[Distr-commits] r1234 - pkg/distrMod/tests/Examples

noreply at r-forge.r-project.org noreply at r-forge.r-project.org
Tue Jul 31 09:03:06 CEST 2018


Author: stamats
Date: 2018-07-31 09:03:05 +0200 (Tue, 31 Jul 2018)
New Revision: 1234

Modified:
   pkg/distrMod/tests/Examples/distrMod-Ex.Rout.save
Log:
update of Rout.save due to new aliases for MDEStimator

Modified: pkg/distrMod/tests/Examples/distrMod-Ex.Rout.save
===================================================================
--- pkg/distrMod/tests/Examples/distrMod-Ex.Rout.save	2018-07-30 22:21:00 UTC (rev 1233)
+++ pkg/distrMod/tests/Examples/distrMod-Ex.Rout.save	2018-07-31 07:03:05 UTC (rev 1234)
@@ -376,7 +376,7 @@
         dimnames = list(nms, nms0))
     list(fval = fval0, mat = mat0)
 }
-<bytecode: 0x88e90c0>
+<bytecode: 0x9e38338>
 Trafo / derivative matrix at which estimate was produced:
        scale shape
 shape  0.000     1
@@ -574,7 +574,7 @@
     ((x - 0)/c(scale = 1) * LogDeriv((x - 0)/c(scale = 1)) - 
         1)/c(scale = 1)
 }
-<environment: 0x99ee1b0>
+<environment: 0xaf2fcb8>
 
 > checkL2deriv(E1)
 precision of centering:	 -1.51181e-06 
@@ -750,8 +750,8 @@
 Slot "fct":
 function (x) 
 QuadFormNorm(x, A = A)
-<bytecode: 0x9bf8f68>
-<environment: 0x9bf8c58>
+<bytecode: 0xb13f8b0>
+<environment: 0xb13f5a0>
 
 > 
 > ## The function is currently defined as
@@ -1030,7 +1030,7 @@
     ((x - 0)/c(meanlog = 1) * LogDeriv((x - 0)/c(meanlog = 1)) - 
         1)/c(meanlog = 1)
 }
-<environment: 0x10c0ad98>
+<environment: 0x4396ab8>
 
