pvalue function for uncorrelated data now accept a W vector. default value is...

pvalue function for uncorrelated data now accept a W vector. default value is Vector{Float64}(), and if W is not provided, it is computed as the inverse of the error squared of the data.

src/Changelog.md

@@ -9,4 +9,5 @@ This file should not be merged into master, but only kept as a reference within
     - Updated documentation in juobs_tools.jl
     - Updated documentation in juobs_type.jl 
   - Dic 8 2024: added functions plat_av to the existing one. This change should not be code-breaking, since it only add different version of the exising one. Now it is possible to pass a Vector or a Matrix W containing the weight to use in the plateau average. If W is a Matrix, a correlated fit is perform. If W is not given, then the old function is called. 
-  - Dic 10 2024: mpcac function now follow the convention more clearly. documentation updated juobs_tools
\ No newline at end of file
+  - Dic 10 2024: mpcac function now follow the convention more clearly. documentation updated juobs_tools. 
+                 pvalue function for uncorrelated data now accept a W vector. default value is Vector{Float64}(), and if W is not provided, it is computed as the inverse of the error squared of the data. 
\ No newline at end of file

src/juobs_tools.jl

@@ -1838,12 +1838,16 @@ Q = pvalue(chisq, chi2, value.(up), y, wpm; W = 1.0 ./ err.(y) .^ 2, nmc=10000)
 function pvalue(chisq::Function,
     chi2::Float64,
     xp::Vector{Float64}, 
-    data::Vector{uwreal};
+    data::Vector{uwreal},
+    W::Vector{Float64}=Vector{Float64}();
     wpm::Union{Dict{Int64,Vector{Float64}},Dict{String,Vector{Float64}}, Nothing} =  Dict{Int64,Vector{Float64}}(),
     nmc::Int64 = 5000)
 
     n = length(xp)   # Number of fit parameters
     m = length(data) # Number of data
+    if (m-n<0)
+      error("more parameters than data")
+    end
 
     xav = zeros(Float64, n+m)
     for i in 1:n
@@ -1862,22 +1866,20 @@ function pvalue(chisq::Function,
     hess = Array{Float64}(undef, n+m, n+m)
     ForwardDiff.hessian!(hess, ccsq, xav, cfg)
     
-    if (m-n > 0)
+    if length(W) ==0 
       W = zeros(Float64, m)
       for i in 1:m
         if (data[i].err == 0.0)
-                    #isnothing(wpm) ? wuerr(data[i]) : uwerr(data[i], wpm)
           uwerr(data[i], wpm)
           if (data[i].err == 0.0)
             error("Zero error in fit data")
           end
         end
-                global W[i] = 1.0 / data[i].err^2
+        W[i] = 1.0 / data[i].err^2
+      end
     end
 
-        m = length(data)
     n = size(hess, 1) - m
-
     hm = view(hess, 1:n, n+1:n+m)
     sm = Array{Float64, 2}(undef, n, m)
     for i in 1:n, j in 1:m
@@ -1909,7 +1911,6 @@ function pvalue(chisq::Function,
     x = x / eig[1]
     #dQ = juobs.mean((x .> 0) .* exp.(-x * 0.5) * 0.5 ./ sqrt.(abs.(x)))
     #dQ = err(cse)/value(cse) * dQ
-    end
     return Q
 end