T}, wt::Vector{T})
  rnk == length(p.delbeta) || throw(RankDeficientException(rnk))
  X = p.X
  W = Diagonal(wt)
- sqrtW = Diagonal(sqrt.(wt)) 
+ sqrtW = Diagonal(sqrt.(wt))
  mul!(p.scratchm1, sqrtW, X)
  mul!(p.delbeta, X'W, r)
  qnr = qr(p.scratchm1)
@@ -88,7 +88,7 @@ function delbeta!(p::DensePredQR{T,<:QRPivoted}, r::Vector{T}) where T<:BlasReal
  if rnk == length(p.delbeta)
  p.delbeta = p.qr\r
  else
- R = @view p.qr.R[:, 1:rnk] 
+ R = @view p.qr.R[:, 1:rnk]
  Q = @view p.qr.Q[:, 1:size(R, 1)]
  piv = p.qr.p
  p.delbeta = zeros(size(p.delbeta))

src/lm.jl

@@ -136,7 +136,7 @@ function fit(::Type{LinearModel}, X::AbstractMatrix{<:Real}, y::AbstractVector{<
  "argument `dropcollinear` instead. Proceeding with positional argument value: $allowrankdeficient_dep"
  dropcollinear = allowrankdeficient_dep
  end
- 
+
  if method === :cholesky
  fit!(LinearModel(LmResp(y, wts), cholpred(X, dropcollinear), nothing))
  elseif method === :qr
@@ -299,7 +299,7 @@ function StatsModels.predict!(res::Union{AbstractVector,
  length(res) == size(newx, 1) ||
  throw(DimensionMismatch("length of `res` must equal the number of rows in `newx`"))
  res .= newx * coef(mm)
- elseif mm.pp isa DensePredChol && 
+ elseif mm.pp isa DensePredChol &&
  mm.pp.chol isa CholeskyPivoted &&
  mm.pp.chol.rank < size(mm.pp.chol, 2)
  throw(ArgumentError("prediction intervals are currently not implemented " *

test/runtests.jl

@@ -3,6 +3,7 @@ using CategoricalArrays, CSV, DataFrames, LinearAlgebra, SparseArrays, StableRNG
 using GLM
 using StatsFuns: logistic
 using Distributions: TDist
+using Downloads
 
 test_show(x) = show(IOBuffer(), x)
 
@@ -28,7 +29,7 @@ linreg(x::AbstractVecOrMat, y::AbstractVector) = qr!(simplemm(x)) \ y
  lm1 = fit(LinearModel, @formula(OptDen ~ Carb), form; method=dmethod)
  test_show(lm1)
  @test isapprox(coef(lm1), linreg(form.Carb, form.OptDen))
- 
+
  @test isapprox(vcov(lm1), Σ)
  @test isapprox(cor(lm1), Diagonal(diag(Σ))^(-1/2)*Σ*Diagonal(diag(Σ))^(-1/2))
  @test dof(lm1) == 3
@@ -72,22 +73,22 @@ linreg(x::AbstractVecOrMat, y::AbstractVector) = qr!(simplemm(x)) \ y
 end
 
 @testset "Cook's Distance in Linear Model with $dmethod" for dmethod in (:cholesky, :qr)
- st_df = DataFrame( 
+ st_df = DataFrame(
  Y=[6.4, 7.4, 10.4, 15.1, 12.3 , 11.4],
  XA=[1.5, 6.5, 11.5, 19.9, 17.0, 15.5],
- XB=[1.8, 7.8, 11.8, 20.5, 17.3, 15.8], 
+ XB=[1.8, 7.8, 11.8, 20.5, 17.3, 15.8],
  XC=[3., 13., 23., 39.8, 34., 31.],
  # values from SAS proc reg
- CooksD_base=[1.4068501943, 0.176809102, 0.0026655177, 1.0704009915, 0.0875726457, 0.1331183932], 
- CooksD_noint=[0.0076891801, 0.0302993877, 0.0410262965, 0.0294348488, 0.0691589296, 0.0273045538], 
+ CooksD_base=[1.4068501943, 0.176809102, 0.0026655177, 1.0704009915, 0.0875726457, 0.1331183932],
+ CooksD_noint=[0.0076891801, 0.0302993877, 0.0410262965, 0.0294348488, 0.0691589296, 0.0273045538],
  CooksD_multi=[1.7122291956, 18.983407026, 0.000118078, 0.8470797843, 0.0715921999, 0.1105843157],
  )
 
