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Testing doctest #5

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d3fab65
Adding doctests after using seed. Work in progress.
ayushpatnaikgit Apr 27, 2022
9842d78
We gratefully acknowledge the JuliaLab at MIT for financial support f…
ayushpatnaikgit Apr 29, 2022
8453366
Adding a function to set random number generator
ayushpatnaikgit May 16, 2022
7eb242c
Added doctests in examples.md.
codetalker7 May 19, 2022
acc9ee0
Only keeping tests for two models.
codetalker7 May 19, 2022
49ea30e
Fixing architecture in documentation CI.
codetalker7 May 19, 2022
5501184
Adding all doctests for the first set of models.
codetalker7 May 19, 2022
0866c86
Revert back to two examples to check GH actions (along with a closing…
ShouvikGhosh2048 May 24, 2022
d56013d
Adding newline before ending ticks.
codetalker7 May 24, 2022
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3 changes: 2 additions & 1 deletion .github/workflows/documentation.yml
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Original file line number Diff line number Diff line change
@@ -15,7 +15,8 @@ jobs:
- uses: actions/checkout@v2
- uses: julia-actions/setup-julia@latest
with:
version: '1.7'
version: '1.7.2'
arch: x64
- name: Install documentation dependencies
run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
- name: Build and deploy
4 changes: 4 additions & 0 deletions README.md
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Original file line number Diff line number Diff line change
@@ -109,3 +109,7 @@ Language | Package/Function | Mean time taken
-----------|--------------------|------------------

where we emphasise that the performance of fitmodel() here is a tiny overhead on top of the implementation of the linear regression in GLM.jl.

# Support

We gratefully acknowledge the JuliaLab at MIT for financial support for this project.
3 changes: 2 additions & 1 deletion docs/Project.toml
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Original file line number Diff line number Diff line change
@@ -4,5 +4,6 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
NLSolversBase = "d41bc354-129a-5804-8e4c-c37616107c6c"
RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b"
StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
1 change: 1 addition & 0 deletions docs/make.jl
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Original file line number Diff line number Diff line change
@@ -17,6 +17,7 @@ makedocs(;
"Home" => "index.md",
"Examples" => "examples.md"
],
strict = :doctest
)

deploydocs(;
482 changes: 159 additions & 323 deletions docs/src/examples.md
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Large diffs are not rendered by default.

4 changes: 2 additions & 2 deletions src/CRRao.jl
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Original file line number Diff line number Diff line change
@@ -41,9 +41,9 @@ struct Cauchit end

export LinearRegression, LogisticRegression, PoissonRegression, NegBinomRegression
export Prior_Ridge, Prior_Laplace, Prior_Cauchy, Prior_TDist, Prior_Uniform
export Logit, Probit, Cloglog, Cauchit, fitmodel, @fitmodel, CRRao_seed
export Logit, Probit, Cloglog, Cauchit, fitmodel, @fitmodel

include("set_seed.jl")
include("random_number_generator.jl")
include("general_stats.jl")
include("LinearRegression.jl")
include("LogisticRegression.jl")
24 changes: 12 additions & 12 deletions src/LinearRegression.jl
View file
Original file line number Diff line number Diff line change
@@ -19,7 +19,7 @@ struct analysis_lm_Gauss_NIP_Optim
end

function linear_reg(formula::FormulaTerm,data::DataFrame)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

modelClass = "LinearReg";
LikelihoodMod="Gauss";
PriorMod="NIP";
@@ -61,7 +61,7 @@ function linear_reg(formula::FormulaTerm,data::DataFrame)
end

function linear_reg_predicts(obj,newdata::DataFrame)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = obj.formula;
fm_frame=ModelFrame(formula,newdata);
X=modelmatrix(fm_frame);
@@ -71,7 +71,7 @@ function linear_reg_predicts(obj,newdata::DataFrame)
end

function linear_reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Ridge,h::Float64,sim_size::Int64)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);

@@ -91,15 +91,15 @@ function linear_reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Ridge,h
y ~ MvNormal(α .+ X * β, σ);
end;
LinReg_model=LinReg(X,y);
chain = sample(LinReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, LinReg_model, NUTS(), sim_size);
summaries, quantiles = describe(chain);

ans = MCMC_chain(chain,summaries,quantiles)
ans
end

function linear_reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Laplace,h::Float64,sim_size::Int64)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);

