Commit c69f46c5 authored by matbuoro's avatar matbuoro
Browse files

Mise à jour estimation 2016

parent 75126bb9
list(Nyears=3.30000E+01, Cm_R= structure(.Data= c(0.00000E+00, 1.00000E+01, 0.00000E+00, 2.00000E+00, 2.00000E+00, 1.30000E+01, 0.00000E+00, 6.00000E+00, 0.00000E+00, 5.00000E+00, 0.00000E+00, 0.00000E+00, 2.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 2.00000E+00, 9.00000E+00, 0.00000E+00, 1.00000E+00, 2.50000E+01, 1.10000E+01, 5.00000E+00, 3.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 1.00000E+00, 1.10000E+01, 1.00000E+01, 0.00000E+00, 1.00000E+00, 3.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 3.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 2.10000E+01, 1.50000E+01, 0.00000E+00, 0.00000E+00, 1.30000E+01, 6.00000E+00, 0.00000E+00, 0.00000E+00, 7.00000E+00, 4.00000E+00, 0.00000E+00, 0.00000E+00, 5.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 1.60000E+01, 6.00000E+00, 1.00000E+00, 0.00000E+00, 1.00000E+00, 3.00000E+00, 0.00000E+00, 0.00000E+00, 2.30000E+01, 5.00000E+00, 1.00000E+00, 1.00000E+00, 1.00000E+01, 3.00000E+00, 1.00000E+00, 1.00000E+00, 5.20000E+01, 2.60000E+01, 1.00000E+00, 9.00000E+00, 3.10000E+01, 3.00000E+01, 1.00000E+00, 7.00000E+00, 2.20000E+01, 1.10000E+01, 1.00000E+00, 2.00000E+00, 1.80000E+01, 1.70000E+01, 0.00000E+00, 7.00000E+00, 5.50000E+01, 2.00000E+01, 0.00000E+00, 3.00000E+00, 3.00000E+00, 6.00000E+00, 0.00000E+00, 3.00000E+00, 2.00000E+00, 1.00000E+00, 0.00000E+00, 2.00000E+00, 1.20000E+01, 8.00000E+00, 0.00000E+00, 0.00000E+00, 1.90000E+01, 3.00000E+00, 1.00000E+00, 6.00000E+00, 6.00000E+00, 1.00000E+00, 0.00000E+00, 1.00000E+00, 1.40000E+01, 1.00000E+00, 1.00000E+00, 0.00000E+00, 3.00000E+00, 3.00000E+00, 0.00000E+00, 1.00000E+00, 4.00000E+00, 1.00000E+00, 0.00000E+00, 5.00000E+00, 2.30000E+01, 1.80000E+01, 0.00000E+00, 2.00000E+00), .Dim=c(33, 4)), Cum_R= structure(.Data= c(0.00000E+00, 9.00000E+00, 0.00000E+00, 1.00000E+00, 1.00000E+00, 2.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 2.00000E+00, 1.20000E+01, 7.00000E+00, 1.00000E+00, 2.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 1.20000E+01, 1.00000E+00, 1.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 1.00000E+00, 0.00000E+00, 2.00000E+00, 2.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 2.00000E+00, 1.00000E+00, 1.00000E+00, 0.00000E+00, 8.00000E+00, 4.00000E+00, 0.00000E+00, 0.00000E+00, 2.90000E+01, 3.10000E+01, 0.00000E+00, 0.00000E+00, 2.