Skip to content

Match svyglm for ProbabilityWeights deviance, nulldeviance, and SE #648

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
gragusa wants to merge 1 commit into
base: nl/zerowts
Choose a base branch
from
Open

Match svyglm for ProbabilityWeights deviance, nulldeviance, and SE #648

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions src/glmfit.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -80,7 +80,7 @@ end
function deviance(r::GlmResp)
wts = weights(r)
d = sum(r.devresid)
return wts isa ProbabilityWeights ? d * nobs(r) / sum(wts) : d
return wts isa ProbabilityWeights ? d * length(r.y) / sum(wts) : d
end

weights(r::GlmResp) = r.weights
Expand Down Expand Up @@ -323,7 +323,7 @@ function nulldeviance(m::GeneralizedLinearModel)
end
end
if wts isa ProbabilityWeights
dev /= sum(wts) / nobs(m)
dev /= sum(wts) / length(y)
end
else
X = fill(1.0, length(y), hasint ? 1 : 0)
Expand Down
6 changes: 4 additions & 2 deletions src/linpred.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -313,8 +313,10 @@ working_weights(x::LinPredModel) = working_weights(x.rr)
function vcov(x::LinPredModel)
if weights(x) isa ProbabilityWeights
## n-1 degrees of freedom - This is coherent with the `R` package `survey`,
## `STATA` uses n-k
s = nobs(x) / (nobs(x) - 1)
## `STATA` uses n-k. Use the full sample size (including zero-weighted obs)
## so the correction matches `survey::svyglm`.
n = length(response(x))
s = n / (n - 1)
mm = momentmatrix(x)
A = invloglikhessian(x)
if distr(x) isa Union{Gamma,InverseGaussian}
Expand Down
4 changes: 2 additions & 2 deletions src/lm.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -66,7 +66,7 @@ function deviance(r::LmResp)
v += abs2(y[i] - mu[i]) * weights[i]
end
end
return weights isa ProbabilityWeights ? v ./ (sum(weights) / nobs(r)) : v
return weights isa ProbabilityWeights ? v ./ (sum(weights) / length(r.y)) : v
end

weights(r::LmResp) = r.weights
Expand Down Expand Up @@ -235,7 +235,7 @@ function nulldeviance(obj::LinearModel)
v += abs2(y[i] - m) * weights[i]
end
end
return v
return weights isa ProbabilityWeights ? v / (sum(weights) / length(y)) : v
end

function nullloglikelihood(m::LinearModel)
Expand Down
5 changes: 3 additions & 2 deletions test/runtests.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2743,7 +2743,8 @@ end
glmod = glm(X, y, Normal(), weights=pweights(wts))
@test nobs(lmod) == nobs(glmod) == count(!iszero, wts)
@test dof_residual(lmod) == dof_residual(glmod) == count(!iszero, wts) - 2
@test deviance(lmod) ≈ deviance(glmod) ≈ 24.500813008130084
@test deviance(lmod) ≈ deviance(glmod) ≈ 30.626016260162601
@test nulldeviance(lmod) ≈ nulldeviance(glmod) ≈ 33.288888888888884
@test coef(lmod) ≈ coef(glmod) ≈ [3.6707317073170724, 0.20731707317073195]
@test stderror(lmod) ≈ stderror(glmod) ≈ [1.7617126628715203, 0.23455696986842048]
@test stderror(lmod) ≈ stderror(glmod) ≈ [1.7370721114074674, 0.2312762912372783]
end
Loading