Merge branch 'main' of https://git.youainti.com/youainti/ClinicalTrialsEstimation

Julia-turing models/Model.jl

@@ -1,62 +0,0 @@
-
-using Turing, StatsFuns
-using Distributions, StatsPlots
-
-using FillArrays
-
-begin #creating synthetic data
-	βs = [1 2 3]'
-	x = Matrix([1:10 1:2:20 1:3:30])
-	X = (x .- mean(x,dims=1)) ./ std(x,dims=1)
-	t = [x for x=1:0.5:5.5]
-	s = rand([1,2],10)
-	σ= [2 3]
-	rand_draw1 = randn(10)
-
-	y = x*βs .+ σ[s]
-end
-
-@model function JointDurationStateModel(
-	DeviationFromExpectedDuration,
-	ConclusionStatus,
-	SnapshotState,
-	CurrentDuration,
-)
-	# get dimensions
-	n,k = size(SnapshotState)
-	
-	#hyperpriors priors
-
-	#Heirarchal parameters
-	#β ~ MvNormal(Fill(0,k),2)
-	η ~ MvNormal(Fill(0,k),2)
-
-	#Direct Priors
-	#σ_DFED ~ filldist(Exponential(1),2) #TODO: check implication of this form
-	
-	#model
-	#μ = SnapshotState * β
-	p = StatsFuns.logistic.(SnapshotState * η)
-	
-	#estimate ConclusionStatus model
-	ConclusionStatus .~ Bernoulli(p)
-	#Estimate DFED model
-	#=
-	for i in eachindex(ConclusionStatus)
-		DeviationFromExpectedDuration ~ Normal(
-			μ[ConclusionStatus[i]],
-			σ_DFED[ConclusionStatus[i]]
-		)
-	end
-	=#
-end
-
-model = JointDurationStateModel(y,s,X,t)
-prior = JointDurationStateModel(fill(missing,size(y)),fill(missing,size(s)),X,t)
-
-chain = sample(model,NUTS(0.85),2000)
-
-
-
-plot(chain)
-