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.50000E+01, 1.20000E+01, 0.00000E+00, 5.00000E+00, 6.00000E+00, 6.00000E+00, 0.00000E+00, 1.00000E+00, 1.40000E+01, 1.60000E+01, 1.00000E+00, 2.00000E+00, 2.00000E+00, 3.00000E+00, 0.00000E+00, 0.00000E+00, 1.40000E+01, 2.20000E+01, 1.00000E+00, 2.00000E+00, 8.00000E+00, 1.00000E+00, 0.00000E+00, 2.00000E+00, 2.00000E+01, 1.40000E+01, 2.00000E+00, 9.00000E+00, 1.50000E+01, 7.00000E+00, 0.00000E+00, 3.00000E+00, 1.10000E+01, 1.30000E+01, 0.00000E+00, 6.00000E+00, 3.00000E+00, 3.00000E+00, 0.00000E+00, 4.00000E+00, 7.00000E+00, 1.00000E+01, 0.00000E+00, 5.00000E+00, 1.70000E+01, 1.30000E+01, 1.00000E+00, 4.00000E+00, 1.04000E+02, 5.30000E+01, 2.00000E+00, 2.00000E+00, 1.40000E+01, 2.00000E+01, 1.00000E+00, 1.20000E+01, 3.20000E+01, 1.80000E+01, 1.00000E+00, 9.00000E+00, 4.90000E+01, 1.70000E+01, 1.00000E+00, 8.00000E+00, 4.50000E+01, 2.90000E+01, 2.00000E+00, 1.30000E+01, 2.00000E+01, 1.10000E+01, 2.00000E+00, 1.10000E+01, 5.50000E+01, 2.10000E+01, 0.00000E+00, 3.00000E+00), .Dim=c(33, 4)), Q=c(2.09300E+02, 1.64400E+02, 1.41762E+03, 1.03383E+03, 5.79370E+02, 2.36974E+02, 3.51897E+02, 6.15205E+02, 1.72976E+03, 1.38403E+03, 1.43613E+03, 3.11615E+02, 3.80718E+02, 3.14590E+02, 1.96835E+03, 1.46658E+03, 4.21397E+03, 7.07705E+02, 1.58821E+03, 3.80564E+02, 9.06179E+02, 8.41077E+02, 1.11855E+03, 1.15265E+03, 1.42599E+03, 1.19684E+03, 7.44431E+02, 1.02953E+03, 1.68858E+03, 1.07772E+03, 9.95728E+02, 8.56676E+02, 6.37538E+02), C_MC= structure(.Data= c(1.02000E+02, 4.00000E+01, 5.00000E+00, 2.10000E+01, 1.33000E+02, 6.60000E+01, 1.70000E+01, 4.60000E+01, 5.90000E+01, 1.20000E+01, 3.00000E+01, 2.50000E+01, 1.40000E+01, 2.00000E+00, 0.00000E+00, 0.00000E+00, 1.20000E+02, 3.10000E+01, 1.20000E+01, 6.30000E+01, 1.32000E+02, 6.40000E+01, 1.80000E+01, 2.10000E+01, 6.00000E+00, 3.00000E+00, 2.00000E+00, 4.00000E+00, 3.00000E+01, 1.30000E+01, 1.00000E+00, 1.00000E+00, 1.70000E+01, 1.10000E+01, 1.00000E+00, 2.00000E+00, 7.10000E+01, 2.20000E+01, 4.00000E+00, 3.00000E+00, 1.20000E+01, 1.80000E+01, 0.00000E+00, 3.00000E+00, 6.30000E+01, 4.50000E+01, 0.00000E+00, 3.00000E+00, 4.00000E+01, 2.70000E+01, 0.00000E+00, 3.00000E+00, 3.40000E+01, 2.10000E+01, 0.00000E+00, 1.00000E+00, 2.30000E+01, 1.00000E+01, 0.00000E+00, 1.00000E+00, 9.30000E+01, 4.30000E+01, 5.00000E+00, 1.30000E+01, 2.40000E+01, 2.70000E+01, 1.00000E+00, 2.00000E+00, 1.00000E+02, 5.00000E+01, 4.00000E+00, 5.00000E+00, 5.30000E+01, 4.00000E+01, 7.00000E+00, 9.00000E+00, 1.03000E+02, 6.00000E+01, 1.00000E+00, 2.40000E+01, 1.12000E+02, 1.00000E+02, 6.00000E+00, 2.80000E+01, 5.80000E+01, 2.30000E+01, 2.00000E+00, 5.00000E+00, 6.10000E+01, 4.10000E+01, 0.00000E+00, 1.90000E+01, 1.12000E+02, 5.60000E+01, 1.00000E+00, 9.00000E+00, 1.20000E+01, 1.60000E+01, 1.00000E+00, 3.00000E+00, 1.00000E+01, 3.00000E+00, 