@@ -118,7 +118,7 @@ function linear_reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Laplace
y ~ MvNormal(α .+ X * β, σ);
end;
LinReg_model=LinReg(X,y);
chain = sample(LinReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, LinReg_model, NUTS(), sim_size);
summaries, quantiles = describe(chain);

ans = MCMC_chain(chain,summaries,quantiles)
@@ -127,7 +127,7 @@ function linear_reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Laplace
end

function linear_reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Cauchy,sim_size::Int64)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);

@@ -143,7 +143,7 @@ function linear_reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Cauchy,
y ~ MvNormal(α .+ X * β, σ);
end;
LinReg_model=LinReg(X,y);
chain = sample(LinReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, LinReg_model, NUTS(), sim_size);
summaries, quantiles = describe(chain);

ans = MCMC_chain(chain,summaries,quantiles)
@@ -152,7 +152,7 @@ function linear_reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Cauchy,
end

function linear_reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_TDist,h::Float64,sim_size::Int64)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);

@@ -171,7 +171,7 @@ function linear_reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_TDist,h
y ~ MvNormal(α .+ X * β, σ);
end;
LinReg_model=LinReg(X,y);
chain = sample(LinReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, LinReg_model, NUTS(), sim_size);
summaries, quantiles = describe(chain);

ans = MCMC_chain(chain,summaries,quantiles)
@@ -180,7 +180,7 @@ function linear_reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_TDist,h
end

function linear_reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Uniform,h::Float64,sim_size::Int64)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);

@@ -197,7 +197,7 @@ function linear_reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Uniform
y ~ MvNormal(α .+ X * β, σ);
end;
LinReg_model=LinReg(X,y);
chain = sample(LinReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, LinReg_model, NUTS(), sim_size);
summaries, quantiles = describe(chain);

ans = MCMC_chain(chain,summaries,quantiles)
24 changes: 12 additions & 12 deletions src/LogisticRegression.jl
View file
Original file line number Diff line number Diff line change
@@ -20,7 +20,7 @@ struct analysis_logistic_Binom_NIP_Optim
end

function logistic_reg(formula::FormulaTerm,data,Link::String="LogitLink")
CRRao.seed != nothing && Random.seed!(CRRao.seed)

modelClass = "LogisticReg";
LikelihoodMod="Binomial";
PriorMod="NIP";
@@ -65,7 +65,7 @@ function logistic_reg(formula::FormulaTerm,data,Link::String="LogitLink")
end

function logistic_reg_predicts(obj,newdata::DataFrame)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = obj.formula;
fm_frame=ModelFrame(formula,newdata);
X=modelmatrix(fm_frame);
@@ -94,7 +94,7 @@ end
## logistic regression with Ridge Prior

function logistic_reg_internal(formula::FormulaTerm, data::DataFrame, link_function, PriorMod::Prior_Ridge, h::Float64=0.5,sim_size::Int64=10000)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);
fm_frame=ModelFrame(formula,data);
@@ -123,7 +123,7 @@ function logistic_reg_internal(formula::FormulaTerm, data::DataFrame, link_funct

LogisticReg_model=LogisticReg(X,y);

chain = sample(LogisticReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, LogisticReg_model, NUTS(), sim_size);

summaries, quantiles = describe(chain);

@@ -152,7 +152,7 @@ end
## logistic regression with Laplace Prior

function logistic_reg_internal(formula::FormulaTerm,data::DataFrame, link_function,PriorMod::Prior_Laplace,h::Float64=0.5,sim_size::Int64=10000)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);
fm_frame=ModelFrame(formula,data);
@@ -181,7 +181,7 @@ function logistic_reg_internal(formula::FormulaTerm,data::DataFrame, link_functi

LogisticReg_model=LogisticReg(X,y);

chain = sample(LogisticReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, LogisticReg_model, NUTS(), sim_size);

summaries, quantiles = describe(chain);

@@ -210,7 +210,7 @@ end
## logistic regression with Cauchy Prior

function logistic_reg_internal(formula::FormulaTerm,data,link_function,PriorMod::Prior_Cauchy,h::Float64,sim_size::Int64)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);
fm_frame=ModelFrame(formula,data);
@@ -239,7 +239,7 @@ function logistic_reg_internal(formula::FormulaTerm,data,link_function,PriorMod:

