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Merged
merged 11 commits into from
Jul 12, 2023
Merged

New Feature: Fix and improve coeftable for optimize() output #2034

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ba63fda
Fix #2033
DominiqueMakowski Jul 10, 2023
f4c6876
Propose additional columns
DominiqueMakowski Jul 10, 2023
ba7d7f3
Add other suggestions
DominiqueMakowski Jul 10, 2023
551cc95
Merge branch 'master' into master
DominiqueMakowski Jul 10, 2023
f5dec2e
Update src/modes/OptimInterface.jl
DominiqueMakowski Jul 10, 2023
52f4605
Update src/modes/OptimInterface.jl
DominiqueMakowski Jul 10, 2023
e5d7774
Update src/modes/OptimInterface.jl
DominiqueMakowski Jul 10, 2023
aae9d43
bump version
yebai Jul 10, 2023
41e1331
import SsStatsAPI
DominiqueMakowski Jul 11, 2023
db2f9b4
Update Project.toml
devmotion Jul 11, 2023
b27b394
Update src/modes/OptimInterface.jl
yebai Jul 11, 2023
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101 changes: 55 additions & 46 deletions src/modes/OptimInterface.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -11,27 +11,27 @@ import Printf

"""
ModeResult{
V<:NamedArrays.NamedArray,
M<:NamedArrays.NamedArray,
O<:Optim.MultivariateOptimizationResults,
V<:NamedArrays.NamedArray,
M<:NamedArrays.NamedArray,
O<:Optim.MultivariateOptimizationResults,
S<:NamedArrays.NamedArray
}

A wrapper struct to store various results from a MAP or MLE estimation.
"""
struct ModeResult{
V<:NamedArrays.NamedArray,
V<:NamedArrays.NamedArray,
O<:Optim.MultivariateOptimizationResults,
M<:OptimLogDensity
} <: StatsBase.StatisticalModel
"A vector with the resulting point estimates."
values :: V
values::V
"The stored Optim.jl results."
optim_result :: O
optim_result::O
"The final log likelihood or log joint, depending on whether `MAP` or `MLE` was run."
lp :: Float64
lp::Float64
"The evaluation function used to calculate the output."
f :: M
f::M
end
#############################
# Various StatsBase methods #
Expand All @@ -50,14 +50,23 @@ function Base.show(io::IO, m::ModeResult)
show(io, m.values.array)
end

function StatsBase.coeftable(m::ModeResult)
function StatsBase.coeftable(m::ModeResult; level::Real=0.95)
# Get columns for coeftable.
terms = StatsBase.coefnames(m)
estimates = m.values.array[:,1]
terms = String.(StatsBase.coefnames(m))
estimates = m.values.array[:, 1]
stderrors = StatsBase.stderror(m)
tstats = estimates ./ stderrors

StatsBase.CoefTable([estimates, stderrors, tstats], ["estimate", "stderror", "tstat"], terms)
zscore = estimates ./ stderrors
p = 2 * (1 .- cdf.(Normal(0, 1), abs.(zscore)))

# Confidence interval (CI)
q = quantile(Normal(0, 1), 1 - (1 - level) / 2)
ci_low = estimates .- q .* stderrors
ci_high = estimates .+ q .* stderrors

StatsBase.CoefTable(
[estimates, stderrors, zscore, p, ci_low, ci_high],
["Coef.", "Std. Error", "z", "Pr(>|z|)", "Lower 95%", "Upper 95%"],
terms)
end

function StatsBase.informationmatrix(m::ModeResult; hessian_function=ForwardDiff.hessian, kwargs...)
Expand Down Expand Up @@ -113,7 +122,7 @@ mle = optimize(model, MLE())
mle = optimize(model, MLE(), NelderMead())
```
"""
function Optim.optimize(model::Model, ::MLE, options::Optim.Options=Optim.Options(); kwargs...)
function Optim.optimize(model::Model, ::MLE, options::Optim.Options=Optim.Options(); kwargs...)
return _mle_optimize(model, options; kwargs...)
end
function Optim.optimize(model::Model, ::MLE, init_vals::AbstractArray, options::Optim.Options=Optim.Options(); kwargs...)
Expand All @@ -123,11 +132,11 @@ function Optim.optimize(model::Model, ::MLE, optimizer::Optim.AbstractOptimizer,
return _mle_optimize(model, optimizer, options; kwargs...)
end
function Optim.optimize(
model::Model,
::MLE,
init_vals::AbstractArray,
optimizer::Optim.AbstractOptimizer,
options::Optim.Options=Optim.Options();
model::Model,
::MLE,
init_vals::AbstractArray,
optimizer::Optim.AbstractOptimizer,
options::Optim.Options=Optim.Options();
kwargs...
)
return _mle_optimize(model, init_vals, optimizer, options; kwargs...)
Expand Down Expand Up @@ -159,7 +168,7 @@ map_est = optimize(model, MAP(), NelderMead())
```
"""