0.00000E+00, 7.00000E+00, 4.10000E+01, 2.60000E+01, 0.00000E+00, 3.00000E+00, 4.70000E+01, 3.50000E+01, 2.00000E+00, 2.00000E+01, 1.80000E+01, 4.00000E+00, 3.00000E+00, 6.00000E+00, 4.10000E+01, 1.40000E+01, 4.00000E+00, 1.30000E+01, 1.10000E+01, 4.00000E+00, 0.00000E+00, 4.00000E+00, 9.00000E+00, 7.00000E+00, 1.00000E+00, 9.00000E+00, 6.00000E+01, 3.40000E+01, 1.00000E+00, 1.40000E+01), .Dim=c(33, 4)), Cm_MC= structure(.Data= c(1.00000E+02, 3.60000E+01, 5.00000E+00, 1.70000E+01, 1.23000E+02, 5.10000E+01, 1.50000E+01, 3.80000E+01, 4.40000E+01, 8.00000E+00, 5.50000E+01, 1.50000E+01, 1.10000E+01, 1.00000E+00, 0.00000E+00, 0.00000E+00, 1.08000E+02, 1.30000E+01, 1.10000E+01, 5.30000E+01, 1.23000E+02, 5.20000E+01, 1.30000E+01, 1.10000E+01, 3.00000E+00, 3.00000E+00, 0.00000E+00, 1.00000E+00, 3.00000E+01, 1.30000E+01, 1.00000E+00, 1.00000E+00, 1.30000E+01, 8.00000E+00, 0.00000E+00, 0.00000E+00, 6.50000E+01, 1.20000E+01, 4.00000E+00, 0.00000E+00, 8.00000E+00, 1.00000E+01, 0.00000E+00, 1.00000E+00, 6.00000E+01, 4.10000E+01, 0.00000E+00, 2.00000E+00, 3.90000E+01, 2.60000E+01, 0.00000E+00, 3.00000E+00, 2.30000E+01, 9.00000E+00, 0.00000E+00, 1.00000E+00, 2.20000E+01, 8.00000E+00, 0.00000E+00, 0.00000E+00, 9.10000E+01, 4.20000E+01, 5.00000E+00, 1.10000E+01, 2.40000E+01, 2.60000E+01, 1.00000E+00, 2.00000E+00, 9.90000E+01, 5.00000E+01, 4.00000E+00, 5.00000E+00, 5.30000E+01, 3.90000E+01, 7.00000E+00, 8.00000E+00, 1.03000E+02, 5.80000E+01, 1.00000E+00, 2.40000E+01, 1.11000E+02, 1.00000E+02, 6.00000E+00, 2.80000E+01, 5.80000E+01, 2.20000E+01, 2.00000E+00, 5.00000E+00, 6.10000E+01, 4.10000E+01, 0.00000E+00, 1.90000E+01, 1.12000E+02, 5.60000E+01, 1.00000E+00, 9.00000E+00, 1.20000E+01, 1.50000E+01, 1.00000E+00, 3.00000E+00, 1.00000E+01, 3.00000E+00, 0.00000E+00, 7.00000E+00, 3.70000E+01, 2.40000E+01, 0.00000E+00, 2.00000E+00, 4.60000E+01, 3.50000E+01, 2.00000E+00, 2.00000E+01, 1.80000E+01, 4.00000E+00, 3.00000E+00, 6.00000E+00, 3.80000E+01, 1.30000E+01, 4.00000E+00, 1.30000E+01, 1.10000E+01, 4.00000E+00, 0.00000E+00, 4.00000E+00, 9.00000E+00, 7.00000E+00, 1.00000E+00, 9.00000E+00, 6.00000E+01, 3.40000E+01, 1.00000E+00, 1.40000E+01), .Dim=c(33, 4)))
list(Nyears=3.30000E+01, Cm_R= structure(.Data= c(0.00000E+00, 1.00000E+01, 0.00000E+00, 2.00000E+00, 2.00000E+00, 1.30000E+01, 0.00000E+00, 6.00000E+00, 0.00000E+00, 5.00000E+00, 0.00000E+00, 0.00000E+00, 2.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 2.00000E+00, 9.00000E+00, 0.00000E+00, 1.00000E+00, 2.50000E+01, 1.10000E+01, 5.00000E+00, 3.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 1.00000E+00, 1.10000E+01, 1.00000E+01, 0.00000E+00, 1.00000E+00, 3.