LogisticReg_model=LogisticReg(X,y);

chain = sample(LogisticReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, LogisticReg_model, NUTS(), sim_size);

summaries, quantiles = describe(chain);

@@ -268,7 +268,7 @@ end
## logistic regression with TDist Prior

function logistic_reg_internal(formula::FormulaTerm,data,link_function,PriorMod::Prior_TDist,h::Float64,sim_size::Int64)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);
fm_frame=ModelFrame(formula,data);
@@ -298,7 +298,7 @@ function logistic_reg_internal(formula::FormulaTerm,data,link_function,PriorMod:

LogisticReg_model=LogisticReg(X,y);

chain = sample(LogisticReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, LogisticReg_model, NUTS(), sim_size);

summaries, quantiles = describe(chain);

@@ -328,7 +328,7 @@ end
## logistic regression with Uniform Prior

function logistic_reg_internal(formula::FormulaTerm,data,link_function,PriorMod::Prior_Uniform,h::Float64=0.5,sim_size::Int64=10000)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);
fm_frame=ModelFrame(formula,data);
@@ -357,7 +357,7 @@ function logistic_reg_internal(formula::FormulaTerm,data,link_function,PriorMod:

LogisticReg_model=LogisticReg(X,y);

chain = sample(LogisticReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, LogisticReg_model, NUTS(), sim_size);

summaries, quantiles = describe(chain);

24 changes: 12 additions & 12 deletions src/NegBinomialRegression.jl
View file
Original file line number Diff line number Diff line change
@@ -20,7 +20,7 @@ end


function NegBinom_Reg(formula::FormulaTerm,data,Link::String="LogLink")
CRRao.seed != nothing && Random.seed!(CRRao.seed)

modelClass = "CountReg";
LikelihoodMod="NegativeBinomial";
PriorMod="NIP";
@@ -53,7 +53,7 @@ end


function NegBinom_Reg_predicts(obj,newdata::DataFrame)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = obj.formula;
fm_frame=ModelFrame(formula,newdata);
X=modelmatrix(fm_frame);
@@ -73,7 +73,7 @@ end

## Negative Binomial Regression with Ridge Prior
function NegBinom_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Ridge,h::Float64,sim_size::Int64)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);
@model NegBinomReg(X, y) = begin
@@ -94,7 +94,7 @@ function NegBinom_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Ridge
end
end
NegBinomReg_model=NegBinomReg(X,y);
chain = sample(NegBinomReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, NegBinomReg_model, NUTS(), sim_size);
summaries, quantiles = describe(chain);
ans = MCMC_chain(chain,summaries,quantiles)
ans
@@ -104,7 +104,7 @@ end

## Negative Binomial Regression with Laplace Prior
function NegBinom_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Laplace,h::Float64,sim_size::Int64)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);
@model NegBinomReg(X, y) = begin
@@ -125,15 +125,15 @@ function NegBinom_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Lapla
end
end
NegBinomReg_model=NegBinomReg(X,y);
chain = sample(NegBinomReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, NegBinomReg_model, NUTS(), sim_size);
summaries, quantiles = describe(chain);
ans = MCMC_chain(chain,summaries,quantiles)
ans
end

## Negative Binomial Regression with Cauchy Prior
function NegBinom_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Cauchy,h::Float64,sim_size::Int64)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);
@model NegBinomReg(X, y) = begin
@@ -154,7 +154,7 @@ function NegBinom_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Cauch
end
end
NegBinomReg_model=NegBinomReg(X,y);
chain = sample(NegBinomReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, NegBinomReg_model, NUTS(), sim_size);
summaries, quantiles = describe(chain);
ans = MCMC_chain(chain,summaries,quantiles)
ans
@@ -163,7 +163,7 @@ end

## Negative Binomial Regression with TDist Prior
function NegBinom_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_TDist,h::Float64,sim_size::Int64)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);
@model NegBinomReg(X, y) = begin
@@ -185,7 +185,7 @@ function NegBinom_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_TDist
end
end
NegBinomReg_model=NegBinomReg(X,y);
chain = sample(NegBinomReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, NegBinomReg_model, NUTS(), sim_size);
summaries, quantiles = describe(chain);
ans = MCMC_chain(chain,summaries,quantiles)
ans
@@ -195,7 +195,7 @@ end