 > checkL2deriv(L1)
 precision of centering:	 -0.003003394 
@@ -1138,18 +1138,6 @@
 +     return(res)
 + }
 > MCEstimator(x = x, ParamFamily = G, criterion = negLoglikelihood)
-Warning in fn(par, ...) :
-  Criterion evaluation at theta = 0.298,4.655 threw an error;
-returning starting par;
-
-Warning in fn(par, ...) :
-  Criterion evaluation at theta = 0.764,4.655 threw an error;
-returning starting par;
-
-Warning in fn(par, ...) :
-  Criterion evaluation at theta = 0.298,5.12 threw an error;
-returning starting par;
-
 Evaluations of Minimum criterion estimate:
 ------------------------------------------
 An object of class “Estimate” 
@@ -1157,28 +1145,16 @@
   MCEstimator(x = x, ParamFamily = G, criterion = negLoglikelihood)
 samplesize:   50
 estimate:
-    scale     shape 
-0.2983286 4.6547001 
+   scale    shape 
+0.342008 4.060286 
 Criterion:
-      
-1e+20 
+        
+47.9651 
 > 
 > ## Kolmogorov(-Smirnov) minimum distance estimator
 > ## Note: you can also use function MDEstimator!
 > MCEstimator(x = x, ParamFamily = G, criterion = KolmogorovDist, 
 +             crit.name = "Kolmogorov distance")
-Warning in fn(par, ...) :
-  Criterion evaluation at theta = 0.298,4.655 threw an error;
-returning starting par;
-
-Warning in fn(par, ...) :
-  Criterion evaluation at theta = 0.764,4.655 threw an error;
-returning starting par;
-
-Warning in fn(par, ...) :
-  Criterion evaluation at theta = 0.298,5.12 threw an error;
-returning starting par;
-
 Evaluations of Minimum Kolmogorov distance estimate:
 ----------------------------------------------------
 An object of class “Estimate” 
@@ -1188,10 +1164,10 @@
 samplesize:   50
 estimate:
     scale     shape 
-0.2983286 4.6547001 
+0.3398645 4.2654569 
 Criterion:
 Kolmogorov distance 
-              1e+20 
+         0.06350364 
 > 
 > ## Total variation minimum distance estimator
 > ## Note: you can also use function MDEstimator!
@@ -1251,7 +1227,8 @@
 > 
 > ### Name: MDEstimator
 > ### Title: Function to compute minimum distance estimates
-> ### Aliases: MDEstimator
+> ### Aliases: MDEstimator CvMMDEstimator KolmogorovMDEstimator
+> ###   TotalVarMDEstimator HellingerMDEstimator
 > ### Keywords: univar robust
 > 
 > ### ** Examples
@@ -1276,6 +1253,23 @@
 Criterion:
 Kolmogorov distance 
          0.06350364 
+> ## or
+> KolmogorovMDEstimator(x = x, ParamFamily = G)
+Evaluations of Minimum Kolmogorov distance estimate:
+----------------------------------------------------
+An object of class “Estimate” 
+generated by call
+  MDEstimator(x = x, ParamFamily = ParamFamily, distance = KolmogorovDist, 
+    paramDepDist = paramDepDist, startPar = startPar, Infos = Infos, 
+    trafo = trafo, penalty = penalty, validity.check = validity.check, 
+    asvar.fct = asvar.fct, na.rm = na.rm, .withEvalAsVar = .withEvalAsVar)
+samplesize:   50
+estimate:
+    scale     shape 
+0.3398645 4.2654569 
+Criterion:
+Kolmogorov distance 
+         0.06350364 
 > 
 > ## von Mises minimum distance estimator with default mu
 > MDEstimator(x = x, ParamFamily = G, distance = CvMDist)
@@ -1296,6 +1290,8 @@
 > ##D ## von Mises minimum distance estimator with default mu
 > ##D MDEstimator(x = x, ParamFamily = G, distance = CvMDist,
 > ##D             asvar.fct = .CvMMDCovariance)
+> ##D ## or
+> ##D CvMMDEstimator(x = x, ParamFamily = G)
 > ##D 
 > ##D ## von Mises minimum distance estimator with mu = N(0,1)
 > ##D MDEstimator(x = x, ParamFamily = G, distance = CvMDist, mu = Norm())
@@ -1303,6 +1299,8 @@
 > ##D ## Total variation minimum distance estimator
 > ##D ## gamma distributions are discretized
 > ##D MDEstimator(x = x, ParamFamily = G, distance = TotalVarDist)
+> ##D ## or
+> ##D TotalVarMDEstimator(x = x, ParamFamily = G)
 > ##D ## or smoothing of emprical distribution (takes some time!)
 > ##D #MDEstimator(x = x, ParamFamily = G, distance = TotalVarDist, asis.smooth.discretize = "smooth")
 > ##D 
@@ -1310,9 +1308,11 @@
 > ##D ## gamma distributions are discretized
 > ##D distroptions(DistrResolution = 1e-10)
 > ##D MDEstimator(x = x, ParamFamily = G, distance = HellingerDist, startPar = c(1,2))
+> ##D ## or
+> ##D HellingerMDEstimator(x = x, ParamFamily = G, startPar = c(1,2))
 > ##D distroptions(DistrResolution = 1e-6) # default
 > ##D ## or smoothing of emprical distribution (takes some time!)
-> ##D #MDEstimator(x = x, ParamFamily = G, distance = HellingerDist, asis.smooth.discretize = "smooth")
+> ##D MDEstimator(x = x, ParamFamily = G, distance = HellingerDist, asis.smooth.discretize = "smooth")
 > ## End(Not run)
 > 
 > 
@@ -2027,7 +2027,7 @@
         return(abs(x))
     else return(sqrt(colSums(x^2)))
 }
-<bytecode: 0x8073fc8>
+<bytecode: 0x12991d28>
 <environment: namespace:distrMod>
 > name(EuclNorm)
 [1] "EuclideanNorm"
@@ -2059,7 +2059,7 @@
         return(abs(x))
     else return(sqrt(colSums(x^2)))
 }
-<bytecode: 0x8073fc8>
+<bytecode: 0x12991d28>
 <environment: namespace:distrMod>
 
 > 
@@ -2518,8 +2518,8 @@
 Slot "fct":
 function (x) 
 QuadFormNorm(x, A = A0)
-<bytecode: 0xc7498c0>
-<environment: 0xc749540>
+<bytecode: 0xbf7c598>
+<environment: 0xbf7c988>
 
 > 
 > ## The function is currently defined as
@@ -2557,8 +2557,8 @@
 Slot "fct":
 function (x) 
 QuadFormNorm(x, A = A)
-<bytecode: 0xee72a50>
-<environment: 0xee72740>
+<bytecode: 0xbe53df0>
+<environment: 0xbe50340>
 
 > 
 > ## The function is currently defined as
@@ -3580,7 +3580,7 @@
     dimnames(mat) <- list(nfval, c("mean", "sd"))
     return(list(fval = fval, mat = mat))
 }
-<bytecode: 0x1061b6a0>
+<bytecode: 0xb1aa450>
 > print(param(NS), show.details = "minimal")
 An object of class "ParamWithScaleFamParameter"
 name:	location and scale
@@ -3629,7 +3629,7 @@
     dimnames(mat) <- list(nfval, c("mean", "sd"))
     return(list(fval = fval, mat = mat))
 }
-<bytecode: 0x1061b6a0>
+<bytecode: 0xb1aa450>
 Trafo / derivative matrix:
             mean         sd
 mu/sig 0.3668695 -0.3024814
@@ -3672,7 +3672,7 @@
     dimnames(mat) <- list(nfval, c("mean", "sd"))
     return(list(fval = fval, mat = mat))
 }
-<bytecode: 0x1061b6a0>
+<bytecode: 0xb1aa450>
 Trafo / derivative matrix:
          mean      sd
 mu/sig 0.3669 -0.3025
@@ -4050,7 +4050,7 @@
 > cleanEx()
 > options(digits = 7L)
 > base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
-Time elapsed:  26.144 0.168 26.377 0 0.004 
+Time elapsed:  31.872 0.196 32.183 0 0.008 
 > grDevices::dev.off()
 null device 
           1 



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