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 3.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 2.10000E+01, 1.50000E+01, 0.00000E+00, 0.00000E+00, 1.30000E+01, 6.00000E+00, 0.00000E+00, 0.00000E+00, 7.00000E+00, 4.00000E+00, 0.00000E+00, 0.00000E+00, 5.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 1.60000E+01, 6.00000E+00, 1.00000E+00, 0.00000E+00, 1.00000E+00, 3.00000E+00, 0.00000E+00, 0.00000E+00, 2.30000E+01, 5.00000E+00, 1.00000E+00, 1.00000E+00, 1.00000E+01, 3.00000E+00, 1.00000E+00, 1.00000E+00, 5.20000E+01, 2.60000E+01, 1.00000E+00, 9.00000E+00, 3.10000E+01, 3.00000E+01, 1.00000E+00, 7.00000E+00, 2.20000E+01, 1.10000E+01, 1.00000E+00, 2.00000E+00, 1.80000E+01, 1.70000E+01, 0.00000E+00, 7.00000E+00, 5.50000E+01, 2.00000E+01, 0.00000E+00, 3.00000E+00, 3.00000E+00, 6.00000E+00, 0.00000E+00, 3.00000E+00, 2.00000E+00, 1.00000E+00, 0.00000E+00, 2.00000E+00, 1.20000E+01, 8.00000E+00, 0.00000E+00, 0.00000E+00, 1.90000E+01, 3.00000E+00, 1.00000E+00, 6.00000E+00, 6.00000E+00, 1.00000E+00, 0.00000E+00, 1.00000E+00, 1.40000E+01, 1.00000E+00, 1.00000E+00, 0.00000E+00, 3.00000E+00, 3.00000E+00, 0.00000E+00, 1.00000E+00, 4.00000E+00, 1.00000E+00, 0.00000E+00, 5.00000E+00, 2.30000E+01, 1.80000E+01, 0.00000E+00, 2.00000E+00), .Dim=c(33, 4)), Cum_R= structure(.Data= c(0.00000E+00, 9.00000E+00, 0.00000E+00, 1.00000E+00, 1.00000E+00, 2.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 2.00000E+00, 1.20000E+01, 7.00000E+00, 1.00000E+00, 2.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 1.20000E+01, 1.00000E+00, 1.00000E+00, 0.00000E+00, 1.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 1.00000E+00, 0.00000E+00, 2.00000E+00, 2.00000E+00, 0.00000E+00, 0.00000E+00, 1.00000E+00, 2.00000E+00, 1.00000E+00, 1.00000E+00, 0.00000E+00, 8.00000E+00, 4.00000E+00, 0.00000E+00, 0.00000E+00, 2.90000E+01, 3.10000E+01, 0.00000E+00, 0.00000E+00, 2.00000E+00, 0.00000E+00, 0.00000E+00, 0.00000E+00, 1.50000E+01, 1.20000E+01, 0.00000E+00, 5.00000E+00, 6.00000E+00, 6.00000E+00, 0.00000E+00, 1.00000E+00, 1.40000E+01, 1.60000E+01, 1.00000E+00, 2.00000E+00, 2.00000E+00, 3.00000E+00, 0.00000E+00, 0.00000E+00, 1.40000E+01, 2.20000E+01, 1.00000E+00, 2.00000E+00, 8.00000E+00, 1.00000E+00, 0.00000E+00, 2.00000E+00, 2.00000E+01, 1.40000E+01, 2.00000E+00, 9.00000E+00, 1.50000E+01, 7.00000E+00, 0.00000E+00, 3.00000E+00, 1.10000E+01, 1.30000E+01, 0.00000E+00, 6.00000E+00, 3.00000E+00, 3.00000E+00, 0.00000E+00, 4.00000E+00, 7.00000E+00, 1.00000E+01, 0.00000E+00, 5.00000E+00, 1.70000E+01, 1.30000E+01, 1.00000E+00, 4.00000E+00, 1.04000E+02, 5.30000E+01, 2.00000E+00, 2.00000E+00, 1.40000E+01, 2.00000E+01, 1.00000E+00, 