function Optim.optimize(model::Model, ::MAP, options::Optim.Options=Optim.Options(); kwargs...)
function Optim.optimize(model::Model, ::MAP, options::Optim.Options=Optim.Options(); kwargs...)
return _map_optimize(model, options; kwargs...)
end
function Optim.optimize(model::Model, ::MAP, init_vals::AbstractArray, options::Optim.Options=Optim.Options(); kwargs...)
Expand All @@ -169,11 +178,11 @@ function Optim.optimize(model::Model, ::MAP, optimizer::Optim.AbstractOptimizer,
return _map_optimize(model, optimizer, options; kwargs...)
end
function Optim.optimize(
model::Model,
::MAP,
init_vals::AbstractArray,
optimizer::Optim.AbstractOptimizer,
options::Optim.Options=Optim.Options();
model::Model,
::MAP,
init_vals::AbstractArray,
optimizer::Optim.AbstractOptimizer,
options::Optim.Options=Optim.Options();
kwargs...
)
return _map_optimize(model, init_vals, optimizer, options; kwargs...)
Expand All @@ -190,43 +199,43 @@ end
Estimate a mode, i.e., compute a MLE or MAP estimate.
"""
function _optimize(
model::Model,
f::OptimLogDensity,
optimizer::Optim.AbstractOptimizer = Optim.LBFGS(),
args...;
model::Model,
f::OptimLogDensity,
optimizer::Optim.AbstractOptimizer=Optim.LBFGS(),
args...;
kwargs...
)
return _optimize(model, f, DynamicPPL.getparams(f), optimizer, args...; kwargs...)
end

function _optimize(
model::Model,
f::OptimLogDensity,
options::Optim.Options = Optim.Options(),
args...;
model::Model,
f::OptimLogDensity,
options::Optim.Options=Optim.Options(),
args...;
kwargs...
)
return _optimize(model, f, DynamicPPL.getparams(f), Optim.LBFGS(), args...; kwargs...)
end

function _optimize(
model::Model,
f::OptimLogDensity,
init_vals::AbstractArray = DynamicPPL.getparams(f),
options::Optim.Options = Optim.Options(),
args...;
model::Model,
f::OptimLogDensity,
init_vals::AbstractArray=DynamicPPL.getparams(f),
options::Optim.Options=Optim.Options(),
args...;
kwargs...
)
return _optimize(model, f, init_vals, Optim.LBFGS(), options, args...; kwargs...)
end

function _optimize(
model::Model,
f::OptimLogDensity,
init_vals::AbstractArray = DynamicPPL.getparams(f),
optimizer::Optim.AbstractOptimizer = Optim.LBFGS(),
options::Optim.Options = Optim.Options(),
args...;
model::Model,
f::OptimLogDensity,
init_vals::AbstractArray=DynamicPPL.getparams(f),
optimizer::Optim.AbstractOptimizer=Optim.LBFGS(),
options::Optim.Options=Optim.Options(),
args...;
kwargs...
)
# Convert the initial values, since it is assumed that users provide them
Expand All @@ -243,7 +252,7 @@ function _optimize(
@warn "Optimization did not converge! You may need to correct your model or adjust the Optim parameters."
end

# Get the VarInfo at the MLE/MAP point, and run the model to ensure
# Get the VarInfo at the MLE/MAP point, and run the model to ensure
# correct dimensionality.
@set! f.varinfo = DynamicPPL.unflatten(f.varinfo, M.minimizer)
@set! f.varinfo = invlink!!(f.varinfo, model)
Expand Down