## Negative Binomial Regression with Uniform Prior
function NegBinom_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Uniform,h::Float64,sim_size::Int64)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);
@model NegBinomReg(X, y) = begin
@@ -216,7 +216,7 @@ function NegBinom_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Unifo
end
end
NegBinomReg_model=NegBinomReg(X,y);
chain = sample(NegBinomReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, NegBinomReg_model, NUTS(), sim_size);
summaries, quantiles = describe(chain);
ans = MCMC_chain(chain,summaries,quantiles)
ans
24 changes: 12 additions & 12 deletions src/PoissonRegression.jl
View file
Original file line number Diff line number Diff line change
@@ -20,7 +20,7 @@ struct analysis_Poisson_Reg
end

function Poisson_Reg(formula::FormulaTerm,data::DataFrame)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);
fm_frame=ModelFrame(formula,data);
@@ -44,7 +44,7 @@ function Poisson_Reg(formula::FormulaTerm,data::DataFrame)
end

function Poisson_Reg_predicts(obj,newdata::DataFrame)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = obj.formula;
fm_frame=ModelFrame(formula,newdata);
X=modelmatrix(fm_frame);
@@ -57,7 +57,7 @@ end

## Poisson Regression with Ridge Prior
function Poisson_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Ridge,h::Float64,sim_size::Int64)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);
@model PoissonReg(X, y) = begin
@@ -78,15 +78,15 @@ function Poisson_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Ridge,
end
end
PoissonReg_model=PoissonReg(X,y);
chain = sample(PoissonReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, PoissonReg_model, NUTS(), sim_size);
summaries, quantiles = describe(chain);
ans = MCMC_chain(chain,summaries,quantiles)
ans
end

## Poisson Regression with Laplace Prior
function Poisson_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Laplace,h::Float64,sim_size::Int64)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);
@model PoissonReg(X, y) = begin
@@ -107,15 +107,15 @@ function Poisson_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Laplac
end
end
PoissonReg_model=PoissonReg(X,y);
chain = sample(PoissonReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, PoissonReg_model, NUTS(), sim_size);
summaries, quantiles = describe(chain);
ans = MCMC_chain(chain,summaries,quantiles)
ans
end

## Poisson Regression with Cauchy Prior
function Poisson_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Cauchy,h::Float64,sim_size::Int64)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);
@model PoissonReg(X, y) = begin
@@ -136,15 +136,15 @@ function Poisson_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Cauchy
end
end
PoissonReg_model=PoissonReg(X,y);
chain = sample(PoissonReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, PoissonReg_model, NUTS(), sim_size);
summaries, quantiles = describe(chain);
ans = MCMC_chain(chain,summaries,quantiles)
ans
end

## Poisson Regression with T-Distributed Prior
function Poisson_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_TDist,h::Float64,sim_size::Int64)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);
@model PoissonReg(X, y) = begin
@@ -166,15 +166,15 @@ function Poisson_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_TDist,
end
end
PoissonReg_model=PoissonReg(X,y);
chain = sample(PoissonReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, PoissonReg_model, NUTS(), sim_size);
summaries, quantiles = describe(chain);
ans = MCMC_chain(chain,summaries,quantiles)
ans
end

## Poisson Regression with Uniform Prior
function Poisson_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Uniform,h::Float64,sim_size::Int64)
CRRao.seed != nothing && Random.seed!(CRRao.seed)

formula = apply_schema(formula, schema(formula, data));
y, X = modelcols(formula, data);
@model PoissonReg(X, y) = begin
@@ -194,7 +194,7 @@ function Poisson_Reg(formula::FormulaTerm,data::DataFrame,PriorMod::Prior_Unifor
end
end
PoissonReg_model=PoissonReg(X,y);
chain = sample(PoissonReg_model, NUTS(), sim_size);
chain = sample(CRRao_rng, PoissonReg_model, NUTS(), sim_size);
summaries, quantiles = describe(chain);
ans = MCMC_chain(chain,summaries,quantiles)
ans
11 changes: 11 additions & 0 deletions src/random_number_generator.jl
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,11 @@
global CRRao_rng = MersenneTwister()

"""
```julia
using StableRNGs
CRRao.set_rng(StableRNG(1234))
```
"""
function set_rng(x)
global CRRao_rng = x
end
5 changes: 0 additions & 5 deletions src/set_seed.jl
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This file was deleted.