1.20000E+01, 3.20000E+01, 1.80000E+01, 1.00000E+00, 9.00000E+00, 4.90000E+01, 1.70000E+01, 1.00000E+00, 8.00000E+00, 4.50000E+01, 2.90000E+01, 2.00000E+00, 1.30000E+01, 2.00000E+01, 1.10000E+01, 2.00000E+00, 1.10000E+01, 5.50000E+01, 2.10000E+01, 0.00000E+00, 3.00000E+00), .Dim=c(33, 4)), Q=c(2.09300E+02, 1.64400E+02, 1.41762E+03, 1.03383E+03, 5.79370E+02, 2.36974E+02, 3.51897E+02, 6.15205E+02, 1.72976E+03, 1.38403E+03, 1.43613E+03, 3.11615E+02, 3.80718E+02, 3.14590E+02, 1.96835E+03, 1.46658E+03, 4.21397E+03, 7.07705E+02, 1.58821E+03, 3.80564E+02, 9.06179E+02, 8.41077E+02, 1.11855E+03, 1.15265E+03, 1.42599E+03, 1.19684E+03, 7.44431E+02, 1.02953E+03, 1.68858E+03, 1.07772E+03, 9.95728E+02, 8.56676E+02, 6.37538E+02), C_MC= structure(.Data= c(1.02000E+02, 4.00000E+01, 5.00000E+00, 2.10000E+01, 1.32000E+02, 6.60000E+01, 1.70000E+01, 4.60000E+01, 5.90000E+01, 1.20000E+01, 3.00000E+01, 2.50000E+01, 1.40000E+01, 2.00000E+00, 0.00000E+00, 0.00000E+00, 1.20000E+02, 3.10000E+01, 1.20000E+01, 6.30000E+01, 1.32000E+02, 6.40000E+01, 1.80000E+01, 2.10000E+01, 6.00000E+00, 3.00000E+00, 2.00000E+00, 4.00000E+00, 3.00000E+01, 1.30000E+01, 1.00000E+00, 1.00000E+00, 1.70000E+01, 1.10000E+01, 1.00000E+00, 2.00000E+00, 7.10000E+01, 2.20000E+01, 4.00000E+00, 3.00000E+00, 1.20000E+01, 1.80000E+01, 0.00000E+00, 3.00000E+00, 6.30000E+01, 4.50000E+01, 0.00000E+00, 3.00000E+00, 4.00000E+01, 2.70000E+01, 0.00000E+00, 3.00000E+00, 3.40000E+01, 2.10000E+01, 0.00000E+00, 1.00000E+00, 2.30000E+01, 1.00000E+01, 0.00000E+00, 1.00000E+00, 9.30000E+01, 4.30000E+01, 5.00000E+00, 1.30000E+01, 2.40000E+01, 2.70000E+01, 1.00000E+00, 2.00000E+00, 1.00000E+02, 5.00000E+01, 4.00000E+00, 5.00000E+00, 5.30000E+01, 4.00000E+01, 7.00000E+00, 9.00000E+00, 1.03000E+02, 6.00000E+01, 1.00000E+00, 2.40000E+01, 1.12000E+02, 1.00000E+02, 6.00000E+00, 2.80000E+01, 5.80000E+01, 2.30000E+01, 2.00000E+00, 5.00000E+00, 6.10000E+01, 4.10000E+01, 0.00000E+00, 1.90000E+01, 1.12000E+02, 5.60000E+01, 1.00000E+00, 9.00000E+00, 1.20000E+01, 1.60000E+01, 1.00000E+00, 3.00000E+00, 1.00000E+01, 3.00000E+00, 0.00000E+00, 7.00000E+00, 4.10000E+01, 2.60000E+01, 0.00000E+00, 3.00000E+00, 4.70000E+01, 3.50000E+01, 2.00000E+00, 2.00000E+01, 1.80000E+01, 4.00000E+00, 3.00000E+00, 6.00000E+00, 4.10000E+01, 1.40000E+01, 4.00000E+00, 1.30000E+01, 1.10000E+01, 4.00000E+00, 0.00000E+00, 4.00000E+00, 9.00000E+00, 7.00000E+00, 1.00000E+00, 9.00000E+00, 6.00000E+01, 3.40000E+01, 1.00000E+00, 1.40000E+01), .Dim=c(33, 4)), Cm_MC= structure(.Data= c(1.00000E+02, 3.60000E+01, 5.00000E+00, 1.70000E+01, 1.23000E+02, 5.10000E+01, 1.50000E+01, 3.80000E+01, 4.40000E+01, 8.00000E+00, 8.00000E+00, 