[EXTREME ESTIMATOR][GEV MARGIN FIT][BAYESIAN] add fevd.R to use fedv package...

[EXTREME ESTIMATOR][GEV MARGIN FIT][BAYESIAN] add fevd.R to use fedv package in the Bayesian mode. Also, fix fevd method, by creating my own method "myfevd" that avoid code crash when using mode use.phi=FALSE

extreme_estimator/margin_fits/gev/fevd.R → extreme_estimator/margin_fits/gev/main_fevd_fixed.R

@@ -5,73 +5,69 @@
 library(extRemes)
 library(stats4)
 library(SpatialExtremes)
-
+source('myfevd.R')
+# Sample from a GEV
 set.seed(42)
 N <- 1000
 loc = 0; scale = 1; shape <- 0.1
 x_gev <- rgev(N, loc = loc, scale = scale, shape = shape)
-start_loc = 0; start_scale = 1; start_shape = 1
-# res = fevd(x_gev, method='GMLE')
-
-fevdPriorMy <- function (theta, q, p, log = FALSE){
-    x = theta["shape"] + 0.5
-
-    print(theta)
-    print(theta["location"])
-    print(dunif(theta["location"]))
-    print(theta[0])
-    dfun <- function(th) dbeta(th[1], shape1 = th[2], shape2 = th[3],
-        log = log)
-    th <- cbind(theta, q, p)
-    res <- apply(th, 1, dfun)
-    return(prod(res))
-}
+
+# fevdPriorMy <- function (theta, q, p, log = FALSE){
+#     x = theta["shape"] + 0.5
+#
+#     print(theta)
+#     print(theta["location"])
+#     print(dunif(theta["location"]))
+#     print(theta[0])
+#     dfun <- function(th) dbeta(th[1], shape1 = th[2], shape2 = th[3],
+#         log = log)
+#     th <- cbind(theta, q, p)
+#     res <- apply(th, 1, dfun)
+#     return(prod(res))
+# }
 
 fevdPriorMyMy <- function (theta, q, p, log = FALSE){
-    print(theta)
-    print(q)
-    print(p)
+    # print(theta)
+    # print(q)
+    # print(p)
     x = theta[length(theta)]
     # + 0.5 enables to shift the Beta law in the interval [-0.5, 0.5]
     res = dbeta(x + 0.5, q, p, log = TRUE)
     return(res)
 }
 
-
 print(pbeta(1.0, 1, 1))
 print(pbeta(0.5, 1, 1))
-print(fevdPriorMy(2.0, 0.0, 0.0))
+print(fevdPriorMyMy(2.0, 0.0, 0.0))
 
-res = fevd(x_gev, method='Bayesian', priorFun="fevdPriorMyMy", priorParams=list(q=c(6), p=c(9)), iter=5000)
+
+
+
+# res = fevd(x_gev, method='Bayesian', priorFun="fevdPriorMyMy", priorParams=list(q=c(6), p=c(9)), iter=5000, verbose=TRUE, use.phi=FALSE)
+res = fevd_fixed(x_gev, method='Bayesian', priorFun="fevdPriorMyMy", priorParams=list(q=c(6), p=c(9)), iter=5000, verbose=TRUE, use.phi=FALSE)
+# res = fevd(x_gev, method='GMLE', iter=5000, verbose=TRUE, use.phi=FALSE)
 print(res)
-# res = fevd(x_gev, method='Bayesian')
-# print(res)
 
-priorFun="shapePriorBeta"
-shapePriorBeta
-#
-#
-# print(shapePriorBeta(0.0, 6, 9))
-# priorParams=list(q=c(1, 1, 6), p=c(1, 1, 9))
-# p.i <- do.call(priorFun, c(list(1.0), priorParams))
-# print(p.i)
-
-# priorFun <- "shapePriorBeta"
-# priorParams <- list(q = 6, p = 9)
-# priorFun <- "fevdPriorDefault"
-# priorParams <- list(q = 6, p = 9)
-# e = do.call(priorFun, c(list(0.0), priorParams))
-# print(e)
-#
-# print(res$method)
-# print(res$priorFun)
-# print(res$priorParams)
-# m = res$results
-# print(m[2,1])
+
+print(res$method)
+print(res$priorFun)
+print(res$priorParams)
+m = res$results
+print(dim(m))
+print(m)
+print(m[1,])
+print(m[1,1])
+
+
+
+
 # print(class(res$chain.info))
-# print(res$chain.info[[1]])
+# ch = res$chain.info
+# print(dim(ch))
+# print(ch)
+
 # # summary(res)
-# print(attributes(res))
+print(attributes(res))
 # print('here')
 # print(attr(res, 'chain.info'))
 # print(attr(res, "method"))
@@ -81,19 +77,7 @@ shapePriorBeta
 # print(res.method)
 
 
-# p.i <- do.call(shapePriorBeta, c(list(theta =  c(-0.12572432087762, -0.0567634605386987, 0.133782230298093)), priorParams=list(q = 6, p = 9)))
-# print(p.i)
-# a = fevd(x_gev, method='Bayesian', priorFun="shapePriorBeta", priorParams=list(q = 6, p = 9))
-
-# priorParams=list(v=c(0.1, 10, 0.1)),
-#     initial=list(location=0, scale=0.1, shape=-0.5)),
-
-# print(a)
-#
-# # S3 method for fevd.bayesian
-# summary(a, FUN = "mean", burn.in = 499)
 
-# print(a.results)
 
 # Bayesian method is using a normal distribution functions for the shape parameter
 # GMLE distribution is using a Beta distribution for the shape parameter