  # linear regression
  t_lm_base = lm(@formula(Y ~ XA), st_df; method=dmethod)
  @test isapprox(st_df.CooksD_base, cooksdistance(t_lm_base))
 
- # linear regression, no intercept 
+ # linear regression, no intercept
  t_lm_noint = lm(@formula(Y ~ XA +0), st_df; method=dmethod)
  @test isapprox(st_df.CooksD_noint, cooksdistance(t_lm_noint))
 
@@ -102,20 +103,20 @@ end
  @test_throws ArgumentError isapprox(st_df.CooksD_base, cooksdistance(t_lm_colli))
 end
 
-@testset "Linear model with weights and $dmethod" for dmethod in (:cholesky, :qr) 
+@testset "Linear model with weights and $dmethod" for dmethod in (:cholesky, :qr)
  df = dataset("quantreg", "engel")
  N = nrow(df)
  df.weights = repeat(1:5, Int(N/5))
  f = @formula(FoodExp ~ Income)
- 
+
  lm_model = lm(f, df, wts = df.weights; method=dmethod)
  glm_model = glm(f, df, Normal(), wts = df.weights; method=dmethod)
  @test isapprox(coef(lm_model), [154.35104595140706, 0.4836896390157505])
  @test isapprox(coef(glm_model), [154.35104595140706, 0.4836896390157505])
  @test isapprox(stderror(lm_model), [9.382302620120193, 0.00816741377772968])
  @test isapprox(r2(lm_model), 0.8330258148644486)
  @test isapprox(adjr2(lm_model), 0.832788298242634)
- @test isapprox(vcov(lm_model), [88.02760245551447 -0.06772589439264813; 
+ @test isapprox(vcov(lm_model), [88.02760245551447 -0.06772589439264813;
  -0.06772589439264813 6.670664781664879e-5])
  @test isapprox(first(predict(lm_model)), 357.57694841780994)
  @test isapprox(loglikelihood(lm_model), -4353.946729075838)
@@ -153,7 +154,7 @@ end
  X = ModelMatrix(ModelFrame(f, dfrm)).m
  y = X * (1:size(X, 2)) + 0.1 * randn(rng, size(X, 1))
  inds = deleteat!(collect(1:length(y)), 7:8)
- 
+
  m1 = fit(LinearModel, X, y; method=dmethod)
  @test isapprox(deviance(m1), 0.12160301538297297)
  Xmissingcell = X[inds, :]
@@ -170,23 +171,23 @@ end
  @test isa(m2p.pp.chol, CholeskyPivoted)
  @test isapprox(coef(m2p), [0.9772643585228885, 8.903341608496437, 3.027347397503281,
  3.9661379199401257, 5.079410103608552, 6.1944618141188625,
- 0.0, 7.930328728005131, 8.879994918604757, 2.986388408421915, 
+ 0.0, 7.930328728005131, 8.879994918604757, 2.986388408421915,
  10.84972230524356, 11.844809275711485])
- 
+
  @test isa(m2p_dep_pos.pp.chol, CholeskyPivoted)
  @test isa(m2p_dep_pos_kw.pp.chol, CholeskyPivoted)
  elseif dmethod == :qr
  @test_throws RankDeficientException m2 = fit(LinearModel, Xmissingcell, ymissingcell;
  method = dmethod, dropcollinear=false)
  @test isapprox(coef(m2p), [0.9772643585228962, 11.889730016918342, 3.027347397503282,
  3.9661379199401177, 5.079410103608539, 6.194461814118862,
- -2.9863884084219015, 7.930328728005132, 8.87999491860477, 
+ -2.9863884084219015, 7.930328728005132, 8.87999491860477,
  0.0, 10.849722305243555, 11.844809275711487]) ||
  isapprox(coef(m2p), [0.9772643585228885, 8.903341608496437, 3.027347397503281,
  3.9661379199401257, 5.079410103608552, 6.1944618141188625,
- 0.0, 7.930328728005131, 8.879994918604757, 2.986388408421915, 
+ 0.0, 7.930328728005131, 8.879994918604757, 2.986388408421915,
  10.84972230524356, 11.844809275711485])
- 
+
  @test isa(m2p.pp.qr, QRPivoted)
 
  @test isa(m2p_dep_pos.pp.qr, QRPivoted)
@@ -198,7 +199,7 @@ end
 
  @test GLM.linpred_rank(m2p.pp) == 11
  @test isapprox(deviance(m2p), 0.1215758392280204)
- 
+
  @test GLM.linpred_rank(m2p_dep_pos.pp) == GLM.linpred_rank(m2p.pp)
  @test isapprox(deviance(m2p_dep_pos), deviance(m2p))
  @test isapprox(coef(m2p_dep_pos), coef(m2p))
@@ -210,7 +211,7 @@ end
 
 @testset "Saturated linear model with $dmethod" for dmethod in (:cholesky, :qr)
  df1 = DataFrame(x=["a", "b", "c"], y=[1, 2, 3])
- 
+
  model = lm(@formula(y ~ x), df1; method=dmethod)
  ct = coeftable(model)
  @test dof_residual(model) == 0
@@ -279,7 +280,7 @@ end
  # https://www.itl.nist.gov/div898/strd/lls/data/LINKS/DATA/NoInt1.dat
 
  data = DataFrame(x = 60:70, y = 130:140)
- 
+
  mdl = lm(@formula(y ~ 0 + x), data; method=:cholesky)
  @test coef(mdl) ≈ [2.07438016528926]
  @test stderror(mdl) ≈ [0.165289256198347E-01]
@@ -303,7 +304,7 @@ end
  # https://www.itl.nist.gov/div898/strd/lls/data/LINKS/DATA/NoInt2.dat
 
  data = DataFrame(x = [4, 5, 6], y = [3, 4, 4])
- 
+
  mdl = lm(@formula(y ~ 0 + x), data; method=:cholesky)
  @test coef(mdl) ≈ [0.727272727272727]
  @test stderror(mdl) ≈ [0.420827318078432E-01]
@@ -324,7 +325,7 @@ end
  X = [4 5 6]'
  y = [3, 4, 4]
  data = DataFrame(x = [4, 5, 6], y = [3, 4, 4])
- 
+
  mdl1 = lm(@formula(y ~ 0 + x), data; method=dmethod)
  mdl2 = lm(X, y)
 
@@ -811,8 +812,8 @@ end
  @test aic(gm22) ≈ 1112.7422553284146
  @test aicc(gm22) ≈ 1113.7933502189255
  @test bic(gm22) ≈ 1136.6111083020812
- @test coef(gm22)[1:7] ≈ [2.8978546663153897, -0.5701067649409168, 0.08040181505082235, 
- -0.4497584898742737, 0.08622664933901254, 0.3558996662512287, 
+ @test coef(gm22)[1:7] ≈ [2.8978546663153897, -0.5701067649409168, 0.08040181505082235,
+ -0.4497584898742737, 0.08622664933901254, 0.3558996662512287,
  0.29016080736927813]
  @test stderror(gm22) ≈ [0.22754287093719366, 0.15274755092180423, 0.15928431669166637,
  0.23853372776980591, 0.2354231414867577, 0.24750780320597515,
@@ -1405,7 +1406,7 @@ end
  ────────────────────────────────────────────────────────────────
  DOF ΔDOF SSR ΔSSR R² ΔR² F* p(>F)
  ────────────────────────────────────────────────────────────────
- [1] 2 3.2292 0.0000 
+ [1] 2 3.2292 0.0000  
  [2] 3 1 0.1283 -3.1008 0.9603 0.9603 241.6234 <1e-7
  ────────────────────────────────────────────────────────────────"""
  end
@@ -1852,7 +1853,7 @@ end
 end
 