1.50000E+01, 1.10000E+01, 1.00000E+00, 0.00000E+00, 0.00000E+00, 1.08000E+02, 1.30000E+01, 1.10000E+01, 5.30000E+01, 1.23000E+02, 5.20000E+01, 1.30000E+01, 1.10000E+01, 3.00000E+00, 3.00000E+00, 0.00000E+00, 1.00000E+00, 3.00000E+01, 1.30000E+01, 1.00000E+00, 1.00000E+00, 1.30000E+01, 8.00000E+00, 0.00000E+00, 0.00000E+00, 6.50000E+01, 1.20000E+01, 4.00000E+00, 0.00000E+00, 8.00000E+00, 1.00000E+01, 0.00000E+00, 1.00000E+00, 6.00000E+01, 4.10000E+01, 0.00000E+00, 2.00000E+00, 3.90000E+01, 2.60000E+01, 0.00000E+00, 3.00000E+00, 2.30000E+01, 9.00000E+00, 0.00000E+00, 1.00000E+00, 2.20000E+01, 8.00000E+00, 0.00000E+00, 0.00000E+00, 9.10000E+01, 4.20000E+01, 5.00000E+00, 1.10000E+01, 2.40000E+01, 2.60000E+01, 1.00000E+00, 2.00000E+00, 9.90000E+01, 5.00000E+01, 4.00000E+00, 5.00000E+00, 5.30000E+01, 3.90000E+01, 7.00000E+00, 8.00000E+00, 1.03000E+02, 5.80000E+01, 1.00000E+00, 2.40000E+01, 1.11000E+02, 1.00000E+02, 6.00000E+00, 2.80000E+01, 5.80000E+01, 2.20000E+01, 2.00000E+00, 5.00000E+00, 6.10000E+01, 4.10000E+01, 0.00000E+00, 1.90000E+01, 1.12000E+02, 5.60000E+01, 1.00000E+00, 9.00000E+00, 1.20000E+01, 1.50000E+01, 1.00000E+00, 3.00000E+00, 1.00000E+01, 3.00000E+00, 0.00000E+00, 7.00000E+00, 3.70000E+01, 2.40000E+01, 0.00000E+00, 2.00000E+00, 4.60000E+01, 3.50000E+01, 2.00000E+00, 2.00000E+01, 1.80000E+01, 4.00000E+00, 3.00000E+00, 6.00000E+00, 3.80000E+01, 1.30000E+01, 4.00000E+00, 1.30000E+01, 1.10000E+01, 4.00000E+00, 0.00000E+00, 4.00000E+00, 9.00000E+00, 7.00000E+00, 1.00000E+00, 9.00000E+00, 6.00000E+01, 3.40000E+01, 1.00000E+00, 1.40000E+01), .Dim=c(33, 4)))
This diff is collapsed.
> cat("=============================\n")
=============================
> cat("DIAGNOSTICS\n")
DIAGNOSTICS
> cat("=============================\n")
=============================
> if (nChains > 1) {
+ cat("Convergence: gelman-Rubin R test\n")
+ gelman.diag(fit.mcmc[,which(varnames(fit.mcmc)%in%parameterstotest)])
+ }
> cat("\n---------------------------\n")
---------------------------
> cat("Heidelberger and Welch's convergence diagnostic\n")
Heidelberger and Welch's convergence diagnostic
> cat("
+ heidel.diag is a run length control diagnostic based on a criterion of relative accuracy for the estimate of the mean. The default setting c ..." ... [TRUNCATED]
heidel.diag is a run length control diagnostic based on a criterion of relative accuracy for the estimate of the mean. The default setting corresponds to a relative accuracy of two significant digits.
heidel.diag also implements a convergence diagnostic, and removes up to half the chain in order to ensure that the means are estimated from a chain that has converged.
