increase unit test coverage

Author: itsdfish

src/multi_choice_models/AttentionalDiffusion.jl

@@ -205,7 +205,7 @@ function rand(rng::AbstractRNG, dist::AbstractaDDM; fixate, Δt = 0.001)
     while abs(v) < α
         t += Δt
         location = fixate()
-        v += increment(rng, dist, location)
+        v += increment!(rng, dist, location)
     end
     choice = (v < α) + 1
     return (; choice, rt = t)
@@ -275,10 +275,10 @@ function survivor(d::AbstractaDDM, choice::Int, ub::Real, args...; fixate, kwarg
     return survivor(Random.default_rng(), d, choice, fixate, ub, args...; fixate, kwargs...)
 end
 
-increment(dist::AbstractaDDM, location) = increment(Random.default_rng(), dist, location)
+increment!(dist::AbstractaDDM, location) = increment!(Random.default_rng(), dist, location)
 
 """
-    increment(rng::AbstractRNG, dist::aDDM, location)
+    increment!(rng::AbstractRNG, dist::aDDM, location)
 
 Returns the change evidence for a single iteration. 
 
@@ -288,7 +288,7 @@ Returns the change evidence for a single iteration.
 - `dist::aDDM`: a model object for the attentional drift diffusion model
 - `location`: an index for fixation location 
 """
-function increment(rng::AbstractRNG, dist::aDDM, location)
+function increment!(rng::AbstractRNG, dist::aDDM, location)
     (; σ, ν, θ, Δ) = dist
     # option 1
     if location == 1
@@ -349,7 +349,7 @@ function simulate(
     while abs(x) < α
         t += Δt
         location = _fixate()
-        x += increment(model, location)
+        x += increment!(model, location)
         push!(evidence, x)
         push!(time_steps, t)
     end

src/multi_choice_models/maaDDM.jl

@@ -153,7 +153,7 @@ get_pdf_type(d::maaDDM) = Approximate
 n_options(d::maaDDM) = size(d.ν, 1)
 
 """
-    increment(rng, dist::maaDDM, location)
+    increment!(rng, dist::maaDDM, location)
 
 Returns the change evidence for a single iteration. 
 
@@ -162,7 +162,7 @@ Returns the change evidence for a single iteration.
 - `dist::maaDDM`: a model object for the multiattribute attentional drift diffusion model
 - `location`: an index for fixation location 
 """
-function increment(rng, dist::maaDDM, location)
+function increment!(rng, dist::maaDDM, location)
     (; ν, θ, ϕ, ω, Δ, σ) = dist
     # option 1, attribute 1
     if location == 1
@@ -177,9 +177,10 @@ function increment(rng, dist::maaDDM, location)
         return Δ * (ω * (θ * ν[1, 1] - ν[2, 1]) + (1 - ω) * ϕ * (θ * ν[1, 2] - ν[2, 2])) +
                noise(rng, σ)
         # option 2, attribute 2
-    else
+    elseif location == 4
         return Δ * (ϕ * ω * (θ * ν[1, 1] - ν[2, 1]) + (1 - ω) * (θ * ν[1, 2] - ν[2, 2])) +
                noise(rng, σ)
     end
+    @argcheck location ∈ [1,2,3,4]
     return -100.0
 end

src/single_choice_models/ShiftedLogNormal.jl

@@ -59,14 +59,20 @@ function pdf(dist::AbstractShiftedLogNormal, rt::Real)
     return pdf(LogNormal(ν, σ), rt - τ)
 end
 
+function cdf(dist::AbstractShiftedLogNormal, rt::Real)
+    (; τ, ν, σ) = dist
+    @argcheck τ ≤ rt
+    return cdf(LogNormal(ν, σ), rt - τ)
+end
+
 function rand(rng::AbstractRNG, dist::AbstractShiftedLogNormal, n_trials::Int)
     (; τ, ν, σ) = dist
     return rand(rng, LogNormal(ν, σ), n_trials) .+ τ
 end
 
 function rand(rng::AbstractRNG, dist::AbstractShiftedLogNormal)
     (; τ, ν, σ) = dist
-    return rand(rng, LogNormal(ν, σ)) .+ τ
+    return rand(rng, LogNormal(ν, σ)) + τ
 end
 
 function params(d::AbstractShiftedLogNormal)

src/single_choice_models/ex_gaussian.jl

@@ -8,7 +8,7 @@ to model reaction time distributions. Note that this is not technically a sequen
 
 - `μ::T`: mean of Gaussian component. μ ∈ ℝ⁺.
 - `σ::T`: standard deviation of Gaussian component. σ ∈ ℝ⁺
-- `τ::T`: mean of exponential component. τ ∈ [0, min_rt]
+- `τ::T`: mean of exponential component. τ ∈ (0, min_rt]
 
 # Constructors
 
@@ -41,7 +41,7 @@ struct ExGaussian{T <: Real} <: SSM1D
     function ExGaussian(μ::T, σ::T, τ::T) where {T <: Real}
         @argcheck μ ≥ 0
         @argcheck σ ≥ 0
-        @argcheck τ ≥ 0
+        @argcheck τ > 0
         return new{T}(μ, σ, τ)
     end
 end
@@ -64,9 +64,6 @@ end
 
 function logpdf(d::ExGaussian, rt::Float64)
     (; μ, σ, τ) = d
-    if τ == 0
-        return -Inf
-    end
     return log(1 / τ) +
            (μ - rt) / τ +
            (σ^2 / 2τ^2) +

src/single_choice_models/wald.jl

@@ -114,8 +114,8 @@ function simulate(rng::AbstractRNG, model::Wald; Δt = 0.001)
     t = 0.0
     evidence = [0.0]
     time_steps = [t]
-    ν′ = rand(truncated(Normal(ν, η), 0, Inf))
-    while x .< α
+    ν′ = rand(rng, truncated(Normal(ν, η), 0, Inf))
+    while x < α
         t += Δt
         x = increment!(rng, model, x, ν′; Δt)
         push!(evidence, x)