 @testset "dropcollinear in GLM with Cholesky" begin
- data = DataFrame(x1=[4, 5, 9, 6, 5], x2=[5, 3, 6, 7, 1], 
+ data = DataFrame(x1=[4, 5, 9, 6, 5], x2=[5, 3, 6, 7, 1],
  x3=[4.2, 4.6, 8.4, 6.2, 4.2], y=[14, 14, 24, 20, 11])
 
  @testset "Check normal with identity link against equivalent linear model" begin
@@ -2001,7 +2002,7 @@ end
  # see issue 503
  y, μ, wt, ϕ = 0.6376811594202898, 0.8492925285671102, 69.0, NaN
  # due to floating point:
- # 1. y * wt == 43.99999999999999 
+ # 1. y * wt == 43.99999999999999
  # 2. 44 / y == wt
  # 3. 44 / wt == y
  @test GLM.loglik_obs(Binomial(), y, μ, wt, ϕ) ≈ GLM.logpdf(Binomial(Int(wt), μ), 44)
@@ -2018,8 +2019,8 @@ end
  # > data(Duncan)
  # > lm1 = lm(prestige ~ 1 + income + education, Duncan)
  # > vif(lm1)
- # income education 
- # 2.1049 2.1049 
+ # income education
+ # 2.1049 2.1049
  # > lm2 = lm(prestige ~ 1 + income + education + type, Duncan)
  # > vif(lm2)
  # GVIF Df GVIF^(1/(2*Df))
@@ -2030,26 +2031,26 @@ end
  lm1 = lm(@formula(Prestige ~ 1 + Income + Education), duncan)
  @test termnames(lm1)[2] == coefnames(lm1)
  @test vif(lm1) ≈ gvif(lm1)
- 
+
  lm1_noform = lm(modelmatrix(lm1), response(lm1))
  @test vif(lm1) ≈ vif(lm1_noform)
  @test_throws ArgumentError("model was fitted without a formula") gvif(lm1_noform)
- 
+
  lm1log = lm(@formula(Prestige ~ 1 + exp(log(Income)) + exp(log(Education))), duncan)
  @test termnames(lm1log)[2] == coefnames(lm1log) == ["(Intercept)", "exp(log(Income))", "exp(log(Education))"]
  @test vif(lm1) ≈ vif(lm1log)
- 
+
  gm1 = glm(modelmatrix(lm1), response(lm1), Normal())
  @test vif(lm1) ≈ vif(gm1)
- 
+
  lm2 = lm(@formula(Prestige ~ 1 + Income + Education + Type), duncan)
  @test termnames(lm2)[2] != coefnames(lm2)
  @test gvif(lm2; scale=true) ≈ [1.486330, 2.301648, 1.502666] atol=1e-4
- 
+
  gm2 = glm(@formula(Prestige ~ 1 + Income + Education + Type), duncan, Normal())
  @test termnames(gm2)[2] != coefnames(gm2)
- @test gvif(gm2; scale=true) ≈ [1.486330, 2.301648, 1.502666] atol=1e-4 
- 
+ @test gvif(gm2; scale=true) ≈ [1.486330, 2.301648, 1.502666] atol=1e-4
+
  # the VIF definition depends on modelmatrix, vcov and stderror returning valid
  # values. It doesn't care about links, offsets, etc. as long as the model matrix,
  # vcov matrix and stderrors are well defined.