> heidel.diag(fit.mcmc[,which(varnames(fit.mcmc)%in%parameterstotest)], eps=0.1, pvalue=0.05)
Stationarity start p-value
test iteration
<<<<<<< HEAD
shape_lambda passed 1 0.391
rate_lambda passed 1 0.454
p_MC90_1SW passed 1 0.849
p_MC90_MSW passed 1 0.168
lambda0 passed 1 0.775
||||||| parent of 00840a9... Mise à jour estimation 2016
shape_lambda passed 10001 0.1734
rate_lambda passed 1 0.0553
p_MC90_1SW passed 1 0.1727
p_MC90_MSW passed 5001 0.1327
lambda0 passed 1 0.3314
=======
shape_lambda passed 5001 0.2675
rate_lambda passed 5001 0.2906
p_MC90_1SW passed 1 0.0672
p_MC90_MSW passed 1 0.8971
lambda0 passed 1 0.9597
>>>>>>> 00840a9... Mise à jour estimation 2016
Halfwidth Mean Halfwidth
test
<<<<<<< HEAD
shape_lambda passed 3.6712 0.24009
rate_lambda passed 0.0169 0.00127
p_MC90_1SW failed 0.1253 0.01344
p_MC90_MSW failed 0.3227 0.03972
lambda0 passed 228.7220 17.24491
||||||| parent of 00840a9... Mise à jour estimation 2016
shape_lambda passed 3.7439 0.05646
rate_lambda passed 0.0173 0.00022
p_MC90_1SW passed 0.1255 0.00270
p_MC90_MSW passed 0.3182 0.00779
lambda0 passed 219.2745 2.93325
=======
shape_lambda passed 3.7920 0.045345
rate_lambda passed 0.0173 0.000222
p_MC90_1SW passed 0.1240 0.002493
p_MC90_MSW passed 0.3090 0.007232
lambda0 passed 219.3195 2.719270
> cat("\n---------------------------\n")
>>>>>>> 00840a9... Mise à jour estimation 2016
---------------------------
> cat("Geweke's convergence diagnostic\n")
Geweke's convergence diagnostic
> cat("
+ Geweke (1992) proposed a convergence diagnostic for Markov chains based on a test for equality of the means of the first and last part of a ..." ... [TRUNCATED]
Geweke (1992) proposed a convergence diagnostic for Markov chains based on a test for equality of the means of the first and last part of a Markov chain (by default the first 10% and the last 50%).
If the samples are drawn from the stationary distribution of the chain, the two means are equal and Geweke's statistic has an asymptotically standard normal distribution.
The test statistic is a standard Z-score: the difference between the two sample means divided by its estimated standard error. The standard error is estimated from the spectral density at zero and so takes into account any autocorrelation.
......@@ -36,18 +91,59 @@ The test statistic is a standard Z-score: the difference between the two sample
The Z-score is calculated under the assumption that the two parts of the chain are asymptotically independent, which requires that the sum of frac1 and frac2 be strictly less than 1.
> geweke.diag(fit.mcmc[,which(varnames(fit.mcmc)%in%parameterstotest)], frac1 = 0.1, frac2 = 0.5)
Fraction in 1st window = 0.1
Fraction in 2nd window = 0.5
shape_lambda rate_lambda p_MC90_1SW p_MC90_MSW lambda0
<<<<<<< HEAD
1.490 1.692 -0.998 -0.606 -0.246
||||||| parent of 00840a9... Mise à jour estimation 2016
1.243 0.951 -0.650 -2.387 0.666
=======
1.23 1.16 -1.78 1.03 1.11
>>>>>>> 00840a9... Mise à jour estimation 2016
> cat("\n---------------------------\n")
---------------------------
> cat("Raftery and Lewis's diagnostic\n")
Raftery and Lewis's diagnostic
> raftery.diag(fit.mcmc[,which(varnames(fit.mcmc)%in%parameterstotest)], q=0.025, r=0.005, s=0.95, converge.eps=0.001)
Quantile (q) = 0.025
Accuracy (r) = +/- 0.005
Probability (s) = 0.95
<<<<<<< HEAD
||||||| parent of 00840a9... Mise à jour estimation 2016
Burn-in Total Lower bound Dependence
(M) (N) (Nmin) factor (I)
shape_lambda 24 32658 3746 8.72
rate_lambda 24 32490 3746 8.67
p_MC90_1SW 40 48128 3746 12.80
p_MC90_MSW 32 36960 3746 9.87
lambda0 18 26418 3746 7.05
=======
Burn-in Total Lower bound Dependence
(M) (N) (Nmin) factor (I)
shape_lambda 24 29526 3746 7.88
rate_lambda 20 21756 3746 5.81
p_MC90_1SW 40 49410 3746 13.20
p_MC90_MSW 32 39496 3746 10.50
lambda0 24 30240 3746 8.07
>>>>>>> 00840a9... Mise à jour estimation 2016
<<<<<<< HEAD
You need a sample size of at least 3746 with these values of q, r and s
||||||| parent of 00840a9... Mise à jour estimation 2016
=======
> # Stop writing to the file
> sink()
>>>>>>> 00840a9... Mise à jour estimation 2016
This diff is collapsed.
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment